[Feat] Add OpenAI compatible bedrock imported models. - qwen etc

[ishaan-jaff]

[Feat] Add OpenAI compatible bedrock imported models. - qwen etc

This PR adds support for calling Bedrock imported models that follow the OpenAI Chat Completions API spec. This is useful for models like Qwen 2.5 VL that are imported into Bedrock and accept OpenAI-formatted messages directly.

OpenAI-Compatible Imported Models (Qwen 2.5 VL, etc.)

Use this route for Bedrock imported models that follow the OpenAI Chat Completions API spec. This includes models like Qwen 2.5 VL that accept OpenAI-formatted messages with support for vision (images), tool calling, and other OpenAI features.

Property	Details
Provider Route	bedrock/openai/{model_arn}
Provider Documentation	Bedrock Imported Models
Supported Features	Vision (images), tool calling, streaming, system messages

LiteLLMSDK Usage

Basic Usage

from litellm import completion

response = completion(
    model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z",  # bedrock/openai/{your-model-arn}
    messages=[{"role": "user", "content": "Tell me a joke"}],
    max_tokens=300,
    temperature=0.5
)

With Vision (Images)

import base64
from litellm import completion

# Load and encode image
with open("image.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode("utf-8")

response = completion(
    model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant that can analyze images."
        },
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}
                }
            ]
        }
    ],
    max_tokens=300,
    temperature=0.5
)

Comparing Multiple Images

import base64
from litellm import completion

# Load images
with open("image1.jpg", "rb") as f:
    image1_base64 = base64.b64encode(f.read()).decode("utf-8")
with open("image2.jpg", "rb") as f:
    image2_base64 = base64.b64encode(f.read()).decode("utf-8")

response = completion(
    model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant that can analyze images."
        },
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Spot the difference between these two images?"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image1_base64}"}
                },
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image2_base64}"}
                }
            ]
        }
    ],
    max_tokens=300,
    temperature=0.5
)

LiteLLM Proxy Usage (AI Gateway)

1. Add to config

model_list:
    - model_name: qwen-25vl-72b
      litellm_params:
        model: bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z

2. Start proxy

litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000

3. Test it!

Basic text request:

curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header 'Authorization: Bearer sk-1234' \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "qwen-25vl-72b",
            "messages": [
                {
                    "role": "user",
                    "content": "what llm are you"
                }
            ],
            "max_tokens": 300
        }'

With vision (image):

curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header 'Authorization: Bearer sk-1234' \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "qwen-25vl-72b",
            "messages": [
                {
                    "role": "system",
                    "content": "You are a helpful assistant that can analyze images."
                },
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "What is in this image?"},
                        {
                            "type": "image_url",
                            "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZ..."}
                        }
                    ]
                }
            ],
            "max_tokens": 300,
            "temperature": 0.5
        }'

Relevant issues

Pre-Submission checklist

Please complete all items before asking a LiteLLM maintainer to review your PR

• I have Added testing in the tests/litellm/ directory, Adding at least 1 test is a hard requirement - see details
• I have added a screenshot of my new test passing locally
• My PR passes all unit tests on make test-unit
• My PR's scope is as isolated as possible, it only solves 1 specific problem

Type

🆕 New Feature
✅ Test

Changes

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Project	Deployment	Preview	Comments	Updated (UTC)
litellm	Ready	Preview	Comment	Nov 25, 2025 8:14pm