diff --git a/.github/workflows/update-models.yml b/.github/workflows/update-models.yml
new file mode 100644
index 00000000000..2915c2c90f3
--- /dev/null
+++ b/.github/workflows/update-models.yml
@@ -0,0 +1,103 @@
+# Weekly refresh of the vendored LiteLLM model catalog
+# (src/common/utils/tokens/models.json) via scripts/update_models.ts, which
+# validates pricing shape and curated-model coverage before writing (#3727).
+# Opens or updates a bot PR only when the data changed.
+# SETUP: reuses the auto-cleanup GitHub App credentials (XUM_APP_ID and
+# XUM_APP_PRIVATE_KEY) so the PR triggers CI.
+
+name: Update Models
+
+on:
+  schedule:
+    - cron: "0 6 * * 1" # Weekly, Mondays 6 AM UTC
+  workflow_dispatch: {}
+
+permissions:
+  contents: write
+  pull-requests: write
+
+concurrency:
+  group: update-models
+  cancel-in-progress: true
+
+jobs:
+  update-models:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check required secrets
+        id: precheck
+        env:
+          XUM_APP_ID: ${{ secrets.XUM_APP_ID }}
+          XUM_APP_PRIVATE_KEY: ${{ secrets.XUM_APP_PRIVATE_KEY }}
+        run: |
+          if [ -z "$XUM_APP_ID" ] || [ -z "$XUM_APP_PRIVATE_KEY" ]; then
+            echo "Skipping (missing XUM_APP_ID / XUM_APP_PRIVATE_KEY)."
+            echo "enabled=false" >> "$GITHUB_OUTPUT"
+          else
+            echo "enabled=true" >> "$GITHUB_OUTPUT"
+          fi
+
+      - uses: actions/create-github-app-token@d72941d797fd3113feb6b93fd0dec494b13a2547 # v1.12.0
+        id: app-token
+        if: ${{ steps.precheck.outputs.enabled == 'true' }}
+        with:
+          # Use a GitHub App token so PR events trigger CI. PRs opened via
+          # GITHUB_TOKEN intentionally do not trigger other workflows.
+          app-id: ${{ secrets.XUM_APP_ID }}
+          private-key: ${{ secrets.XUM_APP_PRIVATE_KEY }}
+
+      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
+        if: ${{ steps.precheck.outputs.enabled == 'true' }}
+        with:
+          # Pin to main so a workflow_dispatch from a feature branch cannot leak
+          # unrelated commits into the shared bot/update-models branch.
+          ref: refs/heads/main
+          token: ${{ steps.app-token.outputs.token }}
+
+      - uses: oven-sh/setup-bun@b7a1c7ccf290d58743029c4f6903da283811b979 # v2.1.0
+        if: ${{ steps.precheck.outputs.enabled == 'true' }}
+        with:
+          bun-version: 1.3.5
+
+      - name: Update models.json
+        if: ${{ steps.precheck.outputs.enabled == 'true' }}
+        # Go through the Makefile target (which installs dependencies first) so
+        # automation and local refreshes share one entry point.
+        run: make update-models
+
+      - name: Push branch and open PR if changed
+        if: ${{ steps.precheck.outputs.enabled == 'true' }}
+        env:
+          GH_TOKEN: ${{ steps.app-token.outputs.token }}
+        run: |
+          if git diff --quiet -- src/common/utils/tokens/models.json; then
+            echo "models.json already up to date; no PR needed."
+            exit 0
+          fi
+
+          git config user.name "mux-bot[bot]"
+          git config user.email "264182336+mux-bot[bot]@users.noreply.github.com"
+          git checkout -B bot/update-models
+          git add src/common/utils/tokens/models.json
+          # Attribution footer per .xum/skills/pull-requests: automation
+          # commits use the workflow marker instead of model/thinking/cost.
+          footer="---
+
+          _Automated by the \`update-models\` workflow (GitHub Actions)._
+
+          <!-- xum-attribution: workflow=update-models -->"
+          git commit -m "🤖 chore: refresh models.json from LiteLLM" -m "$footer"
+          git push --force origin bot/update-models
+
+          open_prs=$(gh pr list --head bot/update-models --state open --json number --jq length)
+          if [ "$open_prs" = "0" ]; then
+            gh pr create \
+              --title "🤖 chore: refresh models.json from LiteLLM" \
+              --body "Automated weekly refresh of the vendored LiteLLM model catalog via \`make update-models\` (#3727).
+
+          $footer" \
+              --base main \
+              --head bot/update-models
+          else
+            echo "Existing open PR for bot/update-models updated by push."
+          fi
diff --git a/.xum/skills/pull-requests/SKILL.md b/.xum/skills/pull-requests/SKILL.md
index b16d89fb047..83f53a3b269 100644
--- a/.xum/skills/pull-requests/SKILL.md
+++ b/.xum/skills/pull-requests/SKILL.md
@@ -19,6 +19,16 @@ _Generated with `xum` • Model: `<modelString>` • Thinking: `<thinkingLevel>`
 
 Always check `$XUM_MODEL_STRING`, `$XUM_THINKING_LEVEL`, and `$XUM_COSTS_USD` via bash before creating or updating PRs—include them in the footer if set.
 
+Scheduled automation (non-AI) commits and PRs from GitHub Actions workflows use this footer instead, since model/thinking/cost do not apply:
+
+```md
+---
+
+_Automated by the `<workflow-name>` workflow (GitHub Actions)._
+
+<!-- xum-attribution: workflow=<workflow-name> -->
+```
+
 ## Lifecycle Rules
 
 - Before submitting a PR, ensure the branch name reflects the work and the base branch is correct.
diff --git a/Makefile b/Makefile
index 69621d46cca..dc91b1b474d 100644
--- a/Makefile
+++ b/Makefile
@@ -219,6 +219,20 @@ start: node_modules/.installed build-main build-preload build-static ## Build an
 ## Build targets (can run in parallel)
 build: node_modules/.installed src/version.ts build-renderer build-main build-preload build-icons build-static ## Build all targets
 
+.PHONY: update-models
+update-models: node_modules/.installed ## Fetch latest LiteLLM model data, validate it, update models.json if changed
+	@bun scripts/update_models.ts
+
+# #3727: `make build UPDATE_MODELS=1` refreshes the vendored models.json before
+# building; plain `make build` stays network-free and reproducible. The refresh
+# must be a prerequisite of every catalog-consuming bundle (not just `build`) so
+# parallel make cannot bundle the old catalog, and the phony prerequisite forces
+# those bundles stale because models.json is not part of $(TS_SOURCES).
+ifeq ($(UPDATE_MODELS),1)
+build: update-models
+build-renderer dist/cli/index.js dist/cli/api.mjs: update-models
+endif
+
 build-main: node_modules/.installed dist/cli/index.js dist/cli/api.mjs ## Build main process
 
 BUILTIN_AGENTS_GENERATED := src/node/services/agentDefinitions/builtInAgentContent.generated.ts
@@ -235,7 +249,9 @@ $(BUILTIN_SKILLS_GENERATED): $(BUILTIN_SKILL_SOURCES) $(DOCS_SOURCES) scripts/ge
 $(WORKFLOW_RUNTIME_SOURCES_GENERATED): $(WORKFLOW_RUNTIME_SOURCES) scripts/gen_workflow_runtime_sources.ts
 	@bun scripts/gen_workflow_runtime_sources.ts
 
-dist/cli/index.js: src/cli/index.ts src/desktop/main.ts src/cli/server.ts src/version.ts tsconfig.main.json tsconfig.json $(TS_SOURCES) $(BUILTIN_AGENTS_GENERATED) $(BUILTIN_SKILLS_GENERATED) $(BUILTIN_WORKFLOWS_GENERATED) $(WORKFLOW_RUNTIME_SOURCES_GENERATED)
+# models.json is bundled but not in $(TS_SOURCES); without this prerequisite a
+# catalog-only refresh would leave a stale main bundle (#3727).
+dist/cli/index.js: src/cli/index.ts src/desktop/main.ts src/cli/server.ts src/version.ts tsconfig.main.json tsconfig.json $(TS_SOURCES) src/common/utils/tokens/models.json $(BUILTIN_AGENTS_GENERATED) $(BUILTIN_SKILLS_GENERATED) $(BUILTIN_WORKFLOWS_GENERATED) $(WORKFLOW_RUNTIME_SOURCES_GENERATED)
 	@echo "Building main process..."
 	@NODE_ENV=production $(TSGO) -p tsconfig.main.json
 	@NODE_ENV=production bun x tsc-alias -p tsconfig.main.json
diff --git a/scripts/update_models.ts b/scripts/update_models.ts
index dcadc4928ee..5299b3c7a66 100644
--- a/scripts/update_models.ts
+++ b/scripts/update_models.ts
@@ -1,62 +1,25 @@
 #!/usr/bin/env bun
 
 /**
- * Downloads the latest model prices and context window data from LiteLLM
- * and saves the subset Xum consumes to src/common/utils/tokens/models.json.
+ * Refreshes src/common/utils/tokens/models.json from LiteLLM. The transform and
+ * validation logic lives in src/common/utils/tokens/updateModelsData.ts so unit
+ * tests cover it. Writes only when the validated content changed, keeping
+ * `make update-models`, `make build UPDATE_MODELS=1`, and the scheduled
+ * update-models workflow idempotent (#3727).
  */
 
+import {
+  pruneModelData,
+  sanitizePricing,
+  serializeModelData,
+  validateModelData,
+  type ModelCatalogData,
+} from "../src/common/utils/tokens/updateModelsData";
+
 const LITELLM_URL =
   "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json";
 const OUTPUT_PATH = "src/common/utils/tokens/models.json";
 
-const RETAINED_FIELDS = [
-  "max_input_tokens",
-  "max_output_tokens",
-  "input_cost_per_token",
-  "output_cost_per_token",
-  "output_cost_per_image_token",
-  "input_cost_per_token_above_200k_tokens",
-  "output_cost_per_token_above_200k_tokens",
-  "cache_creation_input_token_cost",
-  "cache_creation_input_token_cost_above_200k_tokens",
-  "cache_read_input_token_cost",
-  "cache_read_input_token_cost_above_200k_tokens",
-  "tiered_pricing_threshold_tokens",
-  "mode",
-  "litellm_provider",
-  "supports_pdf_input",
-  "supports_vision",
-  "supports_audio_input",
-  "supports_video_input",
-  "max_pdf_size_mb",
-] as const;
-
-function pruneModelData(data: unknown): Record<string, Record<string, unknown>> {
-  if (!data || typeof data !== "object" || Array.isArray(data)) {
-    throw new Error("Expected LiteLLM model metadata object");
-  }
-
-  const pruned: Record<string, Record<string, unknown>> = {};
-  for (const [modelId, rawMetadata] of Object.entries(data)) {
-    if (!rawMetadata || typeof rawMetadata !== "object" || Array.isArray(rawMetadata)) {
-      continue;
-    }
-
-    const metadata = rawMetadata as Record<string, unknown>;
-    const retained: Record<string, unknown> = {};
-    // Keep models.json small: Xum only reads pricing, token limits, provider, mode, and media
-    // capability fields, while upstream LiteLLM ships many provider-specific fields we never use.
-    for (const field of RETAINED_FIELDS) {
-      if (metadata[field] !== undefined) {
-        retained[field] = metadata[field];
-      }
-    }
-    pruned[modelId] = retained;
-  }
-
-  return pruned;
-}
-
 async function updateModels() {
   console.log(`Fetching model data from ${LITELLM_URL}...`);
 
@@ -66,12 +29,37 @@ async function updateModels() {
     throw new Error(`Failed to fetch model data: ${response.status} ${response.statusText}`);
   }
 
-  const data = pruneModelData(await response.json());
+  const sanitized = sanitizePricing(pruneModelData(await response.json()));
+  if (sanitized.droppedModelIds.length > 0) {
+    console.warn(
+      `Dropped ${sanitized.droppedModelIds.length} entries with invalid pricing: ` +
+        sanitized.droppedModelIds.slice(0, 10).join(", ")
+    );
+  }
+
+  const existing = await Bun.file(OUTPUT_PATH)
+    .text()
+    .catch(() => null);
+  // Validate against the vendored catalog so a truncated upstream response, a
+  // field rename, or a targeted repricing cannot silently degrade known models.
+  let baseline: ModelCatalogData | undefined;
+  if (existing !== null) {
+    try {
+      baseline = JSON.parse(existing) as ModelCatalogData;
+    } catch {
+      console.warn(`Could not parse existing ${OUTPUT_PATH}; skipping baseline checks`);
+    }
+  }
+  validateModelData(sanitized, baseline);
 
-  console.log(`Writing model data to ${OUTPUT_PATH}...`);
-  await Bun.write(OUTPUT_PATH, `${JSON.stringify(data, null, 2)}\n`);
+  const serialized = serializeModelData(sanitized.catalog);
+  if (existing === serialized) {
+    console.log("✓ models.json already up to date");
+    return;
+  }
 
-  console.log("✓ Model data updated successfully");
+  await Bun.write(OUTPUT_PATH, serialized);
+  console.log(`✓ Updated ${OUTPUT_PATH} (${Object.keys(sanitized.catalog).length} models)`);
 }
 
 updateModels().catch((error) => {
diff --git a/src/browser/features/Settings/Components/SearchableModelSelect.tsx b/src/browser/features/Settings/Components/SearchableModelSelect.tsx
index b95851ed561..d285e60355e 100644
--- a/src/browser/features/Settings/Components/SearchableModelSelect.tsx
+++ b/src/browser/features/Settings/Components/SearchableModelSelect.tsx
@@ -6,6 +6,10 @@ import { stopKeyboardPropagation } from "@/browser/utils/events";
 import { cn } from "@/common/lib/utils";
 import { getModelName, getModelProvider } from "@/common/utils/ai/models";
 
+// The full model catalog is ~2k entries; rendering every row makes the popover
+// janky, so cap the list and prompt the user to narrow the search instead.
+const MAX_RENDERED_MODELS = 200;
+
 /** Searchable model dropdown with keyboard navigation */
 export function SearchableModelSelect(props: {
   value: string;
@@ -34,6 +38,9 @@ export function SearchableModelSelect(props: {
       model.toLowerCase().includes(searchLower) ||
       (getModelName(model)?.toLowerCase().includes(searchLower) ?? false)
   );
+  const hiddenModelCount = Math.max(0, filteredModels.length - MAX_RENDERED_MODELS);
+  const visibleModels =
+    hiddenModelCount > 0 ? filteredModels.slice(0, MAX_RENDERED_MODELS) : filteredModels;
 
   // Build list of all selectable items (empty option + filtered models)
   const items: Array<{ value: string; label: string; provider?: string; isMuted?: boolean }> = [];
@@ -44,7 +51,7 @@ export function SearchableModelSelect(props: {
       isMuted: true,
     });
   }
-  for (const model of filteredModels) {
+  for (const model of visibleModels) {
     items.push({
       value: model,
       label: getModelName(model) ?? model,
@@ -180,6 +187,11 @@ export function SearchableModelSelect(props: {
               </button>
             ))
           )}
+          {hiddenModelCount > 0 && (
+            <div className="text-muted px-2 py-1 text-center text-[10px]">
+              +{hiddenModelCount} more, keep typing to filter
+            </div>
+          )}
         </div>
       </PopoverContent>
     </Popover>
diff --git a/src/browser/features/Settings/Sections/ModelsSection.tsx b/src/browser/features/Settings/Sections/ModelsSection.tsx
index 39a429daf70..3d775eed592 100644
--- a/src/browser/features/Settings/Sections/ModelsSection.tsx
+++ b/src/browser/features/Settings/Sections/ModelsSection.tsx
@@ -19,6 +19,7 @@ import { useMinThinkingLevels } from "@/browser/hooks/useMinThinkingLevels";
 import { usePersistedState } from "@/browser/hooks/usePersistedState";
 import { useProvidersConfig } from "@/browser/hooks/useProvidersConfig";
 import { KNOWN_MODELS } from "@/common/constants/knownModels";
+import { listModelCatalogIds } from "@/common/utils/tokens/modelCatalog";
 import { isCodexOauthRequiredModelId } from "@/common/constants/codexOAuth";
 import { usePolicy } from "@/browser/contexts/PolicyContext";
 import {
@@ -160,12 +161,10 @@ export function ModelsSection() {
   // cross-hook timing mismatches while settings are loading/refetching.
   const codexOauthConfigured = config?.openai?.codexOauthSet === true;
 
-  // "Treat as" dropdown should only list known models — custom models don't have
-  // the metadata (pricing, context window, tokenizer) that mapping inherits.
-  // Static list — React Compiler handles memoization; no manual useMemo needed.
-  const knownModelIds = Object.values(KNOWN_MODELS)
-    .map((model) => model.id)
-    .sort();
+  // "Treat as" targets must carry the metadata (pricing, context window) that
+  // mapping inherits: any model in the token catalog qualifies, not just the
+  // curated KNOWN_MODELS list (#3727).
+  const treatAsModelIds = listModelCatalogIds();
 
   // Check if a model already exists (for duplicate prevention)
   const modelExists = useCallback(
@@ -473,7 +472,7 @@ export function ModelsSection() {
                       editMappedToModel={isModelEditing ? editing.mappedToModel : undefined}
                       editAutofocus={isModelEditing ? editing.focus : undefined}
                       customContextWindowTokens={model.contextWindowTokens}
-                      allModels={knownModelIds}
+                      allModels={treatAsModelIds}
                       editError={isModelEditing ? error : undefined}
                       saving={false}
                       hasActiveEdit={editing !== null}
diff --git a/src/common/constants/knownModels.test.ts b/src/common/constants/knownModels.test.ts
index c83b5ba4601..0b4e15c0b89 100644
--- a/src/common/constants/knownModels.test.ts
+++ b/src/common/constants/knownModels.test.ts
@@ -5,29 +5,16 @@
 import { describe, test, expect } from "@jest/globals";
 import { KNOWN_MODELS, MODEL_ABBREVIATIONS } from "@/common/constants/knownModels";
 import modelsJson from "@/common/utils/tokens/models.json";
-import { modelsExtra } from "@/common/utils/tokens/models-extra";
+import { findMissingKnownModels } from "@/common/utils/tokens/updateModelsData";
 
 describe("Known Models Integration", () => {
   test("all known models exist in token metadata", () => {
-    const missingModels: string[] = [];
-
-    for (const [key, model] of Object.entries(KNOWN_MODELS)) {
-      const modelId = model.providerModelId;
-
-      // xAI and Moonshot models are provider-prefixed in token metadata.
-      const lookupKey =
-        model.provider === "xai" || model.provider === "moonshotai"
-          ? `${model.provider}/${modelId}`
-          : modelId;
-      if (!(lookupKey in modelsJson) && !(lookupKey in modelsExtra) && !(modelId in modelsExtra)) {
-        missingModels.push(`${key}: ${model.provider}:${modelId}`);
-      }
-    }
+    const missingModels = findMissingKnownModels(modelsJson);
 
     if (missingModels.length > 0) {
       throw new Error(
         `The following known models are missing from token metadata:\n${missingModels.join("\n")}\n\n` +
-          `Run 'bun scripts/update_models.ts' to refresh models.json from LiteLLM.`
+          `Run 'make update-models' to refresh models.json from LiteLLM.`
       );
     }
   });
diff --git a/src/common/utils/ai/modelCapabilities.test.ts b/src/common/utils/ai/modelCapabilities.test.ts
index d95811ab728..f13e6594291 100644
--- a/src/common/utils/ai/modelCapabilities.test.ts
+++ b/src/common/utils/ai/modelCapabilities.test.ts
@@ -1,5 +1,9 @@
 import { describe, expect, it } from "bun:test";
-import { getModelCapabilities, getSupportedInputMediaTypes } from "./modelCapabilities";
+import {
+  extractModelCapabilities,
+  getModelCapabilities,
+  getSupportedInputMediaTypes,
+} from "./modelCapabilities";
 
 describe("getModelCapabilities", () => {
   it("returns capabilities for known models", () => {
@@ -49,11 +53,12 @@ describe("getModelCapabilities", () => {
     expect(caps?.supportsVision).toBe(true);
   });
 
-  it("returns maxPdfSizeMb when present in model metadata", () => {
-    const caps = getModelCapabilities("google:gemini-1.5-flash");
-    expect(caps).not.toBeNull();
-    expect(caps?.supportsPdfInput).toBe(true);
-    expect(caps?.maxPdfSizeMb).toBeGreaterThan(0);
+  it("returns maxPdfSizeMb and infers PDF support when metadata carries the field", () => {
+    // Injected metadata: upstream LiteLLM dropped max_pdf_size_mb, but
+    // models-extra overrides can still supply it.
+    const caps = extractModelCapabilities({ max_pdf_size_mb: 30 });
+    expect(caps.supportsPdfInput).toBe(true);
+    expect(caps.maxPdfSizeMb).toBe(30);
   });
 
   it("returns multimodal capabilities for Gemini 3.5 Flash", () => {
diff --git a/src/common/utils/ai/modelCapabilities.ts b/src/common/utils/ai/modelCapabilities.ts
index 9fde6200ee4..21369770bc4 100644
--- a/src/common/utils/ai/modelCapabilities.ts
+++ b/src/common/utils/ai/modelCapabilities.ts
@@ -2,6 +2,7 @@ import type { ProvidersConfigMap } from "@/common/orpc/types";
 import { resolveModelForMetadata } from "@/common/utils/providers/modelEntries";
 import modelsData from "../tokens/models.json";
 import { modelsExtra } from "../tokens/models-extra";
+import { generateModelLookupKeys } from "../tokens/modelStats";
 import { normalizeToCanonical } from "./models";
 
 interface RawModelCapabilitiesData {
@@ -24,45 +25,9 @@ export interface ModelCapabilities {
 
 export type SupportedInputMediaType = "image" | "pdf" | "audio" | "video";
 
-const PROVIDER_KEY_ALIASES: Record<string, string> = {
-  // GitHub Copilot keys in models.json use underscores for LiteLLM provider names.
-  "github-copilot": "github_copilot",
-};
-
-/**
- * Generates lookup keys for a model string with multiple naming patterns.
- *
- * Keep this aligned with getModelStats(): many providers/layers use slightly different
- * conventions (e.g. "ollama/model-cloud", "provider/model").
- */
-function generateLookupKeys(modelString: string): string[] {
-  const colonIndex = modelString.indexOf(":");
-  const provider = colonIndex !== -1 ? modelString.slice(0, colonIndex) : "";
-  const modelName = colonIndex !== -1 ? modelString.slice(colonIndex + 1) : modelString;
-  const litellmProvider = PROVIDER_KEY_ALIASES[provider] ?? provider;
-
-  const keys: string[] = [
-    modelName, // Direct model name (e.g., "claude-opus-4-5")
-  ];
-
-  if (provider) {
-    keys.push(
-      `${litellmProvider}/${modelName}`, // "ollama/gpt-oss:20b"
-      `${litellmProvider}/${modelName}-cloud` // "ollama/gpt-oss:20b-cloud" (LiteLLM convention)
-    );
-
-    // Fallback: strip size suffix for base model lookup
-    // "ollama:gpt-oss:20b" → "ollama/gpt-oss"
-    if (modelName.includes(":")) {
-      const baseModel = modelName.split(":")[0];
-      keys.push(`${litellmProvider}/${baseModel}`);
-    }
-  }
-
-  return keys;
-}
-
-function extractModelCapabilities(data: RawModelCapabilitiesData): ModelCapabilities {
+// Exported for tests: upstream LiteLLM no longer ships max_pdf_size_mb, so the
+// inference branches can only be exercised with injected metadata.
+export function extractModelCapabilities(data: RawModelCapabilitiesData): ModelCapabilities {
   const maxPdfSizeMb = typeof data.max_pdf_size_mb === "number" ? data.max_pdf_size_mb : undefined;
   const provider = typeof data.litellm_provider === "string" ? data.litellm_provider : undefined;
 
@@ -85,7 +50,9 @@ function extractModelCapabilities(data: RawModelCapabilitiesData): ModelCapabili
 
 export function getModelCapabilities(modelString: string): ModelCapabilities | null {
   const normalized = normalizeToCanonical(modelString);
-  const lookupKeys = generateLookupKeys(normalized);
+  // Shared with getModelStats so capabilities and stats resolve from the same
+  // catalog entry (provider-scoped keys win over bare-name entries).
+  const lookupKeys = generateModelLookupKeys(normalized);
 
   // eslint-disable-next-line local/no-chained-type-assertions -- grandfathered when the rule was introduced; fix the underlying type instead of copying this pattern
   const modelsExtraRecord = modelsExtra as unknown as Record<string, RawModelCapabilitiesData>;
diff --git a/src/common/utils/tokens/modelCatalog.test.ts b/src/common/utils/tokens/modelCatalog.test.ts
new file mode 100644
index 00000000000..a6513d437f7
--- /dev/null
+++ b/src/common/utils/tokens/modelCatalog.test.ts
@@ -0,0 +1,61 @@
+import { describe, test, expect } from "@jest/globals";
+import { KNOWN_MODELS } from "@/common/constants/knownModels";
+import modelsJson from "./models.json";
+import { normalizeToCanonical } from "@/common/utils/ai/models";
+import { listModelCatalogIds } from "./modelCatalog";
+import { getModelStats } from "./modelStats";
+
+describe("listModelCatalogIds", () => {
+  const ids = listModelCatalogIds();
+
+  test("includes every curated known model", () => {
+    for (const model of Object.values(KNOWN_MODELS)) {
+      expect(ids).toContain(model.id);
+    }
+  });
+
+  test("exposes the full catalog, far beyond the curated list", () => {
+    expect(ids.length).toBeGreaterThan(Object.keys(KNOWN_MODELS).length * 10);
+    // Bare LiteLLM key: provider derived from litellm_provider.
+    expect(ids).toContain("openai:gpt-4o-mini");
+    // Provider-prefixed LiteLLM key: provider derived from the key itself.
+    expect(ids).toContain("gemini:gemini-2.5-pro");
+  });
+
+  test("every id is canonical provider:model and resolves to model stats", () => {
+    const invalid = ids.filter((id) => {
+      const colonIndex = id.indexOf(":");
+      return colonIndex <= 0 || colonIndex >= id.length - 1;
+    });
+    expect(invalid).toEqual([]);
+
+    const unresolved = ids.filter((id) => getModelStats(id) === null);
+    expect(unresolved).toEqual([]);
+  });
+
+  test("excludes models without chat-style metadata", () => {
+    expect(ids).not.toContain("openai:text-embedding-3-small");
+  });
+
+  test("excludes provider-scoped duplicates whose stats resolve to another entry", () => {
+    // snowflake/claude-sonnet-4-6 exists upstream, but stats resolution prefers
+    // the bare models-extra override, so exposing the scoped row would inherit
+    // metadata from a different entry than the row represents.
+    expect(Object.keys(modelsJson)).toContain("snowflake/claude-sonnet-4-6");
+    expect(ids).not.toContain("snowflake:claude-sonnet-4-6");
+  });
+
+  test("every id is self-canonical so selection metadata matches the visible row", () => {
+    // Gateway keys like openrouter/deepseek/x normalize to the direct provider id
+    // during stats resolution; exposing non-canonical ids would create rows that
+    // look distinct but inherit another entry's metadata.
+    const nonCanonical = ids.filter((id) => normalizeToCanonical(id) !== id);
+    expect(nonCanonical).toEqual([]);
+  });
+
+  test("is sorted, deduplicated, and stable across calls", () => {
+    expect(new Set(ids).size).toBe(ids.length);
+    expect([...ids].sort()).toEqual(ids);
+    expect(listModelCatalogIds()).toBe(ids);
+  });
+});
diff --git a/src/common/utils/tokens/modelCatalog.ts b/src/common/utils/tokens/modelCatalog.ts
new file mode 100644
index 00000000000..70b95ad701d
--- /dev/null
+++ b/src/common/utils/tokens/modelCatalog.ts
@@ -0,0 +1,56 @@
+/**
+ * Full catalog of model ids usable as custom-model metadata mapping targets
+ * ("Treat as" in Settings). Exposes every models.json entry with usable
+ * chat metadata instead of only the curated KNOWN_MODELS list (#3727).
+ */
+
+import { KNOWN_MODELS } from "@/common/constants/knownModels";
+import { normalizeToCanonical } from "@/common/utils/ai/models";
+import modelsData from "./models.json";
+import { resolveRawModelEntry } from "./modelStats";
+import { MAPPABLE_MODES, toCanonicalModelId } from "./updateModelsData";
+
+let cachedIds: string[] | undefined;
+
+/**
+ * Canonical `provider:model` ids for every catalog entry with resolvable model
+ * stats, unioned with the curated KNOWN_MODELS ids. Sorted. Cached because
+ * models.json is static for the process lifetime.
+ */
+export function listModelCatalogIds(): string[] {
+  if (cachedIds === undefined) {
+    const ids = new Set<string>();
+    for (const model of Object.values(KNOWN_MODELS)) {
+      ids.add(model.id);
+    }
+    for (const [key, metadata] of Object.entries(
+      modelsData as Record<string, Record<string, unknown>>
+    )) {
+      if (typeof metadata.mode !== "string" || !MAPPABLE_MODES.has(metadata.mode)) {
+        continue;
+      }
+      const id = toCanonicalModelId(key, metadata);
+      if (id === null) {
+        continue;
+      }
+      // Stats resolution normalizes gateway ids (openrouter:deepseek/x resolves as
+      // deepseek:x), so expose the normalized identity and let the Set collapse
+      // gateway duplicates; otherwise a gateway row would silently inherit the
+      // direct entry's metadata while looking like a distinct option.
+      const normalized = normalizeToCanonical(id);
+      // Only expose rows whose stats resolve from this very entry (same validity
+      // bar as getModelStats). A provider-scoped duplicate like
+      // snowflake/claude-sonnet-4-6 loses stats resolution to the bare
+      // models-extra override, so selecting it would inherit metadata from a
+      // different entry than the row represents; the id stays selectable through
+      // whichever entry actually backs it.
+      const resolved = resolveRawModelEntry(normalized);
+      if (resolved?.key !== key) {
+        continue;
+      }
+      ids.add(normalized);
+    }
+    cachedIds = [...ids].sort();
+  }
+  return cachedIds;
+}
diff --git a/src/common/utils/tokens/modelStats.ts b/src/common/utils/tokens/modelStats.ts
index 03e498d358f..ee6fb154ad9 100644
--- a/src/common/utils/tokens/modelStats.ts
+++ b/src/common/utils/tokens/modelStats.ts
@@ -41,7 +41,12 @@ const PROVIDER_KEY_ALIASES: Record<string, string> = {
   "github-copilot": "github_copilot",
 };
 
-function parseNum(value: unknown): number | null {
+/**
+ * Runtime numeric semantics for raw catalog values (accepts numeric strings
+ * with comma separators). Exported so update-models validation compares
+ * magnitudes exactly as getModelStats would parse them.
+ */
+export function parseNum(value: unknown): number | null {
   if (typeof value === "number" && Number.isFinite(value)) {
     return value;
   }
@@ -70,9 +75,10 @@ function hasTieredPricing(data: RawModelData): boolean {
 }
 
 /**
- * Validates raw model data has required fields
+ * Whether raw model metadata is usable for stats resolution. Exported so the
+ * update-models validation applies the same bar as getModelStats.
  */
-function isValidModelData(data: RawModelData): boolean {
+export function hasUsableTokenLimits(data: RawModelData): boolean {
   const maxInputTokens = parseNum(data.max_input_tokens);
   return maxInputTokens != null && maxInputTokens > 0;
 }
@@ -135,10 +141,13 @@ function stripLatestSuffix(modelName: string): string {
 }
 
 /**
- * Generates lookup keys for a model string with multiple naming patterns
- * Handles LiteLLM conventions like "ollama/model-cloud" and "provider/model"
+ * Generates lookup keys for a model string with multiple naming patterns.
+ * Handles LiteLLM conventions like "ollama/model-cloud" and "provider/model".
+ * Exported so capability resolution shares the exact same key preference:
+ * provider-scoped entries must win over bare-name entries in both lookups,
+ * otherwise a model can inherit stats and capabilities from different entries.
  */
-function generateLookupKeys(modelString: string): string[] {
+export function generateModelLookupKeys(modelString: string): string[] {
   const colonIndex = modelString.indexOf(":");
   const provider = colonIndex !== -1 ? modelString.slice(0, colonIndex) : "";
   const modelName = colonIndex !== -1 ? modelString.slice(colonIndex + 1) : modelString;
@@ -222,34 +231,51 @@ function generateLookupKeys(modelString: string): string[] {
   return keys;
 }
 
+export interface ResolvedRawModelEntry {
+  /** The models-extra/models.json key the stats actually come from. */
+  key: string;
+  data: RawModelData;
+}
+
 /**
- * Gets model statistics for a given Vercel AI SDK model string
- * @param modelString - Format: "provider:model-name" (e.g., "anthropic:claude-opus-4-1", "ollama:gpt-oss:20b")
- * @returns ModelStats or null if model not found
+ * Resolves the raw override/catalog entry backing a model string, preserving
+ * getModelStats' precedence: models-extra across every lookup key first, then
+ * models.json. Exported so the Treat-as catalog can exclude ids whose stats
+ * would resolve from a different entry than the row represents.
  */
-export function getModelStats(modelString: string): ModelStats | null {
+export function resolveRawModelEntry(modelString: string): ResolvedRawModelEntry | null {
   const normalized = normalizeToCanonical(modelString);
-  const lookupKeys = generateLookupKeys(normalized);
+  const lookupKeys = generateModelLookupKeys(normalized);
 
   // Check models-extra.ts first (overrides for models with incorrect upstream data)
   for (const key of lookupKeys) {
     const data = (modelsExtra as Record<string, RawModelData>)[key];
-    if (data && isValidModelData(data)) {
-      return extractModelStats(data);
+    if (data && hasUsableTokenLimits(data)) {
+      return { key, data };
     }
   }
 
   // Fall back to main models.json
   for (const key of lookupKeys) {
     const data = (modelsData as Record<string, RawModelData>)[key];
-    if (data && isValidModelData(data)) {
-      return extractModelStats(data);
+    if (data && hasUsableTokenLimits(data)) {
+      return { key, data };
     }
   }
 
   return null;
 }
 
+/**
+ * Gets model statistics for a given Vercel AI SDK model string
+ * @param modelString - Format: "provider:model-name" (e.g., "anthropic:claude-opus-4-1", "ollama:gpt-oss:20b")
+ * @returns ModelStats or null if model not found
+ */
+export function getModelStats(modelString: string): ModelStats | null {
+  const entry = resolveRawModelEntry(modelString);
+  return entry === null ? null : extractModelStats(entry.data);
+}
+
 export function getModelStatsResolved(
   modelString: string,
   providersConfig: ProvidersConfigMap | null
diff --git a/src/common/utils/tokens/models.json b/src/common/utils/tokens/models.json
index 63489dfadca..ffa99d6cae7 100644
--- a/src/common/utils/tokens/models.json
+++ b/src/common/utils/tokens/models.json
@@ -1,14 +1,4 @@
 {
-  "sample_spec": {
-    "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
-    "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
-    "input_cost_per_token": 0,
-    "output_cost_per_token": 0,
-    "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search",
-    "litellm_provider": "one of https://docs.litellm.ai/docs/providers",
-    "supports_vision": true,
-    "supports_audio_input": true
-  },
   "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0": {
     "max_input_tokens": 2600,
     "mode": "image_generation",
@@ -23,11 +13,47 @@
     "mode": "image_generation",
     "litellm_provider": "openai"
   },
+  "1024-x-1024/gpt-image-1.5": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "1024-x-1024/gpt-image-1.5-2025-12-16": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "1024-x-1024/max-steps/stability.stable-diffusion-xl-v1": {
     "max_input_tokens": 77,
     "mode": "image_generation",
     "litellm_provider": "bedrock"
   },
+  "1024-x-1536/gpt-image-1.5": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "1024-x-1536/gpt-image-1.5-2025-12-16": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "1536-x-1024/gpt-image-1.5": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "1536-x-1024/gpt-image-1.5-2025-12-16": {
+    "mode": "image_generation",
+    "litellm_provider": "openai",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "256-x-256/dall-e-2": {
     "mode": "image_generation",
     "litellm_provider": "openai"
@@ -46,6 +72,10 @@
     "mode": "image_generation",
     "litellm_provider": "bedrock"
   },
+  "agentcore/search": {
+    "mode": "search",
+    "litellm_provider": "agentcore"
+  },
   "ai21.j2-mid-v1": {
     "max_input_tokens": 8191,
     "max_output_tokens": 8191,
@@ -134,54 +164,46 @@
     "mode": "image_generation",
     "litellm_provider": "aiml"
   },
-  "amazon.nova-canvas-v1:0": {
-    "max_input_tokens": 2600,
+  "aiml/openai/gpt-image-2": {
     "mode": "image_generation",
-    "litellm_provider": "bedrock"
-  },
-  "us.writer.palmyra-x4-v1:0": {
-    "max_input_tokens": 128000,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 0.0000025,
-    "output_cost_per_token": 0.00001,
-    "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true
+    "litellm_provider": "aiml",
+    "supports_vision": true
   },
-  "us.writer.palmyra-x5-v1:0": {
-    "max_input_tokens": 1000000,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 6e-7,
-    "output_cost_per_token": 0.000006,
+  "amazon-nova/nova-lite-v1": {
+    "max_input_tokens": 300000,
+    "max_output_tokens": 10000,
+    "input_cost_per_token": 6e-8,
+    "output_cost_per_token": 2.4e-7,
     "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true
+    "litellm_provider": "amazon_nova",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "writer.palmyra-x4-v1:0": {
+  "amazon-nova/nova-micro-v1": {
     "max_input_tokens": 128000,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 0.0000025,
-    "output_cost_per_token": 0.00001,
+    "max_output_tokens": 10000,
+    "input_cost_per_token": 3.5e-8,
+    "output_cost_per_token": 1.4e-7,
     "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true
+    "litellm_provider": "amazon_nova"
   },
-  "writer.palmyra-x5-v1:0": {
+  "amazon-nova/nova-premier-v1": {
     "max_input_tokens": 1000000,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 6e-7,
-    "output_cost_per_token": 0.000006,
+    "max_output_tokens": 10000,
+    "input_cost_per_token": 0.0000025,
+    "output_cost_per_token": 0.0000125,
     "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true
+    "litellm_provider": "amazon_nova",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "amazon.nova-lite-v1:0": {
+  "amazon-nova/nova-pro-v1": {
     "max_input_tokens": 300000,
     "max_output_tokens": 10000,
-    "input_cost_per_token": 6e-8,
-    "output_cost_per_token": 2.4e-7,
+    "input_cost_per_token": 8e-7,
+    "output_cost_per_token": 0.0000032,
     "mode": "chat",
-    "litellm_provider": "bedrock_converse",
+    "litellm_provider": "amazon_nova",
     "supports_pdf_input": true,
     "supports_vision": true
   },
@@ -197,50 +219,41 @@
     "supports_vision": true,
     "supports_video_input": true
   },
-  "apac.amazon.nova-2-lite-v1:0": {
-    "max_input_tokens": 1000000,
-    "max_output_tokens": 64000,
-    "input_cost_per_token": 3.3e-7,
-    "output_cost_per_token": 0.00000275,
-    "cache_read_input_token_cost": 8.25e-8,
-    "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true,
-    "supports_vision": true,
+  "amazon.nova-2-multimodal-embeddings-v1:0": {
+    "max_input_tokens": 8172,
+    "input_cost_per_token": 1.35e-7,
+    "output_cost_per_token": 0,
+    "mode": "embedding",
+    "litellm_provider": "bedrock",
+    "supports_audio_input": true,
     "supports_video_input": true
   },
-  "eu.amazon.nova-2-lite-v1:0": {
+  "amazon.nova-2-pro-preview-20251202-v1:0": {
     "max_input_tokens": 1000000,
     "max_output_tokens": 64000,
-    "input_cost_per_token": 3.3e-7,
-    "output_cost_per_token": 0.00000275,
-    "cache_read_input_token_cost": 8.25e-8,
+    "input_cost_per_token": 0.0000021875,
+    "output_cost_per_token": 0.0000175,
+    "cache_read_input_token_cost": 5.46875e-7,
     "mode": "chat",
     "litellm_provider": "bedrock_converse",
     "supports_pdf_input": true,
     "supports_vision": true,
     "supports_video_input": true
   },
-  "us.amazon.nova-2-lite-v1:0": {
-    "max_input_tokens": 1000000,
-    "max_output_tokens": 64000,
-    "input_cost_per_token": 3.3e-7,
-    "output_cost_per_token": 0.00000275,
-    "cache_read_input_token_cost": 8.25e-8,
+  "amazon.nova-canvas-v1:0": {
+    "max_input_tokens": 2600,
+    "mode": "image_generation",
+    "litellm_provider": "bedrock"
+  },
+  "amazon.nova-lite-v1:0": {
+    "max_input_tokens": 300000,
+    "max_output_tokens": 10000,
+    "input_cost_per_token": 6e-8,
+    "output_cost_per_token": 2.4e-7,
     "mode": "chat",
     "litellm_provider": "bedrock_converse",
     "supports_pdf_input": true,
-    "supports_vision": true,
-    "supports_video_input": true
-  },
-  "amazon.nova-2-multimodal-embeddings-v1:0": {
-    "max_input_tokens": 8172,
-    "input_cost_per_token": 1.35e-7,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "bedrock",
-    "supports_audio_input": true,
-    "supports_video_input": true
+    "supports_vision": true
   },
   "amazon.nova-micro-v1:0": {
     "max_input_tokens": 128000,
@@ -268,6 +281,13 @@
     "mode": "rerank",
     "litellm_provider": "bedrock"
   },
+  "amazon.titan-embed-g1-text-02": {
+    "max_input_tokens": 8192,
+    "input_cost_per_token": 1e-7,
+    "output_cost_per_token": 0,
+    "mode": "embedding",
+    "litellm_provider": "bedrock"
+  },
   "amazon.titan-embed-image-v1": {
     "max_input_tokens": 128,
     "input_cost_per_token": 8e-7,
@@ -284,7 +304,7 @@
   },
   "amazon.titan-embed-text-v2:0": {
     "max_input_tokens": 8192,
-    "input_cost_per_token": 2e-7,
+    "input_cost_per_token": 2e-8,
     "output_cost_per_token": 0,
     "mode": "embedding",
     "litellm_provider": "bedrock"
@@ -301,45 +321,6 @@
     "mode": "image_generation",
     "litellm_provider": "bedrock"
   },
-  "twelvelabs.marengo-embed-2-7-v1:0": {
-    "max_input_tokens": 77,
-    "input_cost_per_token": 0.00007,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "bedrock"
-  },
-  "us.twelvelabs.marengo-embed-2-7-v1:0": {
-    "max_input_tokens": 77,
-    "input_cost_per_token": 0.00007,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "bedrock"
-  },
-  "eu.twelvelabs.marengo-embed-2-7-v1:0": {
-    "max_input_tokens": 77,
-    "input_cost_per_token": 0.00007,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "bedrock"
-  },
-  "twelvelabs.pegasus-1-2-v1:0": {
-    "output_cost_per_token": 0.0000075,
-    "mode": "chat",
-    "litellm_provider": "bedrock",
-    "supports_video_input": true
-  },
-  "us.twelvelabs.pegasus-1-2-v1:0": {
-    "output_cost_per_token": 0.0000075,
-    "mode": "chat",
-    "litellm_provider": "bedrock",
-    "supports_video_input": true
-  },
-  "eu.twelvelabs.pegasus-1-2-v1:0": {
-    "output_cost_per_token": 0.0000075,
-    "mode": "chat",
-    "litellm_provider": "bedrock",
-    "supports_video_input": true
-  },
   "amazon.titan-text-express-v1": {
     "max_input_tokens": 42000,
     "max_output_tokens": 8000,
@@ -375,47 +356,33 @@
     "litellm_provider": "bedrock",
     "supports_pdf_input": true
   },
-  "anthropic.claude-haiku-4-5-20251001-v1:0": {
-    "max_input_tokens": 200000,
-    "max_output_tokens": 64000,
-    "input_cost_per_token": 0.000001,
-    "output_cost_per_token": 0.000005,
-    "cache_creation_input_token_cost": 0.00000125,
-    "cache_read_input_token_cost": 1e-7,
-    "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
-  "anthropic.claude-haiku-4-5@20251001": {
-    "max_input_tokens": 200000,
-    "max_output_tokens": 64000,
-    "input_cost_per_token": 0.000001,
-    "output_cost_per_token": 0.000005,
-    "cache_creation_input_token_cost": 0.00000125,
-    "cache_read_input_token_cost": 1e-7,
-    "mode": "chat",
-    "litellm_provider": "bedrock_converse",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
   "anthropic.claude-3-5-sonnet-20240620-v1:0": {
-    "max_input_tokens": 200000,
+    "max_input_tokens": 1000000,
     "max_output_tokens": 4096,
     "input_cost_per_token": 0.000003,
     "output_cost_per_token": 0.000015,
+    "input_cost_per_token_above_200k_tokens": 0.000006,
+    "output_cost_per_token_above_200k_tokens": 0.00003,
+    "cache_creation_input_token_cost": 0.00000375,
+    "cache_creation_input_token_cost_above_200k_tokens": 0.0000075,
+    "cache_read_input_token_cost": 3e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 6e-7,
     "mode": "chat",
     "litellm_provider": "bedrock",
     "supports_pdf_input": true,
     "supports_vision": true
   },
   "anthropic.claude-3-5-sonnet-20241022-v2:0": {
-    "max_input_tokens": 200000,
+    "max_input_tokens": 1000000,
     "max_output_tokens": 8192,
     "input_cost_per_token": 0.000003,
     "output_cost_per_token": 0.000015,
+    "input_cost_per_token_above_200k_tokens": 0.000006,
+    "output_cost_per_token_above_200k_tokens": 0.00003,
     "cache_creation_input_token_cost": 0.00000375,
+    "cache_creation_input_token_cost_above_200k_tokens": 0.0000075,
     "cache_read_input_token_cost": 3e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 6e-7,
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@@ -450,6 +417,8 @@
     "max_output_tokens": 4096,
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     "output_cost_per_token": 0.00000125,
+    "cache_creation_input_token_cost": 3.125e-7,
+    "cache_read_input_token_cost": 2.5e-8,
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@@ -460,6 +429,8 @@
     "max_output_tokens": 4096,
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@@ -469,19 +440,66 @@
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     "mode": "chat",
     "litellm_provider": "bedrock",
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-    "max_output_tokens": 8191,
-    "input_cost_per_token": 8e-7,
-    "output_cost_per_token": 0.0000024,
+  "anthropic.claude-fable-5": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00001,
+    "output_cost_per_token": 0.00005,
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-    "litellm_provider": "bedrock"
-  },
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-haiku-4-5-20251001-v1:0": {
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+    "max_output_tokens": 64000,
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+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-haiku-4-5@20251001": {
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+    "max_output_tokens": 64000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.000005,
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+    "cache_read_input_token_cost": 1e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-instant-v1": {
+    "max_input_tokens": 100000,
+    "max_output_tokens": 8191,
+    "input_cost_per_token": 8e-7,
+    "output_cost_per_token": 0.0000024,
+    "mode": "chat",
+    "litellm_provider": "bedrock"
+  },
+  "anthropic.claude-mythos-preview": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0,
+    "output_cost_per_token": 0,
+    "mode": "chat",
+    "litellm_provider": "bedrock",
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   "anthropic.claude-opus-4-1-20250805-v1:0": {
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     "max_output_tokens": 32000,
@@ -518,6 +536,54 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "anthropic.claude-opus-4-6-v1": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.000025,
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+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-opus-4-7": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.000025,
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+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-opus-4-8": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.000025,
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+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-opus-5": {
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+    "max_output_tokens": 128000,
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+    "output_cost_per_token": 0.000025,
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+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "anthropic.claude-sonnet-4-20250514-v1:0": {
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     "max_output_tokens": 64000,
@@ -550,6 +616,30 @@
     "supports_pdf_input": true,
     "supports_vision": true
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+  "anthropic.claude-sonnet-4-6": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 64000,
+    "input_cost_per_token": 0.000003,
+    "output_cost_per_token": 0.000015,
+    "cache_creation_input_token_cost": 0.00000375,
+    "cache_read_input_token_cost": 3e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "anthropic.claude-sonnet-5": {
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+    "max_output_tokens": 128000,
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+    "output_cost_per_token": 0.00001,
+    "cache_creation_input_token_cost": 0.0000025,
+    "cache_read_input_token_cost": 2e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "anthropic.claude-v1": {
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     "max_output_tokens": 8191,
@@ -662,6 +752,30 @@
     "mode": "chat",
     "litellm_provider": "anyscale"
   },
+  "apac.amazon.nova-2-lite-v1:0": {
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+    "max_output_tokens": 64000,
+    "input_cost_per_token": 3.3e-7,
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+    "cache_read_input_token_cost": 8.25e-8,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true,
+    "supports_video_input": true
+  },
+  "apac.amazon.nova-2-pro-preview-20251202-v1:0": {
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+    "max_output_tokens": 64000,
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+    "cache_read_input_token_cost": 5.46875e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true,
+    "supports_video_input": true
+  },
   "apac.amazon.nova-lite-v1:0": {
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@@ -695,6 +809,8 @@
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+    "cache_read_input_token_cost": 3e-7,
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     "litellm_provider": "bedrock",
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@@ -717,6 +833,20 @@
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+    "cache_read_input_token_cost": 2.5e-8,
+    "mode": "chat",
+    "litellm_provider": "bedrock",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "apac.anthropic.claude-3-sonnet-20240229-v1:0": {
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+    "max_output_tokens": 4096,
+    "input_cost_per_token": 0.000003,
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+    "cache_read_input_token_cost": 3e-7,
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     "litellm_provider": "bedrock",
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@@ -734,16 +864,6 @@
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-  "apac.anthropic.claude-3-sonnet-20240229-v1:0": {
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   "apac.anthropic.claude-sonnet-4-20250514-v1:0": {
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     "max_output_tokens": 64000,
@@ -760,6 +880,14 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "apiserpent/deep_search": {
+    "mode": "search",
+    "litellm_provider": "apiserpent"
+  },
+  "apiserpent/search": {
+    "mode": "search",
+    "litellm_provider": "apiserpent"
+  },
   "assemblyai/best": {
     "mode": "audio_transcription",
     "litellm_provider": "assemblyai"
@@ -768,6 +896,66 @@
     "mode": "audio_transcription",
     "litellm_provider": "assemblyai"
   },
+  "au.anthropic.claude-haiku-4-5-20251001-v1:0": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 64000,
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+    "cache_creation_input_token_cost": 0.000001375,
+    "cache_read_input_token_cost": 1.1e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "au.anthropic.claude-opus-4-6-v1": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000055,
+    "output_cost_per_token": 0.0000275,
+    "cache_creation_input_token_cost": 0.000006875,
+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "au.anthropic.claude-opus-4-7": {
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+    "input_cost_per_token": 0.0000055,
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+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "au.anthropic.claude-opus-4-8": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000055,
+    "output_cost_per_token": 0.0000275,
+    "cache_creation_input_token_cost": 0.000006875,
+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "au.anthropic.claude-opus-5": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000055,
+    "output_cost_per_token": 0.0000275,
+    "cache_creation_input_token_cost": 0.000006875,
+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "au.anthropic.claude-sonnet-4-5-20250929-v1:0": {
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     "max_output_tokens": 64000,
@@ -784,6 +972,46 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "au.anthropic.claude-sonnet-4-6": {
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+    "max_output_tokens": 64000,
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+    "output_cost_per_token": 0.0000165,
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+    "cache_read_input_token_cost": 3.3e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "au.anthropic.claude-sonnet-5": {
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+    "max_output_tokens": 128000,
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+    "output_cost_per_token": 0.000011,
+    "cache_creation_input_token_cost": 0.00000275,
+    "cache_read_input_token_cost": 2.2e-7,
+    "mode": "chat",
+    "litellm_provider": "bedrock_converse",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "aws_polly/generative": {
+    "mode": "audio_speech",
+    "litellm_provider": "aws_polly"
+  },
+  "aws_polly/long-form": {
+    "mode": "audio_speech",
+    "litellm_provider": "aws_polly"
+  },
+  "aws_polly/neural": {
+    "mode": "audio_speech",
+    "litellm_provider": "aws_polly"
+  },
+  "aws_polly/standard": {
+    "mode": "audio_speech",
+    "litellm_provider": "aws_polly"
+  },
   "azure/ada": {
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     "input_cost_per_token": 1e-7,
@@ -810,54 +1038,6 @@
     "mode": "chat",
     "litellm_provider": "azure"
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-  "azure_ai/claude-haiku-4-5": {
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@@ -871,14 +1051,6 @@
     "mode": "chat",
     "litellm_provider": "azure"
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   "azure/eu/gpt-4o-2024-08-06": {
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@@ -915,7 +1087,7 @@
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     "output_cost_per_token": 0.00000264,
     "cache_read_input_token_cost": 3.3e-7,
-    "mode": "chat",
+    "mode": "realtime",
     "litellm_provider": "azure",
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@@ -925,7 +1097,7 @@
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-    "mode": "chat",
+    "mode": "realtime",
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@@ -935,7 +1107,7 @@
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-    "mode": "chat",
+    "mode": "realtime",
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     "supports_audio_input": true
   },
@@ -961,6 +1133,17 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "azure/eu/gpt-5-nano-2025-08-07": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 5.5e-8,
+    "output_cost_per_token": 4.4e-7,
+    "cache_read_input_token_cost": 5.5e-9,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "azure/eu/gpt-5.1": {
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@@ -1005,12 +1188,89 @@
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-  "azure/eu/gpt-5-nano-2025-08-07": {
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+  "azure/eu/gpt-5.4": {
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     "max_output_tokens": 128000,
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+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.4-2026-03-05": {
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+    "max_output_tokens": 128000,
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+    "cache_read_input_token_cost": 2.8e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.5": {
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+    "output_cost_per_token": 0.000033,
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+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.5-2026-04-23": {
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+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.6": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000055,
+    "output_cost_per_token": 0.000033,
+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.6-luna": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2.2e-7,
+    "output_cost_per_token": 0.00000132,
+    "cache_read_input_token_cost": 2.2e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.6-sol": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000055,
+    "output_cost_per_token": 0.000033,
+    "cache_read_input_token_cost": 5.5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/eu/gpt-5.6-terra": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000022,
+    "output_cost_per_token": 0.0000132,
+    "cache_read_input_token_cost": 2.2e-7,
     "mode": "chat",
     "litellm_provider": "azure",
     "supports_pdf_input": true,
@@ -1188,22 +1448,6 @@
     "mode": "chat",
     "litellm_provider": "azure"
   },
-  "azure/gpt-35-turbo-0301": {
-    "max_input_tokens": 4097,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 2e-7,
-    "output_cost_per_token": 0.000002,
-    "mode": "chat",
-    "litellm_provider": "azure"
-  },
-  "azure/gpt-35-turbo-0613": {
-    "max_input_tokens": 4097,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 0.0000015,
-    "output_cost_per_token": 0.000002,
-    "mode": "chat",
-    "litellm_provider": "azure"
-  },
   "azure/gpt-35-turbo-1106": {
     "max_input_tokens": 16384,
     "max_output_tokens": 4096,
@@ -1425,24 +1669,6 @@
     "litellm_provider": "azure",
     "supports_vision": true
   },
-  "azure/gpt-audio-2025-08-28": {
-    "max_input_tokens": 128000,
-    "max_output_tokens": 16384,
-    "input_cost_per_token": 0.0000025,
-    "output_cost_per_token": 0.00001,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_vision": false
-  },
-  "azure/gpt-audio-mini-2025-10-06": {
-    "max_input_tokens": 128000,
-    "max_output_tokens": 16384,
-    "input_cost_per_token": 6e-7,
-    "output_cost_per_token": 0.0000024,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_vision": false
-  },
   "azure/gpt-4o-audio-preview-2024-12-17": {
     "max_input_tokens": 128000,
     "max_output_tokens": 16384,
@@ -1487,27 +1713,7 @@
     "input_cost_per_token": 6e-7,
     "output_cost_per_token": 0.0000024,
     "cache_read_input_token_cost": 3e-7,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_audio_input": true
-  },
-  "azure/gpt-realtime-2025-08-28": {
-    "max_input_tokens": 32000,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 0.000004,
-    "output_cost_per_token": 0.000016,
-    "cache_read_input_token_cost": 0.000004,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_audio_input": true
-  },
-  "azure/gpt-realtime-mini-2025-10-06": {
-    "max_input_tokens": 32000,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 6e-7,
-    "output_cost_per_token": 0.0000024,
-    "cache_read_input_token_cost": 6e-8,
-    "mode": "chat",
+    "mode": "realtime",
     "litellm_provider": "azure",
     "supports_audio_input": true
   },
@@ -1531,7 +1737,7 @@
     "input_cost_per_token": 0.000005,
     "output_cost_per_token": 0.00002,
     "cache_read_input_token_cost": 0.0000025,
-    "mode": "chat",
+    "mode": "realtime",
     "litellm_provider": "azure",
     "supports_audio_input": true
   },
@@ -1541,7 +1747,7 @@
     "input_cost_per_token": 0.000005,
     "output_cost_per_token": 0.00002,
     "cache_read_input_token_cost": 0.0000025,
-    "mode": "chat",
+    "mode": "realtime",
     "litellm_provider": "azure",
     "supports_audio_input": true
   },
@@ -1561,50 +1767,6 @@
     "mode": "audio_transcription",
     "litellm_provider": "azure"
   },
-  "azure/gpt-5.1-2025-11-13": {
-    "max_input_tokens": 272000,
-    "max_output_tokens": 128000,
-    "input_cost_per_token": 0.00000125,
-    "output_cost_per_token": 0.00001,
-    "cache_read_input_token_cost": 1.25e-7,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
-  "azure/gpt-5.1-chat-2025-11-13": {
-    "max_input_tokens": 128000,
-    "max_output_tokens": 16384,
-    "input_cost_per_token": 0.00000125,
-    "output_cost_per_token": 0.00001,
-    "cache_read_input_token_cost": 1.25e-7,
-    "mode": "chat",
-    "litellm_provider": "azure",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
-  "azure/gpt-5.1-codex-2025-11-13": {
-    "max_input_tokens": 272000,
-    "max_output_tokens": 128000,
-    "input_cost_per_token": 0.00000125,
-    "output_cost_per_token": 0.00001,
-    "cache_read_input_token_cost": 1.25e-7,
-    "mode": "responses",
-    "litellm_provider": "azure",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
-  "azure/gpt-5.1-codex-mini-2025-11-13": {
-    "max_input_tokens": 272000,
-    "max_output_tokens": 128000,
-    "input_cost_per_token": 2.5e-7,
-    "output_cost_per_token": 0.000002,
-    "cache_read_input_token_cost": 2.5e-8,
-    "mode": "responses",
-    "litellm_provider": "azure",
-    "supports_pdf_input": true,
-    "supports_vision": true
-  },
   "azure/gpt-5": {
     "max_input_tokens": 272000,
     "max_output_tokens": 128000,
@@ -1725,6 +1887,17 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "azure/gpt-5.1-2025-11-13": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.00001,
+    "cache_read_input_token_cost": 1.25e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "azure/gpt-5.1-chat": {
     "max_input_tokens": 128000,
     "max_output_tokens": 128000,
@@ -1736,6 +1909,17 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
+  "azure/gpt-5.1-chat-2025-11-13": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.00001,
+    "cache_read_input_token_cost": 1.25e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
   "azure/gpt-5.1-codex": {
     "max_input_tokens": 272000,
     "max_output_tokens": 128000,
@@ -1747,7 +1931,7 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
-  "azure/gpt-5.1-codex-max": {
+  "azure/gpt-5.1-codex-2025-11-13": {
     "max_input_tokens": 272000,
     "max_output_tokens": 128000,
     "input_cost_per_token": 0.00000125,
@@ -1758,11 +1942,33 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
-  "azure/gpt-5.1-codex-mini": {
+  "azure/gpt-5.1-codex-max": {
     "max_input_tokens": 272000,
     "max_output_tokens": 128000,
-    "input_cost_per_token": 2.5e-7,
-    "output_cost_per_token": 0.000002,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.00001,
+    "cache_read_input_token_cost": 1.25e-7,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.1-codex-mini": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2.5e-7,
+    "output_cost_per_token": 0.000002,
+    "cache_read_input_token_cost": 2.5e-8,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.1-codex-mini-2025-11-13": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2.5e-7,
+    "output_cost_per_token": 0.000002,
     "cache_read_input_token_cost": 2.5e-8,
     "mode": "responses",
     "litellm_provider": "azure",
@@ -1814,12 +2020,12 @@
     "supports_vision": true
   },
   "azure/gpt-5.2-codex": {
-    "max_input_tokens": 128000,
-    "max_output_tokens": 16384,
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
     "input_cost_per_token": 0.00000175,
     "output_cost_per_token": 0.000014,
     "cache_read_input_token_cost": 1.75e-7,
-    "mode": "chat",
+    "mode": "responses",
     "litellm_provider": "azure",
     "supports_pdf_input": true,
     "supports_vision": true
@@ -1844,61 +2050,244 @@
     "supports_pdf_input": true,
     "supports_vision": true
   },
-  "azure/gpt-image-1": {
+  "azure/gpt-5.3-chat": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 0.00000175,
+    "output_cost_per_token": 0.000014,
+    "cache_read_input_token_cost": 1.75e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.3-codex": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00000175,
+    "output_cost_per_token": 0.000014,
+    "cache_read_input_token_cost": 1.75e-7,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4": {
+    "max_input_tokens": 1050000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000025,
+    "output_cost_per_token": 0.000015,
+    "cache_read_input_token_cost": 2.5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-2026-03-05": {
+    "max_input_tokens": 1050000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000025,
+    "output_cost_per_token": 0.000015,
+    "cache_read_input_token_cost": 2.5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-mini": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 7.5e-7,
+    "output_cost_per_token": 0.0000045,
+    "cache_read_input_token_cost": 7.5e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-mini-2026-03-17": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 7.5e-7,
+    "output_cost_per_token": 0.0000045,
+    "cache_read_input_token_cost": 7.5e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-nano": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2e-7,
+    "output_cost_per_token": 0.00000125,
+    "cache_read_input_token_cost": 2e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-nano-2026-03-17": {
+    "max_input_tokens": 272000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2e-7,
+    "output_cost_per_token": 0.00000125,
+    "cache_read_input_token_cost": 2e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-pro": {
+    "max_input_tokens": 1050000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00003,
+    "output_cost_per_token": 0.00018,
+    "cache_read_input_token_cost": 0.000003,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.4-pro-2026-03-05": {
+    "max_input_tokens": 1050000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00003,
+    "output_cost_per_token": 0.00018,
+    "cache_read_input_token_cost": 0.000003,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
+  },
+  "azure/gpt-5.5": {
+    "max_input_tokens": 1050000,
+    "max_output_tokens": 128000,
     "input_cost_per_token": 0.000005,
-    "output_cost_per_image_token": 0.00004,
-    "cache_read_input_token_cost": 0.00000125,
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+    "output_cost_per_token": 0.00003,
+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/hd/1024-x-1024/dall-e-3": {
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-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.5-2026-04-23": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.00003,
+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/hd/1024-x-1792/dall-e-3": {
-    "output_cost_per_token": 0,
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.5-pro": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00003,
+    "output_cost_per_token": 0.00018,
+    "cache_read_input_token_cost": 0.000003,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/hd/1792-x-1024/dall-e-3": {
-    "output_cost_per_token": 0,
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.5-pro-2026-04-23": {
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+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.00003,
+    "output_cost_per_token": 0.00018,
+    "cache_read_input_token_cost": 0.000003,
+    "mode": "responses",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/high/1024-x-1024/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.6": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.00003,
+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/high/1024-x-1536/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.6-luna": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 2e-7,
+    "output_cost_per_token": 0.0000012,
+    "cache_read_input_token_cost": 2e-8,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/high/1536-x-1024/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.6-sol": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.00003,
+    "cache_read_input_token_cost": 5e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/low/1024-x-1024/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-5.6-terra": {
+    "max_input_tokens": 922000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000002,
+    "output_cost_per_token": 0.000012,
+    "cache_read_input_token_cost": 2e-7,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_pdf_input": true,
+    "supports_vision": true
   },
-  "azure/low/1024-x-1536/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-audio-1.5-2026-02-23": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 0.0000025,
+    "output_cost_per_token": 0.00001,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_vision": false
   },
-  "azure/low/1536-x-1024/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-audio-2025-08-28": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 0.0000025,
+    "output_cost_per_token": 0.00001,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_vision": false
   },
-  "azure/medium/1024-x-1024/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-audio-mini": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000024,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_vision": false
   },
-  "azure/medium/1024-x-1536/gpt-image-1": {
-    "mode": "image_generation",
-    "litellm_provider": "azure"
+  "azure/gpt-audio-mini-2025-10-06": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 16384,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000024,
+    "mode": "chat",
+    "litellm_provider": "azure",
+    "supports_vision": false
   },
-  "azure/medium/1536-x-1024/gpt-image-1": {
+  "azure/gpt-image-1": {
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_image_token": 0.00004,
+    "cache_read_input_token_cost": 0.00000125,
     "mode": "image_generation",
     "litellm_provider": "azure"
   },
@@ -1923,7 +2312,119 @@
     "mode": "image_generation",
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   },
-  "azure/low/1024-x-1024/gpt-image-1-mini": {
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+    "mode": "image_generation",
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+    "supports_vision": true
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+  "azure/gpt-realtime-whisper": {
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+    "litellm_provider": "azure",
+    "supports_audio_input": true
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+    "litellm_provider": "azure"
+  },
+  "azure/high/1024-x-1024/gpt-image-1-mini": {
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+    "litellm_provider": "azure"
+  },
+  "azure/high/1024-x-1536/gpt-image-1": {
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+    "litellm_provider": "azure"
+  },
+  "azure/high/1024-x-1536/gpt-image-1-mini": {
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+    "litellm_provider": "azure"
+  },
+  "azure/high/1536-x-1024/gpt-image-1": {
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+    "litellm_provider": "azure"
+  },
+  "azure/high/1536-x-1024/gpt-image-1-mini": {
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+  "azure/low/1024-x-1024/gpt-image-1": {
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+    "litellm_provider": "azure"
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+  "azure/low/1024-x-1024/gpt-image-1-mini": {
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+  "azure/low/1024-x-1536/gpt-image-1": {
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     "litellm_provider": "azure"
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@@ -1931,31 +2432,35 @@
     "mode": "image_generation",
     "litellm_provider": "azure"
   },
+  "azure/low/1536-x-1024/gpt-image-1": {
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+  },
   "azure/low/1536-x-1024/gpt-image-1-mini": {
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   },
-  "azure/medium/1024-x-1024/gpt-image-1-mini": {
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     "litellm_provider": "azure"
   },
-  "azure/medium/1024-x-1536/gpt-image-1-mini": {
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   },
-  "azure/medium/1536-x-1024/gpt-image-1-mini": {
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   },
-  "azure/high/1024-x-1024/gpt-image-1-mini": {
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   },
-  "azure/high/1024-x-1536/gpt-image-1-mini": {
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   },
-  "azure/high/1536-x-1024/gpt-image-1-mini": {
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     "litellm_provider": "azure"
   },
@@ -2123,6 +2628,30 @@
     "litellm_provider": "azure",
     "supports_vision": true
   },
+  "azure/sora-2": {
+    "mode": "video_generation",
+    "litellm_provider": "azure"
+  },
+  "azure/sora-2-pro": {
+    "mode": "video_generation",
+    "litellm_provider": "azure"
+  },
+  "azure/sora-2-pro-high-res": {
+    "mode": "video_generation",
+    "litellm_provider": "azure"
+  },
+  "azure/speech/azure-stt": {
+    "mode": "audio_transcription",
+    "litellm_provider": "azure"
+  },
+  "azure/speech/azure-tts": {
+    "mode": "audio_speech",
+    "litellm_provider": "azure"
+  },
+  "azure/speech/azure-tts-hd": {
+    "mode": "audio_speech",
+    "litellm_provider": "azure"
+  },
   "azure/standard/1024-x-1024/dall-e-2": {
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     "mode": "image_generation",
@@ -2164,14 +2693,6 @@
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     "litellm_provider": "azure"
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-  "azure/speech/azure-tts": {
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-    "litellm_provider": "azure"
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-  "azure/speech/azure-tts-hd": {
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   "azure/tts-1": {
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     "litellm_provider": "azure"
@@ -2246,7 +2767,7 @@
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-    "mode": "chat",
+    "mode": "realtime",
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@@ -2256,7 +2777,7 @@
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-    "mode": "chat",
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@@ -2266,7 +2787,7 @@
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@@ -2347,6 +2868,94 @@
     "supports_pdf_input": true,
     "supports_vision": true
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+  "azure/us/gpt-5.4": {
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+    "supports_vision": true
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@@ -2433,37 +3042,164 @@
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     "litellm_provider": "azure_ai"
   },
-  "azure_ai/flux.2-pro": {
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@@ -2481,6 +3217,32 @@
     "litellm_provider": "azure_ai",
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+  "azure_ai/MAI-DS-R1": {
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   "azure_ai/Meta-Llama-3-70B-Instruct": {
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@@ -2611,23 +3373,23 @@
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-  "azure_ai/Phi-4-multimodal-instruct": {
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@@ -11824,7886 +11938,14275 @@
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+    "supports_vision": true
   },
-  "novita/sao10k/l31-70b-euryale-v2.2": {
-    "max_input_tokens": 8192,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 0.00000148,
-    "output_cost_per_token": 0.00000148,
+  "xai/grok-4.20-multi-agent-0309": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 1000000,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.0000025,
+    "input_cost_per_token_above_200k_tokens": 0.0000025,
+    "output_cost_per_token_above_200k_tokens": 0.000005,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
     "mode": "chat",
-    "litellm_provider": "novita"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "novita/qwen/qwen3-embedding-0.6b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 32768,
-    "input_cost_per_token": 7e-8,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "novita"
+  "xai/grok-4.20-multi-agent-beta-0309": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 1000000,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.0000025,
+    "input_cost_per_token_above_200k_tokens": 0.0000025,
+    "output_cost_per_token_above_200k_tokens": 0.000005,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "novita/qwen/qwen3-embedding-8b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 7e-8,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "novita"
+  "xai/grok-4.3": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 1000000,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.0000025,
+    "input_cost_per_token_above_200k_tokens": 0.0000025,
+    "output_cost_per_token_above_200k_tokens": 0.000005,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "novita/baai/bge-m3": {
-    "max_input_tokens": 8192,
-    "max_output_tokens": 96000,
-    "input_cost_per_token": 1e-8,
-    "output_cost_per_token": 1e-8,
-    "mode": "embedding",
-    "litellm_provider": "novita"
+  "xai/grok-4.3-latest": {
+    "max_input_tokens": 1000000,
+    "max_output_tokens": 1000000,
+    "input_cost_per_token": 0.00000125,
+    "output_cost_per_token": 0.0000025,
+    "input_cost_per_token_above_200k_tokens": 0.0000025,
+    "output_cost_per_token_above_200k_tokens": 0.000005,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "novita/qwen/qwen3-reranker-8b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 5e-8,
-    "output_cost_per_token": 5e-8,
-    "mode": "rerank",
-    "litellm_provider": "novita"
+  "xai/grok-4.5": {
+    "max_input_tokens": 500000,
+    "max_output_tokens": 500000,
+    "input_cost_per_token": 0.000002,
+    "output_cost_per_token": 0.000006,
+    "input_cost_per_token_above_200k_tokens": 0.000004,
+    "output_cost_per_token_above_200k_tokens": 0.000012,
+    "cache_read_input_token_cost": 3e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 6e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "novita/baai/bge-reranker-v2-m3": {
-    "max_input_tokens": 8000,
-    "max_output_tokens": 8000,
-    "input_cost_per_token": 1e-8,
-    "output_cost_per_token": 1e-8,
-    "mode": "rerank",
-    "litellm_provider": "novita"
+  "xai/grok-4.5-latest": {
+    "max_input_tokens": 500000,
+    "max_output_tokens": 500000,
+    "input_cost_per_token": 0.000002,
+    "output_cost_per_token": 0.000006,
+    "input_cost_per_token_above_200k_tokens": 0.000004,
+    "output_cost_per_token_above_200k_tokens": 0.000012,
+    "cache_read_input_token_cost": 3e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 6e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
+  },
+  "xai/grok-4.6": {
+    "max_input_tokens": 500000,
+    "max_output_tokens": 500000,
+    "input_cost_per_token": 0.000002,
+    "output_cost_per_token": 0.000006,
+    "input_cost_per_token_above_200k_tokens": 0.000004,
+    "output_cost_per_token_above_200k_tokens": 0.000012,
+    "cache_read_input_token_cost": 5e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 0.000001,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/llama-3.1-8b": {
+  "xai/grok-beta": {
     "max_input_tokens": 131072,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 3e-8,
-    "output_cost_per_token": 5e-8,
+    "max_output_tokens": 131072,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.000015,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/llama-3.2-3b": {
-    "max_input_tokens": 131072,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 4e-8,
-    "output_cost_per_token": 8e-8,
+  "xai/grok-build-0.1": {
+    "max_input_tokens": 256000,
+    "max_output_tokens": 256000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.000002,
+    "input_cost_per_token_above_200k_tokens": 0.000002,
+    "output_cost_per_token_above_200k_tokens": 0.000004,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/mistral-7b-v0.3": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 1e-7,
-    "output_cost_per_token": 1.5e-7,
+  "xai/grok-code-fast": {
+    "max_input_tokens": 256000,
+    "max_output_tokens": 256000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.000002,
+    "input_cost_per_token_above_200k_tokens": 0.000002,
+    "output_cost_per_token_above_200k_tokens": 0.000004,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/qwen3-8b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 4e-8,
-    "output_cost_per_token": 1.4e-7,
+  "xai/grok-code-fast-1": {
+    "max_input_tokens": 256000,
+    "max_output_tokens": 256000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.000002,
+    "input_cost_per_token_above_200k_tokens": 0.000002,
+    "output_cost_per_token_above_200k_tokens": 0.000004,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/dolphin3-8b": {
-    "max_input_tokens": 128000,
+  "xai/grok-code-fast-1-0825": {
+    "max_input_tokens": 256000,
+    "max_output_tokens": 256000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.000002,
+    "input_cost_per_token_above_200k_tokens": 0.000002,
+    "output_cost_per_token_above_200k_tokens": 0.000004,
+    "cache_read_input_token_cost": 2e-7,
+    "cache_read_input_token_cost_above_200k_tokens": 4e-7,
+    "mode": "chat",
+    "litellm_provider": "xai",
+    "supports_vision": true
+  },
+  "xai/grok-vision-beta": {
+    "max_input_tokens": 8192,
     "max_output_tokens": 8192,
-    "input_cost_per_token": 8e-8,
-    "output_cost_per_token": 1.5e-7,
+    "input_cost_per_token": 0.000005,
+    "output_cost_per_token": 0.000015,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "xai",
+    "supports_vision": true
   },
-  "llamagate/deepseek-r1-8b": {
-    "max_input_tokens": 65536,
-    "max_output_tokens": 16384,
-    "input_cost_per_token": 1e-7,
-    "output_cost_per_token": 2e-7,
+  "you_com/search": {
+    "mode": "search",
+    "litellm_provider": "you_com"
+  },
+  "zai.glm-4.7": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000022,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "bedrock_converse"
   },
-  "llamagate/deepseek-r1-7b-qwen": {
-    "max_input_tokens": 131072,
-    "max_output_tokens": 16384,
-    "input_cost_per_token": 8e-8,
-    "output_cost_per_token": 1.5e-7,
+  "zai.glm-4.7-flash": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 7e-8,
+    "output_cost_per_token": 4e-7,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "bedrock_converse"
   },
-  "llamagate/openthinker-7b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 8e-8,
-    "output_cost_per_token": 1.5e-7,
+  "zai.glm-5": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.0000032,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "bedrock_converse"
   },
-  "llamagate/qwen2.5-coder-7b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 6e-8,
-    "output_cost_per_token": 1.2e-7,
+  "zai/glm-4-32b-0414-128k": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 1e-7,
+    "output_cost_per_token": 1e-7,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "zai"
   },
-  "llamagate/deepseek-coder-6.7b": {
-    "max_input_tokens": 16384,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 6e-8,
-    "output_cost_per_token": 1.2e-7,
+  "zai/glm-4.5": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000022,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "zai"
   },
-  "llamagate/codellama-7b": {
-    "max_input_tokens": 16384,
-    "max_output_tokens": 4096,
-    "input_cost_per_token": 6e-8,
-    "output_cost_per_token": 1.2e-7,
+  "zai/glm-4.5-air": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 2e-7,
+    "output_cost_per_token": 0.0000011,
     "mode": "chat",
-    "litellm_provider": "llamagate"
+    "litellm_provider": "zai"
   },
-  "llamagate/qwen3-vl-8b": {
-    "max_input_tokens": 32768,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 1.5e-7,
-    "output_cost_per_token": 5.5e-7,
+  "zai/glm-4.5-airx": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 0.0000011,
+    "output_cost_per_token": 0.0000045,
     "mode": "chat",
-    "litellm_provider": "llamagate",
-    "supports_vision": true
+    "litellm_provider": "zai"
   },
-  "llamagate/llava-7b": {
-    "max_input_tokens": 4096,
-    "max_output_tokens": 2048,
-    "input_cost_per_token": 1e-7,
-    "output_cost_per_token": 2e-7,
+  "zai/glm-4.5-flash": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 0,
+    "output_cost_per_token": 0,
     "mode": "chat",
-    "litellm_provider": "llamagate",
-    "supports_vision": true
+    "litellm_provider": "zai"
   },
-  "llamagate/gemma3-4b": {
+  "zai/glm-4.5-x": {
     "max_input_tokens": 128000,
-    "max_output_tokens": 8192,
-    "input_cost_per_token": 3e-8,
-    "output_cost_per_token": 8e-8,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 0.0000022,
+    "output_cost_per_token": 0.0000089,
     "mode": "chat",
-    "litellm_provider": "llamagate",
+    "litellm_provider": "zai"
+  },
+  "zai/glm-4.5v": {
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32000,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000018,
+    "mode": "chat",
+    "litellm_provider": "zai",
     "supports_vision": true
   },
-  "llamagate/nomic-embed-text": {
-    "max_input_tokens": 8192,
-    "input_cost_per_token": 2e-8,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "llamagate"
+  "zai/glm-4.6": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000022,
+    "cache_creation_input_token_cost": 0,
+    "cache_read_input_token_cost": 1.1e-7,
+    "mode": "chat",
+    "litellm_provider": "zai"
   },
-  "llamagate/qwen3-embedding-8b": {
-    "max_input_tokens": 40960,
-    "input_cost_per_token": 2e-8,
-    "output_cost_per_token": 0,
-    "mode": "embedding",
-    "litellm_provider": "llamagate"
+  "zai/glm-4.7": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 6e-7,
+    "output_cost_per_token": 0.0000022,
+    "cache_creation_input_token_cost": 0,
+    "cache_read_input_token_cost": 1.1e-7,
+    "mode": "chat",
+    "litellm_provider": "zai"
   },
-  "sarvam/sarvam-m": {
-    "max_input_tokens": 8192,
-    "max_output_tokens": 32000,
+  "zai/glm-4.7-flash": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
     "input_cost_per_token": 0,
     "output_cost_per_token": 0,
     "cache_creation_input_token_cost": 0,
     "cache_read_input_token_cost": 0,
     "mode": "chat",
-    "litellm_provider": "sarvam"
+    "litellm_provider": "zai"
+  },
+  "zai/glm-5": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.000001,
+    "output_cost_per_token": 0.0000032,
+    "cache_creation_input_token_cost": 0,
+    "cache_read_input_token_cost": 2e-7,
+    "mode": "chat",
+    "litellm_provider": "zai"
+  },
+  "zai/glm-5-code": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000012,
+    "output_cost_per_token": 0.000005,
+    "cache_creation_input_token_cost": 0,
+    "cache_read_input_token_cost": 3e-7,
+    "mode": "chat",
+    "litellm_provider": "zai"
+  },
+  "zai/glm-5.1": {
+    "max_input_tokens": 200000,
+    "max_output_tokens": 128000,
+    "input_cost_per_token": 0.0000014,
+    "output_cost_per_token": 0.0000044,
+    "cache_creation_input_token_cost": 0,
+    "cache_read_input_token_cost": 2.6e-7,
+    "mode": "chat",
+    "litellm_provider": "zai"
   }
 }
diff --git a/src/common/utils/tokens/updateModelsData.test.ts b/src/common/utils/tokens/updateModelsData.test.ts
new file mode 100644
index 00000000000..8cdad5083bd
--- /dev/null
+++ b/src/common/utils/tokens/updateModelsData.test.ts
@@ -0,0 +1,425 @@
+import { describe, test, expect } from "@jest/globals";
+import { KNOWN_MODELS } from "@/common/constants/knownModels";
+import modelsJson from "./models.json";
+import {
+  findMissingKnownModels,
+  pruneModelData,
+  sanitizePricing,
+  serializeModelData,
+  summarizeCatalog,
+  validateModelData,
+} from "./updateModelsData";
+
+function chatEntries(
+  count: number,
+  overrides: Record<string, unknown> = {},
+  prefix = "provider/model"
+): Record<string, Record<string, unknown>> {
+  const entries: Record<string, Record<string, unknown>> = {};
+  for (let i = 0; i < count; i++) {
+    entries[`${prefix}-${i}`] = {
+      mode: "chat",
+      max_input_tokens: 128000,
+      input_cost_per_token: 0.000001,
+      output_cost_per_token: 0.000002,
+      ...overrides,
+    };
+  }
+  return entries;
+}
+
+// Covers every curated model so only the guard under test can fail validation.
+const curatedCoverage = Object.fromEntries(
+  Object.values(KNOWN_MODELS).map((model) => [
+    model.providerModelId,
+    { mode: "chat", max_input_tokens: 200000 },
+  ])
+);
+
+describe("pruneModelData", () => {
+  test("rejects non-object payloads", () => {
+    expect(() => pruneModelData(null)).toThrow("Expected LiteLLM model metadata object");
+    expect(() => pruneModelData([1, 2])).toThrow("Expected LiteLLM model metadata object");
+  });
+
+  test("keeps only retained fields and drops non-object entries", () => {
+    const pruned = pruneModelData({
+      "provider/model-a": {
+        max_input_tokens: 1000,
+        input_cost_per_token: 0.001,
+        litellm_provider: "provider",
+        source: "https://example.com",
+        deprecation_date: "2027-01-01",
+      },
+      bogus: "not-an-object",
+    });
+
+    expect(pruned["provider/model-a"]).toEqual({
+      max_input_tokens: 1000,
+      input_cost_per_token: 0.001,
+      litellm_provider: "provider",
+    });
+    expect(pruned.bogus).toBeUndefined();
+  });
+});
+
+describe("sanitizePricing", () => {
+  test("drops entries whose present cost fields are unusable", () => {
+    const { catalog, droppedModelIds } = sanitizePricing({
+      good: { input_cost_per_token: 0.001, output_cost_per_token: 0 },
+      // Subscription providers legitimately omit costs entirely.
+      "no-costs": { max_input_tokens: 1000 },
+      negative: { input_cost_per_token: -0.001 },
+      "not-a-number": { output_cost_per_token: "0.001" },
+      infinite: { cache_read_input_token_cost: Number.POSITIVE_INFINITY },
+    });
+
+    expect(Object.keys(catalog).sort()).toEqual(["good", "no-costs"]);
+    expect(droppedModelIds.sort()).toEqual(["infinite", "negative", "not-a-number"]);
+  });
+});
+
+describe("findMissingKnownModels", () => {
+  const usable = { max_input_tokens: 256000 };
+
+  test("reports all curated models against an empty catalog and no overrides", () => {
+    const missing = findMissingKnownModels({}, {});
+    expect(missing.sort()).toEqual(
+      Object.values(KNOWN_MODELS)
+        .map((model) => model.id)
+        .sort()
+    );
+  });
+
+  test("any runtime-resolvable key form satisfies coverage, unrelated keys do not", () => {
+    const grok = Object.values(KNOWN_MODELS).find((model) => model.provider === "xai");
+    if (!grok) throw new Error("expected a curated xai model");
+
+    // Coverage mirrors getModelStats' lookup keys: both the provider-prefixed
+    // form LiteLLM uses for xAI and the bare fallback resolve at runtime.
+    expect(findMissingKnownModels({ [`xai/${grok.providerModelId}`]: usable }, {})).not.toContain(
+      grok.id
+    );
+    expect(findMissingKnownModels({ [grok.providerModelId]: usable }, {})).not.toContain(grok.id);
+    expect(
+      findMissingKnownModels({ [`unrelated/${grok.providerModelId}x`]: usable }, {})
+    ).toContain(grok.id);
+  });
+
+  test("a retained key with unusable token limits still counts as missing", () => {
+    const anthropic = Object.values(KNOWN_MODELS).find((model) => model.provider === "anthropic");
+    if (!anthropic) throw new Error("expected a curated anthropic model");
+
+    // Key presence is not coverage: getModelStats would reject these entries.
+    expect(findMissingKnownModels({ [anthropic.providerModelId]: {} }, {})).toContain(anthropic.id);
+    expect(
+      findMissingKnownModels({ [anthropic.providerModelId]: { max_input_tokens: 0 } }, {})
+    ).toContain(anthropic.id);
+  });
+
+  test("models-extra overrides satisfy models absent upstream", () => {
+    const anthropic = Object.values(KNOWN_MODELS).find((model) => model.provider === "anthropic");
+    if (!anthropic) throw new Error("expected a curated anthropic model");
+
+    expect(findMissingKnownModels({}, { [anthropic.providerModelId]: usable })).not.toContain(
+      anthropic.id
+    );
+  });
+});
+
+describe("validateModelData", () => {
+  test("accepts the current vendored models.json against its own baseline", () => {
+    const vendored = modelsJson as Record<string, Record<string, unknown>>;
+    const sanitized = sanitizePricing(vendored);
+    expect(sanitized.droppedModelIds).toEqual([]);
+    expect(() => validateModelData(sanitized, vendored)).not.toThrow();
+  });
+
+  test("rejects truncated upstream data", () => {
+    expect(() => validateModelData({ catalog: chatEntries(10), droppedModelIds: [] })).toThrow(
+      /only 10 usable chat\/responses models found/
+    );
+  });
+
+  test("rejects a large shrink below the vendored baseline even above the floor", () => {
+    const sanitized = {
+      catalog: { ...chatEntries(600), ...curatedCoverage },
+      droppedModelIds: [],
+    };
+    expect(() => validateModelData(sanitized, chatEntries(2000))).toThrow(
+      /chat usable model count shrank from 2000/
+    );
+    // A small decrease within the bound is acceptable (e.g. upstream pruning).
+    expect(() => validateModelData(sanitized, chatEntries(650))).not.toThrow();
+  });
+
+  test("rejects the loss of one pricing field even when the other survives", () => {
+    // An upstream rename of just output_cost_per_token keeps every entry
+    // input-priced, so aggregated priced coverage would hide the regression.
+    const outputless = chatEntries(700, { output_cost_per_token: undefined }, "prov/outputless");
+    const sanitized = { catalog: { ...outputless, ...curatedCoverage }, droppedModelIds: [] };
+    expect(() => validateModelData(sanitized, chatEntries(700))).toThrow(
+      /chat output_cost_per_token coverage shrank from 700 to 0/
+    );
+  });
+
+  test("rejects pricing loss confined to a smaller mode", () => {
+    // Stripping costs from every responses row barely moves aggregate priced
+    // counts, so each mode compares against its own baseline.
+    const responses = (overrides: Record<string, unknown>) =>
+      chatEntries(60, { mode: "responses", ...overrides }, "prov/responses");
+    const baseline = { ...chatEntries(700), ...responses({}) };
+    const sanitized = {
+      catalog: {
+        ...chatEntries(700),
+        ...responses({ input_cost_per_token: undefined, output_cost_per_token: undefined }),
+        ...curatedCoverage,
+      },
+      droppedModelIds: [],
+    };
+    expect(() => validateModelData(sanitized, baseline)).toThrow(
+      /responses input_cost_per_token coverage shrank from 60 to 0/
+    );
+  });
+
+  test("rejects wholesale omission of a non-mappable mode", () => {
+    // A partial payload that keeps chat/responses but drops image_generation
+    // rows must trip that mode's own entry-count baseline.
+    const baseline = {
+      ...chatEntries(700),
+      ...chatEntries(300, { mode: "image_generation", max_input_tokens: undefined }, "prov/image"),
+    };
+    const sanitized = {
+      catalog: { ...chatEntries(700), ...curatedCoverage },
+      droppedModelIds: [],
+    };
+    expect(() => validateModelData(sanitized, baseline)).toThrow(
+      /image_generation entry count shrank from 300 to 0/
+    );
+  });
+
+  test("rejects removal of a cost field beyond the base input/output pair", () => {
+    // Wiping output_cost_per_image_token leaves the base pair intact; the
+    // runtime would silently fall back to (much lower) generic output rates.
+    const image = (overrides: Record<string, unknown>) =>
+      chatEntries(300, { mode: "image_generation", ...overrides }, "prov/image");
+    const baseline = {
+      ...chatEntries(700),
+      ...image({ output_cost_per_image_token: 0.00002 }),
+    };
+    const sanitized = {
+      catalog: { ...chatEntries(700), ...image({}), ...curatedCoverage },
+      droppedModelIds: [],
+    };
+    expect(() => validateModelData(sanitized, baseline)).toThrow(
+      /image_generation output_cost_per_image_token coverage shrank from 300 to 0/
+    );
+  });
+
+  test("rejects a catalog-wide loss of capability flags", () => {
+    // Runtime treats missing/false capability flags as unsupported, so both a
+    // field removal and a mass flip to false must register as coverage loss.
+    const baseline = chatEntries(700, { supports_pdf_input: true });
+    const flipped = {
+      catalog: { ...chatEntries(700, { supports_pdf_input: false }), ...curatedCoverage },
+      droppedModelIds: [],
+    };
+    const removed = {
+      catalog: { ...chatEntries(700), ...curatedCoverage },
+      droppedModelIds: [],
+    };
+    for (const sanitized of [flipped, removed]) {
+      expect(() => validateModelData(sanitized, baseline)).toThrow(
+        /chat supports_pdf_input coverage shrank from 700 to 0/
+      );
+    }
+  });
+
+  test("rejects a catalog-wide loss of token limits even when pricing survives", () => {
+    // Entries keep mode + pricing but lose max_input_tokens: getModelStats
+    // rejects them, so usable coverage must register the collapse.
+    const limitless = chatEntries(700, { max_input_tokens: undefined }, "prov/limitless");
+    const sanitized = { catalog: { ...limitless, ...curatedCoverage }, droppedModelIds: [] };
+    expect(() => validateModelData(sanitized, chatEntries(700))).toThrow(
+      /chat usable model count shrank from 700/
+    );
+  });
+
+  test("rejects a catalog-wide token-limit magnitude collapse", () => {
+    // max_input_tokens: 1 still passes the usability bar and every coverage
+    // count, so only the magnitude median can catch mass-shrunk context sizes.
+    const tiny = chatEntries(700, { max_input_tokens: 1 }, "prov/tiny");
+    const sanitized = { catalog: { ...tiny, ...curatedCoverage }, droppedModelIds: [] };
+    expect(() => validateModelData(sanitized, chatEntries(700))).toThrow(
+      /max_input_tokens median shifted/
+    );
+  });
+
+  test("summarizes per-mode field coverage and mappable numeric magnitudes", () => {
+    expect(
+      summarizeCatalog({
+        usableChat: {
+          mode: "chat",
+          max_input_tokens: 128000,
+          input_cost_per_token: 0.000001,
+          supports_vision: true,
+        },
+        usableResponses: {
+          mode: "responses",
+          max_input_tokens: 128000,
+          input_cost_per_token: 0.000002,
+          output_cost_per_token: 0.000008,
+        },
+        // supports_vision: false counts as absent (runtime treats it as unsupported).
+        limitlessChat: { mode: "chat", input_cost_per_token: 0.000001, supports_vision: false },
+        embedding: { mode: "embedding", max_input_tokens: 8192, input_cost_per_token: 0.0000001 },
+      })
+    ).toEqual({
+      totalEntries: 4,
+      modes: {
+        chat: {
+          entries: 2,
+          usable: 1,
+          fields: { max_input_tokens: 1, input_cost_per_token: 2, supports_vision: 1 },
+        },
+        responses: {
+          entries: 1,
+          usable: 1,
+          fields: { max_input_tokens: 1, input_cost_per_token: 1, output_cost_per_token: 1 },
+        },
+        embedding: {
+          entries: 1,
+          usable: 1,
+          fields: { max_input_tokens: 1, input_cost_per_token: 1 },
+        },
+      },
+      // Magnitudes span mappable entries only, so the embedding cost is excluded.
+      numericFields: {
+        max_input_tokens: { samples: 2, median: 128000 },
+        input_cost_per_token: { samples: 3, median: 0.000001 },
+        output_cost_per_token: { samples: 1, median: 0.000008 },
+      },
+    });
+  });
+
+  test("rejects a catalog-wide price magnitude collapse", () => {
+    // Every field survives and all counters match, but rates are scaled by
+    // 1e-9; only the median magnitude guard can catch this.
+    const scaled = chatEntries(
+      700,
+      { input_cost_per_token: 0.000001e-9, output_cost_per_token: 0.000002e-9 },
+      "prov/scaled"
+    );
+    const sanitized = { catalog: { ...scaled, ...curatedCoverage }, droppedModelIds: [] };
+    expect(() => validateModelData(sanitized, chatEntries(700))).toThrow(
+      /input_cost_per_token median shifted/
+    );
+  });
+
+  test("rejects a catalog-wide zeroing of prices", () => {
+    // Zero is a valid per-entry price and keeps field coverage intact, but a
+    // catalog-wide zero-out must fail the magnitude check (median collapses to
+    // 0), not slip past it because the ratio guard only ran on positive values.
+    const free = chatEntries(
+      700,
+      { input_cost_per_token: 0, output_cost_per_token: 0 },
+      "prov/free"
+    );
+    const sanitized = { catalog: { ...free, ...curatedCoverage }, droppedModelIds: [] };
+    expect(() => validateModelData(sanitized, chatEntries(700))).toThrow(
+      /input_cost_per_token median shifted from 0.000001 to 0/
+    );
+  });
+
+  test("rejects a targeted single-row price shift that no catalog-wide guard sees", () => {
+    const poison = (overrides: Record<string, unknown>) => {
+      const catalog = { ...chatEntries(700), ...curatedCoverage };
+      catalog["provider/model-3"] = { ...catalog["provider/model-3"], ...overrides };
+      return { catalog, droppedModelIds: [] };
+    };
+    // Scaling (or deleting) one row's price moves no count and no median; only
+    // the per-identity magnitude bound can catch it.
+    expect(() =>
+      validateModelData(poison({ input_cost_per_token: 0.000001e-9 }), chatEntries(700))
+    ).toThrow(/provider:model-3 input_cost_per_token: 0.000001 -> 1e-15/);
+    expect(() =>
+      validateModelData(poison({ input_cost_per_token: undefined }), chatEntries(700))
+    ).toThrow(/provider:model-3 input_cost_per_token: 0.000001 -> absent/);
+    // Ordinary per-row repricing within the factor is accepted.
+    expect(() =>
+      validateModelData(poison({ input_cost_per_token: 0.00001 }), chatEntries(700))
+    ).not.toThrow();
+  });
+
+  test("rejects a poisoned higher-precedence alias shadowing a baseline row", () => {
+    // Adding openai/x on top of a baseline bare x (or moving the row there)
+    // leaves every count and median intact, but runtime lookup prefers the
+    // scoped key; comparison must follow resolved identities, not raw keys.
+    const bare = {
+      mode: "chat",
+      max_input_tokens: 128000,
+      input_cost_per_token: 0.000001,
+      output_cost_per_token: 0.000002,
+      litellm_provider: "acme",
+    };
+    const poisonedAlias = { ...bare, input_cost_per_token: 0.000001e-9 };
+    const baseline = { ...chatEntries(700), "acme-chat-1": bare };
+    for (const catalog of [
+      // Attack 1: new alias added while the original row survives.
+      { ...baseline, "acme/acme-chat-1": poisonedAlias },
+      // Attack 2: original key removed, row substituted under the alias.
+      { ...chatEntries(700), "acme/acme-chat-1": poisonedAlias },
+    ]) {
+      const sanitized = { catalog: { ...catalog, ...curatedCoverage }, droppedModelIds: [] };
+      expect(() => validateModelData(sanitized, baseline)).toThrow(
+        /acme:acme-chat-1 input_cost_per_token: 0.000001 -> 1e-15/
+      );
+    }
+  });
+
+  test("rejects a targeted capability-flag flip on a single row", () => {
+    // One true -> false flip is far inside the aggregate 10% allowance, but
+    // runtime would start rejecting valid attachments for that model.
+    const baseline = chatEntries(700, { supports_pdf_input: true });
+    const catalog = { ...baseline, ...curatedCoverage };
+    catalog["provider/model-3"] = { ...catalog["provider/model-3"], supports_pdf_input: false };
+    expect(() => validateModelData({ catalog, droppedModelIds: [] }, baseline)).toThrow(
+      /provider:model-3 supports_pdf_input: true -> false/
+    );
+  });
+
+  test("accepts runtime-parseable numeric strings as equal magnitudes", () => {
+    // getModelStats parses "128000" like 128000, so a representation change
+    // must not read as a >100x collapse and block the refresh.
+    const catalog = { ...chatEntries(700), ...curatedCoverage };
+    catalog["provider/model-3"] = { ...catalog["provider/model-3"], max_input_tokens: "128000" };
+    expect(() =>
+      validateModelData({ catalog, droppedModelIds: [] }, chatEntries(700))
+    ).not.toThrow();
+  });
+
+  test("rejects data where too many entries had invalid pricing", () => {
+    // 100 dropped out of 700 total (14%) exceeds the 5% abort threshold.
+    const dropped = Array.from({ length: 100 }, (_, i) => `dropped-${i}`);
+    expect(() =>
+      validateModelData({ catalog: chatEntries(600), droppedModelIds: dropped })
+    ).toThrow(/entries have invalid pricing/);
+  });
+
+  test("rejects data missing curated known models", () => {
+    // A large catalog that satisfies the size floor but carries none of the
+    // curated model keys that models-extra does not already cover.
+    expect(() => validateModelData({ catalog: chatEntries(600), droppedModelIds: [] })).toThrow(
+      /curated known models missing from upstream/
+    );
+  });
+});
+
+describe("serializeModelData", () => {
+  test("sorts keys so upstream reordering produces no diff", () => {
+    const forward = serializeModelData({ b: { mode: "chat" }, a: { mode: "chat" } });
+    const reversed = serializeModelData({ a: { mode: "chat" }, b: { mode: "chat" } });
+    expect(forward).toBe(reversed);
+    expect(forward.indexOf('"a"')).toBeLessThan(forward.indexOf('"b"'));
+  });
+});
diff --git a/src/common/utils/tokens/updateModelsData.ts b/src/common/utils/tokens/updateModelsData.ts
new file mode 100644
index 00000000000..ac2bcd275c9
--- /dev/null
+++ b/src/common/utils/tokens/updateModelsData.ts
@@ -0,0 +1,444 @@
+/**
+ * Pure transform/validation logic for refreshing models.json from LiteLLM's
+ * model_prices_and_context_window.json. The fetch/write CLI wrapper lives in
+ * scripts/update_models.ts (run via `make update-models`); the logic lives here
+ * so `bun test src` covers it (#3727).
+ */
+
+import { KNOWN_MODELS } from "@/common/constants/knownModels";
+import { normalizeToCanonical } from "../ai/models";
+import { modelsExtra } from "./models-extra";
+import { generateModelLookupKeys, hasUsableTokenLimits, parseNum } from "./modelStats";
+
+const RETAINED_FIELDS = [
+  "max_input_tokens",
+  "max_output_tokens",
+  "input_cost_per_token",
+  "output_cost_per_token",
+  "output_cost_per_image_token",
+  "input_cost_per_token_above_200k_tokens",
+  "output_cost_per_token_above_200k_tokens",
+  "cache_creation_input_token_cost",
+  "cache_creation_input_token_cost_above_200k_tokens",
+  "cache_read_input_token_cost",
+  "cache_read_input_token_cost_above_200k_tokens",
+  "tiered_pricing_threshold_tokens",
+  "mode",
+  "litellm_provider",
+  "supports_pdf_input",
+  "supports_vision",
+  "supports_audio_input",
+  "supports_video_input",
+  "max_pdf_size_mb",
+] as const;
+
+const COST_FIELDS = [
+  "input_cost_per_token",
+  "output_cost_per_token",
+  "output_cost_per_image_token",
+  "input_cost_per_token_above_200k_tokens",
+  "output_cost_per_token_above_200k_tokens",
+  "cache_creation_input_token_cost",
+  "cache_creation_input_token_cost_above_200k_tokens",
+  "cache_read_input_token_cost",
+  "cache_read_input_token_cost_above_200k_tokens",
+] as const;
+
+// Abort thresholds guarding against truncated or structurally reshaped upstream data.
+// The absolute floor catches degenerate payloads; the relative bounds catch partial
+// responses or field renames that would silently degrade thousands of known models.
+const MIN_USABLE_MAPPABLE_MODELS = 500;
+const MAX_BASELINE_SHRINK_FRACTION = 0.1;
+// Relative checks only apply to baselines with enough entries for the fraction
+// to be signal rather than noise (a 3-entry mode legitimately losing 1 entry
+// must not block refreshes forever).
+const MIN_BASELINE_FOR_SHRINK_CHECK = 20;
+const MAX_INVALID_PRICING_FRACTION = 0.05;
+// A poisoned upstream could keep every field present but rescale values
+// (prices toward zero, token limits toward 1), silently corrupting cost
+// accounting or context handling. Legitimate repricing rarely approaches this
+// factor, so both a catalog-wide median shift and a per-entry value shift
+// beyond it are treated as corruption.
+const MAX_MAGNITUDE_SHIFT_FACTOR = 100;
+
+/** Modes whose metadata applies to chat-style usage; mirrored by the Treat-as catalog. */
+export const MAPPABLE_MODES = new Set(["chat", "responses"]);
+
+/**
+ * Canonical `provider:model` id for a catalog key. LiteLLM keys are either
+ * "provider/model" or a bare model id whose provider lives in litellm_provider;
+ * both forms round-trip through getModelStats' lookup keys. Shared with the
+ * Treat-as catalog so validation and the UI derive identities identically.
+ */
+export function toCanonicalModelId(
+  catalogKey: string,
+  metadata: Record<string, unknown>
+): string | null {
+  const slashIndex = catalogKey.indexOf("/");
+  if (slashIndex > 0) {
+    return `${catalogKey.slice(0, slashIndex)}:${catalogKey.slice(slashIndex + 1)}`;
+  }
+  const provider = metadata.litellm_provider;
+  return typeof provider === "string" && provider.length > 0 ? `${provider}:${catalogKey}` : null;
+}
+
+export type ModelCatalogData = Record<string, Record<string, unknown>>;
+
+export interface SanitizedModelData {
+  catalog: ModelCatalogData;
+  /** Catalog keys dropped because a present cost field was not a finite non-negative number. */
+  droppedModelIds: string[];
+}
+
+export function pruneModelData(data: unknown): ModelCatalogData {
+  if (!data || typeof data !== "object" || Array.isArray(data)) {
+    throw new Error("Expected LiteLLM model metadata object");
+  }
+
+  const pruned: ModelCatalogData = {};
+  for (const [modelId, rawMetadata] of Object.entries(data)) {
+    if (!rawMetadata || typeof rawMetadata !== "object" || Array.isArray(rawMetadata)) {
+      continue;
+    }
+
+    const metadata = rawMetadata as Record<string, unknown>;
+    const retained: Record<string, unknown> = {};
+    // Keep models.json small: Xum only reads pricing, token limits, provider, mode, and media
+    // capability fields, while upstream LiteLLM ships many provider-specific fields we never use.
+    for (const field of RETAINED_FIELDS) {
+      if (metadata[field] !== undefined) {
+        retained[field] = metadata[field];
+      }
+    }
+    pruned[modelId] = retained;
+  }
+
+  return pruned;
+}
+
+function hasValidPricing(metadata: Record<string, unknown>): boolean {
+  return COST_FIELDS.every((field) => {
+    const value = metadata[field];
+    // Absent costs are fine (e.g. subscription providers); present costs must be
+    // usable numbers, otherwise cost tracking would silently misprice the model.
+    return (
+      value === undefined || (typeof value === "number" && Number.isFinite(value) && value >= 0)
+    );
+  });
+}
+
+export function sanitizePricing(pruned: ModelCatalogData): SanitizedModelData {
+  const catalog: ModelCatalogData = {};
+  const droppedModelIds: string[] = [];
+
+  for (const [modelId, metadata] of Object.entries(pruned)) {
+    if (hasValidPricing(metadata)) {
+      catalog[modelId] = metadata;
+    } else {
+      droppedModelIds.push(modelId);
+    }
+  }
+
+  return { catalog, droppedModelIds };
+}
+
+function providesUsableMetadata(entry: unknown): boolean {
+  return (
+    typeof entry === "object" &&
+    entry !== null &&
+    hasUsableTokenLimits(entry as Record<string, unknown>)
+  );
+}
+
+/**
+ * Curated models that would lose token/pricing metadata under the given catalog.
+ * Coverage uses getModelStats' own lookup keys and usability bar, so any key
+ * form the runtime can resolve (bare, provider-scoped, models-extra override)
+ * counts, and an entry that loses its token limits still counts as missing.
+ */
+export function findMissingKnownModels(
+  catalog: ModelCatalogData,
+  extra: Record<string, unknown> = modelsExtra
+): string[] {
+  const missing: string[] = [];
+  for (const model of Object.values(KNOWN_MODELS)) {
+    const covered = generateModelLookupKeys(model.id).some(
+      (key) => providesUsableMetadata(catalog[key]) || providesUsableMetadata(extra[key])
+    );
+    if (!covered) {
+      missing.push(model.id);
+    }
+  }
+  return missing;
+}
+
+export interface ModeCoverage {
+  /** Entries in this mode. */
+  entries: number;
+  /** Entries passing getModelStats' usability bar (usable token limits). */
+  usable: number;
+  /**
+   * Per retained field, entries carrying a consumed value. Every retained
+   * field is read somewhere at runtime (stats, pricing, capabilities) and most
+   * are optional per entry, so coverage is tracked per field and per mode:
+   * an upstream removal or rename of any one field registers as shrinkage
+   * instead of hiding behind counts of the surviving fields.
+   */
+  fields: Record<string, number>;
+}
+
+export interface NumericFieldStats {
+  /** Mappable-mode entries carrying a positive value for the field. */
+  samples: number;
+  /** Median of those positive values (0 when none). */
+  median: number;
+}
+
+export interface CatalogSummary {
+  totalEntries: number;
+  /** Coverage per mode string, spanning every mode (not just mappable ones). */
+  modes: Record<string, ModeCoverage>;
+  /** Magnitude stats per numeric retained field across mappable entries. */
+  numericFields: Record<string, NumericFieldStats>;
+}
+
+function median(values: number[]): number {
+  if (values.length === 0) {
+    return 0;
+  }
+  const sorted = [...values].sort((a, b) => a - b);
+  return sorted[Math.floor(sorted.length / 2)];
+}
+
+// Booleans count only when true: capability flags are consumed as "absent ==
+// unsupported", so a catalog-wide flip to false is equivalent to removal.
+function hasConsumedValue(value: unknown): boolean {
+  if (typeof value === "boolean") {
+    return value;
+  }
+  if (typeof value === "number") {
+    return Number.isFinite(value);
+  }
+  return typeof value === "string" && value.length > 0;
+}
+
+export function summarizeCatalog(catalog: ModelCatalogData): CatalogSummary {
+  const summary: CatalogSummary = { totalEntries: 0, modes: {}, numericFields: {} };
+  const numericSamples = new Map<string, number[]>();
+  for (const metadata of Object.values(catalog)) {
+    summary.totalEntries++;
+    const mode = typeof metadata.mode === "string" ? metadata.mode : "unknown";
+    const coverage = (summary.modes[mode] ??= { entries: 0, usable: 0, fields: {} });
+    coverage.entries++;
+    // Usability applies getModelStats' bar: entries without usable token limits
+    // never resolve, so losing max_input_tokens must register as shrinkage.
+    if (hasUsableTokenLimits(metadata)) {
+      coverage.usable++;
+    }
+    for (const field of RETAINED_FIELDS) {
+      if (field === "mode") {
+        continue; // the bucket key itself; `entries` already counts it
+      }
+      const value = metadata[field];
+      if (hasConsumedValue(value)) {
+        coverage.fields[field] = (coverage.fields[field] ?? 0) + 1;
+      }
+      // parseNum mirrors runtime parsing, so a numeric string like "128000"
+      // contributes its real magnitude instead of reading as a collapse.
+      const numeric = parseNum(value);
+      if (MAPPABLE_MODES.has(mode) && numeric !== null && numeric > 0) {
+        let samples = numericSamples.get(field);
+        if (samples === undefined) {
+          samples = [];
+          numericSamples.set(field, samples);
+        }
+        samples.push(numeric);
+      }
+    }
+  }
+  for (const [field, samples] of numericSamples) {
+    summary.numericFields[field] = { samples: samples.length, median: median(samples) };
+  }
+  return summary;
+}
+
+/**
+ * The entry a catalog would serve for a model id, using getModelStats' own
+ * lookup preference and usability bar. Alias-shadowing corruption is only
+ * visible through this resolution, never through raw key comparison.
+ */
+function resolveCatalogEntry(
+  catalog: ModelCatalogData,
+  modelId: string
+): Record<string, unknown> | undefined {
+  for (const key of generateModelLookupKeys(modelId)) {
+    const entry = catalog[key];
+    if (entry !== undefined && hasUsableTokenLimits(entry)) {
+      return entry;
+    }
+  }
+  return undefined;
+}
+
+function belowBaseline(count: number, baseline: number): boolean {
+  return (
+    baseline >= MIN_BASELINE_FOR_SHRINK_CHECK &&
+    count < Math.ceil(baseline * (1 - MAX_BASELINE_SHRINK_FRACTION))
+  );
+}
+
+/**
+ * Throws when the sanitized upstream data is unfit to replace the vendored
+ * models.json. `baselineCatalog` is the currently vendored catalog (defaults
+ * to empty when unavailable, disabling the relative checks).
+ */
+export function validateModelData(
+  sanitized: SanitizedModelData,
+  baselineCatalog: ModelCatalogData = {}
+): void {
+  const { catalog, droppedModelIds } = sanitized;
+  const errors: string[] = [];
+
+  const summary = summarizeCatalog(catalog);
+  const baseline = summarizeCatalog(baselineCatalog);
+  const usableMappableModels = [...MAPPABLE_MODES].reduce(
+    (count, mode) => count + (summary.modes[mode]?.usable ?? 0),
+    0
+  );
+  if (usableMappableModels < MIN_USABLE_MAPPABLE_MODELS) {
+    errors.push(
+      `only ${usableMappableModels} usable chat/responses models found ` +
+        `(expected at least ${MIN_USABLE_MAPPABLE_MODELS})`
+    );
+  }
+
+  // Fields are optional per entry (e.g. subscription providers omit costs), so
+  // an upstream rename or wholesale omission of any retained field, or of a
+  // whole mode, would sail through the per-entry checks; comparing every mode's
+  // entry/usable counts and every retained field's per-mode coverage against
+  // the vendored baseline keeps a refresh from silently dropping, zero-pricing,
+  // or de-capability-ing whole slices of the catalog.
+  const shrinkChecks: Array<[label: string, count: number, baselineCount: number]> = [
+    ["total entry count", summary.totalEntries, baseline.totalEntries],
+  ];
+  const emptyCoverage: ModeCoverage = { entries: 0, usable: 0, fields: {} };
+  for (const [mode, baselineCoverage] of Object.entries(baseline.modes)) {
+    const coverage = summary.modes[mode] ?? emptyCoverage;
+    shrinkChecks.push(
+      [`${mode} entry count`, coverage.entries, baselineCoverage.entries],
+      [`${mode} usable model count`, coverage.usable, baselineCoverage.usable]
+    );
+    for (const [field, baselineCount] of Object.entries(baselineCoverage.fields)) {
+      shrinkChecks.push([`${mode} ${field} coverage`, coverage.fields[field] ?? 0, baselineCount]);
+    }
+  }
+  // Numeric magnitudes are guarded too, not just field presence: a poisoned
+  // catalog could retain every field while rescaling values (prices toward
+  // zero or absurdly high, token limits toward 1). A collapsed sample count
+  // (e.g. every price zeroed) fails both the sample shrink and the median
+  // check, since a zero median is more than MAX_MEDIAN_SHIFT_FACTOR below any
+  // positive baseline.
+  for (const [field, baselineStats] of Object.entries(baseline.numericFields)) {
+    if (baselineStats.samples < MIN_BASELINE_FOR_SHRINK_CHECK || baselineStats.median <= 0) {
+      continue;
+    }
+    const candidate = summary.numericFields[field] ?? { samples: 0, median: 0 };
+    shrinkChecks.push([`${field} positive-value count`, candidate.samples, baselineStats.samples]);
+    const ratio = candidate.median / baselineStats.median;
+    if (ratio > MAX_MAGNITUDE_SHIFT_FACTOR || ratio < 1 / MAX_MAGNITUDE_SHIFT_FACTOR) {
+      errors.push(
+        `${field} median shifted from ${baselineStats.median} to ${candidate.median} ` +
+          `(more than ${MAX_MAGNITUDE_SHIFT_FACTOR}x from the vendored baseline; possible corruption)`
+      );
+    }
+  }
+  for (const [label, count, baselineCount] of shrinkChecks) {
+    if (belowBaseline(count, baselineCount)) {
+      errors.push(
+        `${label} shrank from ${baselineCount} to ${count} ` +
+          `(more than ${MAX_BASELINE_SHRINK_FRACTION * 100}% below the vendored baseline)`
+      );
+    }
+  }
+
+  // Catalog-wide counts and medians cannot see targeted single-row corruption,
+  // and raw-key comparison cannot see alias shadowing: a poisoned catalog can
+  // add a higher-precedence key (openai/gpt-4o-mini over a baseline
+  // gpt-4o-mini) or move a row to such an alias, leaving every count and
+  // median intact while runtime lookups resolve the poisoned entry. Entries
+  // are therefore compared by runtime identity: for every model id either
+  // catalog can serve, the entry each catalog resolves through getModelStats'
+  // own lookup preference must keep positive numeric fields present and
+  // within the magnitude factor (runtime parseNum semantics, so numeric
+  // strings compare by value) and keep baseline-true capability flags (runtime
+  // treats absent/false as unsupported). Legitimate changes beyond these
+  // bounds fail closed: delete models.json and rerun to deliberately accept a
+  // full refresh.
+  const entryShifts: string[] = [];
+  const identities = new Set<string>();
+  for (const source of [baselineCatalog, catalog]) {
+    for (const [key, metadata] of Object.entries(source)) {
+      identities.add(normalizeToCanonical(toCanonicalModelId(key, metadata) ?? key));
+    }
+  }
+  for (const id of identities) {
+    const baselineEntry = resolveCatalogEntry(baselineCatalog, id);
+    if (baselineEntry === undefined) {
+      continue; // genuinely new identity; nothing vendored to compare against
+    }
+    const entry = resolveCatalogEntry(catalog, id);
+    if (entry === undefined) {
+      continue; // fully removed identities are governed by the coverage baselines
+    }
+    for (const field of RETAINED_FIELDS) {
+      const baselineValue = baselineEntry[field];
+      if (baselineValue === true) {
+        if (entry[field] !== true) {
+          entryShifts.push(`${id} ${field}: true -> ${JSON.stringify(entry[field]) ?? "absent"}`);
+        }
+        continue;
+      }
+      const baselineNum = typeof baselineValue === "boolean" ? null : parseNum(baselineValue);
+      if (baselineNum === null || baselineNum <= 0) {
+        continue;
+      }
+      const candidateNum = parseNum(entry[field]);
+      const ratio = candidateNum === null ? 0 : candidateNum / baselineNum;
+      if (ratio > MAX_MAGNITUDE_SHIFT_FACTOR || ratio < 1 / MAX_MAGNITUDE_SHIFT_FACTOR) {
+        entryShifts.push(`${id} ${field}: ${baselineNum} -> ${candidateNum ?? "absent"}`);
+      }
+    }
+  }
+  if (entryShifts.length > 0) {
+    errors.push(
+      `${entryShifts.length} resolved model identities regressed a retained field beyond the ` +
+        `vendored baseline (e.g. ${entryShifts.slice(0, 5).join("; ")})`
+    );
+  }
+
+  const total = Object.keys(catalog).length + droppedModelIds.length;
+  if (total > 0 && droppedModelIds.length / total > MAX_INVALID_PRICING_FRACTION) {
+    errors.push(
+      `${droppedModelIds.length}/${total} entries have invalid pricing ` +
+        `(e.g. ${droppedModelIds.slice(0, 5).join(", ")})`
+    );
+  }
+
+  const missingKnownModels = findMissingKnownModels(catalog);
+  if (missingKnownModels.length > 0) {
+    errors.push(`curated known models missing from upstream: ${missingKnownModels.join(", ")}`);
+  }
+
+  if (errors.length > 0) {
+    throw new Error(`Refusing to update models.json:\n- ${errors.join("\n- ")}`);
+  }
+}
+
+export function serializeModelData(catalog: ModelCatalogData): string {
+  // Sort keys so upstream object reordering never shows up as a diff (the CLI
+  // and the scheduled workflow treat byte-identical output as "no update").
+  const sorted = Object.fromEntries(
+    Object.entries(catalog).sort(([a], [b]) => (a < b ? -1 : a > b ? 1 : 0))
+  );
+  return `${JSON.stringify(sorted, null, 2)}\n`;
+}