diff --git a/tests/e2e/test_model_alias_routing.py b/tests/e2e/test_model_alias_routing.py
index c285bda8..5b1d3719 100644
--- a/tests/e2e/test_model_alias_routing.py
+++ b/tests/e2e/test_model_alias_routing.py
@@ -634,3 +634,150 @@ def test_anthropic_client_streaming_with_bedrock():
     # Verify final message structure
     assert final_message is not None
     assert final_message.content and len(final_message.content) > 0
+
+
+def test_openai_client_streaming_with_bedrock():
+    """Test OpenAI client using 'coding-model' alias (maps to Bedrock) with streaming"""
+    logger.info(
+        "Testing OpenAI client with 'coding-model' alias -> Bedrock (streaming)"
+    )
+
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(
+        api_key="test-key",
+        base_url=f"{base_url}/v1",
+    )
+
+    stream = client.chat.completions.create(
+        model="coding-model",  # This should resolve to us.amazon.nova-premier-v1:0
+        max_tokens=500,
+        messages=[
+            {
+                "role": "user",
+                "content": "Write a short 4-line sonnet about coding.",
+            }
+        ],
+        stream=True,
+    )
+
+    content_chunks = []
+    for chunk in stream:
+        if chunk.choices and len(chunk.choices) > 0:
+            delta = chunk.choices[0].delta
+            if delta.content:
+                content_chunks.append(delta.content)
+
+    full_content = "".join(content_chunks)
+    logger.info(f"Streaming response from coding-model: {full_content}")
+
+    # Should get a text response
+    assert len(full_content) > 0, "Expected text response from streaming"
+
+
+def test_openai_client_streaming_with_bedrock_and_tools():
+    """Test OpenAI client using 'coding-model' alias (maps to Bedrock) with streaming and tools"""
+    logger.info(
+        "Testing OpenAI client with 'coding-model' alias -> Bedrock with tools (streaming)"
+    )
+
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(
+        api_key="test-key",
+        base_url=f"{base_url}/v1",
+    )
+
+    stream = client.chat.completions.create(
+        model="coding-model",  # This should resolve to us.amazon.nova-premier-v1:0
+        max_tokens=1000,
+        messages=[
+            {
+                "role": "user",
+                "content": "I need to write a Python function that calculates the factorial of a number. Can you help me write and run it?. You should use the tool to run the code.",
+            }
+        ],
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "run_python_code",
+                    "description": "Execute Python code and return the result",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "code": {
+                                "type": "string",
+                                "description": "Python code to execute",
+                            }
+                        },
+                        "required": ["code"],
+                    },
+                },
+            }
+        ],
+        tool_choice="auto",
+        stream=True,
+    )
+
+    content_chunks = []
+    tool_calls = []
+    chunk_count = 0
+
+    for chunk in stream:
+        chunk_count += 1
+        if chunk.choices and len(chunk.choices) > 0:
+            delta = chunk.choices[0].delta
+
+            # Log what we see in each chunk
+            has_content = delta.content is not None
+            has_tool_calls = delta.tool_calls is not None
+
+            if (
+                chunk_count % 50 == 0 or has_tool_calls
+            ):  # Log every 50th chunk or any chunk with tool calls
+                logger.info(
+                    f"Chunk {chunk_count}: content={has_content}, tool_calls={has_tool_calls}"
+                )
+                if has_tool_calls:
+                    logger.info(f"  Tool calls in chunk: {delta.tool_calls}")
+
+            # Collect text content
+            if delta.content:
+                content_chunks.append(delta.content)
+
+            # Collect tool calls
+            if delta.tool_calls:
+                for tool_call in delta.tool_calls:
+                    # Extend or create tool call entries
+                    while len(tool_calls) <= tool_call.index:
+                        tool_calls.append(
+                            {
+                                "id": "",
+                                "type": "function",
+                                "function": {"name": "", "arguments": ""},
+                            }
+                        )
+
+                    if tool_call.id:
+                        tool_calls[tool_call.index]["id"] = tool_call.id
+                    if tool_call.function:
+                        if tool_call.function.name:
+                            tool_calls[tool_call.index]["function"][
+                                "name"
+                            ] = tool_call.function.name
+                        if tool_call.function.arguments:
+                            tool_calls[tool_call.index]["function"][
+                                "arguments"
+                            ] += tool_call.function.arguments
+
+    full_content = "".join(content_chunks)
+    logger.info(f"Streaming response from coding-model with tools: {full_content}")
+    logger.info(f"Tool calls collected: {len(tool_calls)}")
+
+    if tool_calls:
+        for i, tc in enumerate(tool_calls):
+            logger.info(f"  Tool call {i}: {tc['function']['name']}")
+
+    # Should get either text response or tool calls for coding assistance
+    assert (
+        full_content or len(tool_calls) > 0
+    ), f"Expected text or tool calls. Got text_len={len(full_content)}, tools={len(tool_calls)}"