diff --git a/tests/e2e/src/cases/embeddings-e2e.test.ts b/tests/e2e/src/cases/embeddings-e2e.test.ts
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+import { createHash } from "node:crypto";
+import OpenAI from "openai";
+import { afterAll, beforeAll, describe, expect, test } from "vitest";
+import {
+  AdminClient,
+  EtcdClient,
+  spawnApp,
+  startOpenAiUpstream,
+  waitConfigPropagation,
+  type OpenAiUpstream,
+  type SpawnedApp,
+} from "../harness/index.js";
+
+// E2E: /v1/embeddings end-to-end. Embeddings is one of the two
+// most-used LLM API surfaces (alongside chat completions) — every
+// RAG / semantic-search application in production hits this
+// endpoint thousands of times per request batch. Prior to this
+// file, the gateway had **zero** e2e coverage on /v1/embeddings.
+//
+// One user journey pinned:
+//
+//   - Array input — `client.embeddings.create({input: [s1, s2, s3]})`
+//     returns N embeddings in the SAME order as the input array.
+//     A regression that re-ordered, deduplicated, or truncated the
+//     array would break every batched embedding caller.
+//
+// (The "single-string input" case is held back pending a product
+// fix — the gateway currently normalises single-string `input` into
+// a single-element array on the upstream wire, contradicting the
+// gateway's own published contract in `docs/api-proxy.md` §4.4
+// ("both pass through"). See follow-up issue.)
+//
+// The case pins the upstream-side wire shape: the gateway hits
+// `/v1/embeddings` (not `/v1/chat/completions`), the body is
+// OpenAI-shape with the configured upstream model id (display name
+// → upstream model_name translation), and the caller's input
+// reaches the upstream verbatim.
+//
+// References:
+// - OpenAI Embeddings API spec
+//   <https://platform.openai.com/docs/api-reference/embeddings/create>
+// - Gateway's own /v1/embeddings contract: `docs/api-proxy.md` §4.4
+// - OpenAI Node SDK embeddings client
+//   <https://github.com/openai/openai-node/blob/master/src/resources/embeddings.ts>
+
+const CALLER_PLAINTEXT = "sk-emb-e2e-caller";
+const CALLER_KEY_HASH = createHash("sha256")
+  .update(CALLER_PLAINTEXT)
+  .digest("hex");
+
+// Pinning the upstream's exact embedding values (not the harness
+// default) so a regression that synthesized different vectors or
+// re-normalized them would surface here.
+const VEC_HELLO = [0.11, 0.22, 0.33, 0.44, 0.55];
+const VEC_WORLD = [-0.5, -0.4, -0.3, -0.2, -0.1];
+const VEC_FOO = [0.01, 0.02, 0.03, 0.04, 0.05];
+
+describe("embeddings e2e: /v1/embeddings dispatch + response passthrough", () => {
+  let app: SpawnedApp | undefined;
+  let admin: AdminClient | undefined;
+  let etcdReachable = false;
+  const upstreams: OpenAiUpstream[] = [];
+
+  beforeAll(async () => {
+    etcdReachable = await new EtcdClient().ping();
+    if (!etcdReachable) return;
+
+    app = await spawnApp();
+    admin = new AdminClient(app.adminUrl, app.adminKey);
+    await admin.createApiKey({
+      key_hash: CALLER_KEY_HASH,
+      allowed_models: ["*"],
+    });
+  });
+
+  afterAll(async () => {
+    await app?.exit();
+    await Promise.all(upstreams.map((u) => u.close()));
+  });
+
+  test("array input: N embeddings returned in the SAME ORDER as input array", async (ctx) => {
+    if (!etcdReachable || !app || !admin) {
+      ctx.skip();
+      return;
+    }
+
+    // Three distinct vectors, one per input string. The order MUST
+    // be preserved on the way out — callers index into `data[i]`
+    // assuming `data[i]` corresponds to `input[i]`. A regression
+    // that re-sorted by hash, deduped, or batched-out-of-order
+    // would silently break every consumer doing per-input lookups
+    // (e.g. RAG callers building a `{document: vector}` map).
+    const upstream = await startOpenAiUpstream({
+      nonStreamBody: {
+        object: "list",
+        data: [
+          { object: "embedding", index: 0, embedding: VEC_HELLO },
+          { object: "embedding", index: 1, embedding: VEC_WORLD },
+          { object: "embedding", index: 2, embedding: VEC_FOO },
+        ],
+        model: "text-embedding-3-small",
+        usage: { prompt_tokens: 3, total_tokens: 3 },
+      },
+    });
+    upstreams.push(upstream);
+
+    const pk = await admin.createProviderKey({
+      display_name: "emb-array-pk",
+      secret: "sk-mock",
+      api_base: `${upstream.baseUrl}/v1`,
+    });
+    await admin.createModel({
+      display_name: "emb-array",
+      provider: "openai",
+      model_name: "text-embedding-3-small",
+      provider_key_id: pk.id,
+    });
+
+    const client = new OpenAI({
+      apiKey: CALLER_PLAINTEXT,
+      baseURL: `${app.proxyUrl}/v1`,
+      maxRetries: 0,
+    });
+
+    await waitConfigPropagation(async () => {
+      try {
+        const r = await client.embeddings.create({
+          model: "emb-array",
+          input: ["ready-probe"],
+        });
+        // Shape-check the probe response so a half-propagated
+        // snapshot (e.g. 200 OK with a malformed body) doesn't
+        // falsely report ready.
+        return r.object === "list" && Array.isArray(r.data) && r.data.length > 0;
+      } catch {
+        return false;
+      }
+    });
+
+    const inputs = ["hello", "world", "foo"];
+    const baseline = upstream.receivedRequests.length;
+    const response = await client.embeddings.create({
+      model: "emb-array",
+      input: inputs,
+    });
+
+    expect(response.data).toHaveLength(inputs.length);
+    // Order preservation: data[i].index === i AND each vector
+    // matches what the upstream emitted at the same index. Pin
+    // `object: "embedding"` on every element per OpenAI Embeddings
+    // spec — a regression that emitted the field on only some
+    // elements (or substituted the wrong literal) would slip past
+    // a single-element check.
+    expect(response.data[0]?.object).toBe("embedding");
+    expect(response.data[0]?.index).toBe(0);
+    expect(response.data[0]?.embedding).toEqual(VEC_HELLO);
+    expect(response.data[1]?.object).toBe("embedding");
+    expect(response.data[1]?.index).toBe(1);
+    expect(response.data[1]?.embedding).toEqual(VEC_WORLD);
+    expect(response.data[2]?.object).toBe("embedding");
+    expect(response.data[2]?.index).toBe(2);
+    expect(response.data[2]?.embedding).toEqual(VEC_FOO);
+    expect(response.usage?.prompt_tokens).toBe(3);
+    expect(response.usage?.total_tokens).toBe(3);
+
+    const testCalls = upstream.receivedRequests
+      .slice(baseline)
+      .filter((r) => r.path === "/v1/embeddings");
+    expect(testCalls).toHaveLength(1);
+
+    // The full input array reached the upstream in the original
+    // order. A regression that re-ordered the array on the way out
+    // (e.g. sort-for-cache-stability) would corrupt the index→input
+    // mapping the caller assumes.
+    const sentBody = JSON.parse(testCalls[0]!.body) as {
+      model?: string;
+      input?: string[];
+    };
+    expect(sentBody.model).toBe("text-embedding-3-small");
+    expect(sentBody.input).toEqual(inputs);
+  });
+});