Rename to v1_models and use with openai as well

server/lib/aiCapabilities.ts

@@ -135,8 +135,8 @@ export const AI_CAPABILITY_DEFS: Record<AiCapability, AiCapabilityDefinition> =
                 joinUpstreamUrl(base, pathFromRequest(req)),
             isStreaming: isBodyOrSseStreaming
         },
-        anthropic_models: {
-            id: "anthropic_models",
+        v1_models: {
+            id: "v1_models",
             protocolFamily: "anthropic",
             routes: [
                 { method: "GET", path: "/v1/models" },

server/lib/aiMessageNormalization.ts

@@ -472,7 +472,7 @@ const REQUEST_NORMALIZERS: Record<
     openai_responses: normalizeOpenAiResponsesRequest,
     anthropic_messages: normalizeAnthropicRequest,
     // Model discovery carries no transcript to normalize.
-    anthropic_models: () => null,
+    v1_models: () => null,
     gemini_generate_content: normalizeGeminiRequest,
     google_generate_content: normalizeGeminiRequest,
     google_raw_predict: normalizeBestEffortRequest,
@@ -487,7 +487,7 @@ const RESPONSE_NORMALIZERS: Record<
     openai_chat: normalizeOpenAiChatResponse,
     openai_responses: normalizeOpenAiResponsesResponse,
     anthropic_messages: normalizeAnthropicResponse,
-    anthropic_models: () => null,
+    v1_models: () => null,
     gemini_generate_content: normalizeGeminiResponse,
     google_generate_content: normalizeGeminiResponse,
     google_raw_predict: normalizeGoogleRawPredictResponse,

server/lib/aiUsageExtraction.ts

@@ -336,7 +336,7 @@ const EXTRACTORS: Record<
     openai_responses: extractOpenAiResponses,
     anthropic_messages: extractAnthropicMessages,
     // Model discovery never runs a model, so there are no tokens to bill.
-    anthropic_models: () => null,
+    v1_models: () => null,
     gemini_generate_content: extractGoogleGenerateContent,
     google_generate_content: extractGoogleGenerateContent,
     // rawPredict is a passthrough to whatever the underlying publisher

server/routers/aiGateway/createAiGatewayRouter.ts

@@ -4,7 +4,7 @@ import {
     type AiCapability
 } from "@server/lib/aiCapabilities";
 import { handleAiGatewayProxy } from "@server/routers/aiGateway/pipeline";
-import { handleAnthropicModels } from "@server/routers/aiGateway/anthropicModels";
+import { handleV1Models } from "@server/routers/aiGateway";
 
 type CapabilityHandler = (
     req: Request,
@@ -15,7 +15,7 @@ type CapabilityHandler = (
 // Capabilities the gateway answers itself instead of proxying upstream.
 // Everything else goes through the inference pipeline.
 const LOCAL_HANDLERS: Partial<Record<AiCapability, CapabilityHandler>> = {
-    anthropic_models: handleAnthropicModels
+    v1_models: handleV1Models
 };
 
 export function createAiGatewayRouter() {

server/routers/aiGateway/index.ts

@@ -1,3 +1,3 @@
 export { handleAiGatewayProxy } from "./pipeline";
-export { handleAnthropicModels } from "./anthropicModels";
+export { handleV1Models } from "./v1Models";
 export { createAiGatewayRouter } from "./createAiGatewayRouter";

server/routers/aiGateway/v1Models.ts

@@ -35,7 +35,7 @@ import {
 import logger from "@server/logger";
 import HttpCode from "@server/types/HttpCode";
 
-const CAPABILITY: AiCapability = "anthropic_models";
+const CAPABILITY: AiCapability = "v1_models";
 
 const querySchema = z.object({
     limit: z.coerce.number().int().min(1).max(MODEL_PAGE_MAX_LIMIT).optional(),
@@ -163,7 +163,7 @@ function buildDiscoveryProviders(
  * list at all or would expose models the resource's allow/block lists forbid,
  * so the response is built from the same effective lists that gate inference.
  */
-export async function handleAnthropicModels(
+export async function handleV1Models(
     req: Request,
     res: Response
 ): Promise<any> {