diff --git a/gitnexus/src/core/embeddings/http-client.ts b/gitnexus/src/core/embeddings/http-client.ts
index 7919b2376..f5f0443dd 100644
--- a/gitnexus/src/core/embeddings/http-client.ts
+++ b/gitnexus/src/core/embeddings/http-client.ts
@@ -364,11 +364,10 @@ const countMismatchMessage = (
  * @param model - Model name for the request body
  * @param apiKey - Bearer token (only used in Authorization header)
  * @param batchIndex - Logical batch number (for error context)
- * @param dimensions - Optional output-vector size. When provided, sent as
- *   the `dimensions` field in the request body. Endpoints that implement
- *   Matryoshka truncation (OpenAI text-embedding-3-*, Cohere embed-v3,
- *   Voyage) return a truncated vector at that size; endpoints that do not
- *   recognise the field may ignore it or return 400. Set
+ * @param dimensions - Optional output-vector size. Voyage uses its
+ *   `output_dimension` field; other OpenAI-compatible endpoints use
+ *   `dimensions`. Endpoints that do not recognise the field may ignore it or
+ *   return 400. Set
  *   `GITNEXUS_EMBEDDING_REQUEST_DIMS=omit` for strict backends while keeping
  *   `GITNEXUS_EMBEDDING_DIMS` set to the returned vector size.
  */
@@ -385,12 +384,27 @@ const httpEmbedBatch = async (
   minIntervalMs = 0,
   timeoutMs = DEFAULT_HTTP_TIMEOUT_MS,
 ): Promise<EmbeddingItem[]> => {
-  const requestBody: { input: string[]; model: string; dimensions?: number } = {
+  const requestBody: {
+    input: string[];
+    model: string;
+    dimensions?: number;
+    output_dimension?: number;
+  } = {
     input: batch,
     model,
   };
   if (dimensions !== undefined) {
-    requestBody.dimensions = dimensions;
+    let hostname = '';
+    try {
+      hostname = new URL(url).hostname.toLowerCase().replace(/\.$/, '');
+    } catch {
+      // Fetch below owns malformed-URL reporting.
+    }
+    if (hostname === 'voyageai.com' || hostname.endsWith('.voyageai.com')) {
+      requestBody.output_dimension = dimensions;
+    } else {
+      requestBody.dimensions = dimensions;
+    }
   }
 
   // Built on demand, not up front. Both describe faults, so in a healthy run —
diff --git a/gitnexus/test/unit/http-embedder.test.ts b/gitnexus/test/unit/http-embedder.test.ts
index 397481569..9cded176d 100644
--- a/gitnexus/test/unit/http-embedder.test.ts
+++ b/gitnexus/test/unit/http-embedder.test.ts
@@ -168,6 +168,56 @@ describe('HTTP embedding backend', () => {
       expect(result.length).toBe(1024);
     });
 
+    it('uses output_dimension for Voyage document batches', async () => {
+      process.env.GITNEXUS_EMBEDDING_URL = 'https://api.voyageai.com/v1';
+      process.env.GITNEXUS_EMBEDDING_MODEL = 'voyage-code-3';
+      process.env.GITNEXUS_EMBEDDING_API_KEY = 'test-key';
+      process.env.GITNEXUS_EMBEDDING_DIMS = '2048';
+
+      const vec2048 = Array.from({ length: 2048 }, (_, i) => i / 2048);
+      vi.stubGlobal(
+        'fetch',
+        vi.fn().mockResolvedValue({
+          ok: true,
+          json: async () => ({ data: [{ embedding: vec2048 }] }),
+        }),
+      );
+
+      const { embedText } = await import('../../src/core/embeddings/embedder.js');
+      const result = await embedText('test text');
+
+      const body = JSON.parse((fetch as any).mock.calls[0][1].body);
+      expect(body).toMatchObject({
+        input: ['test text'],
+        model: 'voyage-code-3',
+        output_dimension: 2048,
+      });
+      expect(body.dimensions).toBeUndefined();
+      expect(result.length).toBe(2048);
+    });
+
+    it('does not treat a voyageai.com lookalike host as Voyage', async () => {
+      process.env.GITNEXUS_EMBEDDING_URL = 'https://voyageai.com.example/v1';
+      process.env.GITNEXUS_EMBEDDING_MODEL = 'test-model';
+      process.env.GITNEXUS_EMBEDDING_DIMS = '512';
+
+      const vec512 = Array.from({ length: 512 }, (_, i) => i / 512);
+      vi.stubGlobal(
+        'fetch',
+        vi.fn().mockResolvedValue({
+          ok: true,
+          json: async () => ({ data: [{ embedding: vec512 }] }),
+        }),
+      );
+
+      const { embedText } = await import('../../src/core/embeddings/embedder.js');
+      await embedText('test text');
+
+      const body = JSON.parse((fetch as any).mock.calls[0][1].body);
+      expect(body.dimensions).toBe(512);
+      expect(body.output_dimension).toBeUndefined();
+    });
+
     it('can validate custom dims without forwarding dimensions to strict backends', async () => {
       process.env.GITNEXUS_EMBEDDING_URL = 'http://test:8080/v1';
       process.env.GITNEXUS_EMBEDDING_MODEL = 'bge-m3';
@@ -214,6 +264,30 @@ describe('HTTP embedding backend', () => {
       expect(result.length).toBe(512);
     });
 
+    it('uses output_dimension for Voyage on the single-query path', async () => {
+      process.env.GITNEXUS_EMBEDDING_URL = 'https://voyageai.com/v1';
+      process.env.GITNEXUS_EMBEDDING_MODEL = 'voyage-code-3';
+      process.env.GITNEXUS_EMBEDDING_API_KEY = 'test-key';
+      process.env.GITNEXUS_EMBEDDING_DIMS = '2048';
+
+      const vec2048 = Array.from({ length: 2048 }, (_, i) => i / 2048);
+      vi.stubGlobal(
+        'fetch',
+        vi.fn().mockResolvedValue({
+          ok: true,
+          json: async () => ({ data: [{ embedding: vec2048 }] }),
+        }),
+      );
+
+      const mod = await import('../../src/mcp/core/embedder.js');
+      const result = await mod.embedQuery('query text');
+
+      const body = JSON.parse((fetch as any).mock.calls[0][1].body);
+      expect(body.output_dimension).toBe(2048);
+      expect(body.dimensions).toBeUndefined();
+      expect(result.length).toBe(2048);
+    });
+
     it('can omit dimensions on the single-query path while validating custom dims', async () => {
       process.env.GITNEXUS_EMBEDDING_URL = 'http://test:8080/v1';
       process.env.GITNEXUS_EMBEDDING_MODEL = 'bge-m3';