diff --git a/tests/e2e/README.md b/tests/e2e/README.md
index 3876d106..e4d6d37f 100644
--- a/tests/e2e/README.md
+++ b/tests/e2e/README.md
@@ -61,9 +61,19 @@ The e2e test suite includes:
 - Testing the beta responses endpoint
 - Note: This endpoint is in alpha/beta and may require updates
 
+### Embeddings Tests (`embeddings.test.ts`)
+- **Embeddings generation:**
+  - Generating embeddings for single text input
+  - Generating embeddings for multiple text inputs (batch processing)
+  - Verifying consistent embedding dimensions
+  - Handling edge cases (empty strings)
+  - Model information validation
+  - Support for both array and base64 encoded embeddings
+
 ## Notes
 
 - Tests make real API calls to OpenRouter, so you need a valid API key
 - Tests may consume API credits
-- Some tests use the `openai/gpt-3.5-turbo` model by default
+- Chat tests use the `meta-llama/llama-3.2-1b-instruct` model by default
+- Embeddings tests use the `openai/text-embedding-3-small` model by default
 - The beta responses endpoint has limited test coverage as it's still in development
diff --git a/tests/e2e/embeddings.test.ts b/tests/e2e/embeddings.test.ts
new file mode 100644
index 00000000..6b09be4d
--- /dev/null
+++ b/tests/e2e/embeddings.test.ts
@@ -0,0 +1,158 @@
+import { beforeAll, describe, expect, it } from "vitest";
+import { OpenRouter } from "../../src/sdk/sdk.js";
+
+describe("Embeddings E2E Tests", () => {
+  let client: OpenRouter;
+
+  beforeAll(() => {
+    const apiKey = process.env.OPENROUTER_API_KEY;
+    if (!apiKey) {
+      throw new Error(
+        "OPENROUTER_API_KEY environment variable is required for e2e tests"
+      );
+    }
+
+    client = new OpenRouter({
+      apiKey,
+    });
+  });
+
+  describe("embeddings.generate()", () => {
+    it("should successfully generate embeddings for a single text input", async () => {
+      const response = await client.embeddings.generate({
+        input: "The quick brown fox jumps over the lazy dog",
+        model: "openai/text-embedding-3-small",
+      });
+
+      expect(response).toBeDefined();
+
+      // Check if response is an object (not a string)
+      if (typeof response === "object") {
+        expect(response.data).toBeDefined();
+        expect(Array.isArray(response.data)).toBe(true);
+        expect(response.data.length).toBeGreaterThan(0);
+
+        const firstEmbedding = response.data[0];
+        expect(firstEmbedding).toBeDefined();
+        expect(firstEmbedding?.embedding).toBeDefined();
+
+        // Handle both array and base64 string embeddings
+        if (Array.isArray(firstEmbedding?.embedding)) {
+          expect(firstEmbedding.embedding.length).toBeGreaterThan(0);
+          // Verify embedding values are numbers
+          const firstValue = firstEmbedding.embedding[0];
+          expect(typeof firstValue).toBe("number");
+        } else {
+          expect(typeof firstEmbedding?.embedding).toBe("string");
+        }
+
+        // Verify usage information if available
+        if (response.usage) {
+          expect(response.usage.totalTokens).toBeGreaterThan(0);
+        }
+      }
+    });
+
+    it("should generate embeddings for multiple text inputs", async () => {
+      const inputs = [
+        "Hello, world!",
+        "OpenRouter is amazing",
+        "Embeddings are vector representations of text",
+      ];
+
+      const response = await client.embeddings.generate({
+        input: inputs,
+        model: "openai/text-embedding-3-small",
+      });
+
+      expect(response).toBeDefined();
+
+      if (typeof response === "object") {
+        expect(response.data).toBeDefined();
+        expect(Array.isArray(response.data)).toBe(true);
+        expect(response.data.length).toBe(inputs.length);
+
+        // Verify each embedding
+        response.data.forEach((embedding, index) => {
+          expect(embedding).toBeDefined();
+          expect(embedding?.embedding).toBeDefined();
+
+          if (Array.isArray(embedding?.embedding)) {
+            expect(embedding.embedding.length).toBeGreaterThan(0);
+          } else {
+            expect(typeof embedding?.embedding).toBe("string");
+          }
+
+          expect(embedding?.index).toBe(index);
+        });
+      }
+    });
+
+    it("should generate consistent embedding dimensions", async () => {
+      const response = await client.embeddings.generate({
+        input: ["First text", "Second text"],
+        model: "openai/text-embedding-3-small",
+      });
+
+      expect(response).toBeDefined();
+
+      if (typeof response === "object") {
+        expect(response.data.length).toBe(2);
+
+        const firstEmbedding = response.data[0]?.embedding;
+        const secondEmbedding = response.data[1]?.embedding;
+
+        // Only check dimensions if both are arrays
+        if (Array.isArray(firstEmbedding) && Array.isArray(secondEmbedding)) {
+          const firstDimension = firstEmbedding.length;
+          const secondDimension = secondEmbedding.length;
+
+          expect(firstDimension).toBe(secondDimension);
+          expect(firstDimension).toBeGreaterThan(0);
+        }
+      }
+    });
+
+    it("should handle empty string input gracefully", async () => {
+      const response = await client.embeddings.generate({
+        input: "",
+        model: "openai/text-embedding-3-small",
+      });
+
+      expect(response).toBeDefined();
+
+      if (typeof response === "object") {
+        expect(response.data).toBeDefined();
+        expect(Array.isArray(response.data)).toBe(true);
+
+        if (response.data.length > 0) {
+          const embedding = response.data[0];
+          expect(embedding?.embedding).toBeDefined();
+        }
+      }
+    });
+
+    it("should include model information in response", async () => {
+      const modelName = "openai/text-embedding-3-small";
+      const response = await client.embeddings.generate({
+        input: "Test input for model verification",
+        model: modelName,
+      });
+
+      expect(response).toBeDefined();
+
+      if (typeof response === "object") {
+        expect(response.model).toBeDefined();
+        expect(typeof response.model).toBe("string");
+
+        if (response.usage) {
+          expect(response.usage.promptTokens).toBeDefined();
+          expect(response.usage.totalTokens).toBeDefined();
+          expect(typeof response.usage.promptTokens).toBe("number");
+          expect(typeof response.usage.totalTokens).toBe("number");
+          expect(response.usage.totalTokens).toBeGreaterThan(0);
+        }
+      }
+    });
+  });
+});