Day 0 Support: GPT-6.1 Sol

LiteLLM now supports GPT-6.1 Sol. Route traffic to it through the LiteLLM AI Gateway with the same config you use for every other OpenAI model.

GPT-6.1 Sol is an upgrade to GPT-6 Sol at the same $2 input and $10 output per 1M tokens, with cached input cut in half to $0.10. Per OpenAI, it matches GPT-6 Astra on DeepSWE v1.1 at roughly a fifth of the cost, and on Terminal-Bench Science it averages $5.47 a task against $23.21 for Opus 5.5.

note

No image upgrade needed. Pricing landed in PR #43738; hit Reload Model Cost Map in the Admin UI (or POST /reload/model_cost_map) to pull it, on v1.76.0 and above.

Usage

LiteLLM Proxy

1. Setup config.yaml

model_list:
  - model_name: gpt-6.1-sol
    litellm_params:
      model: openai/gpt-6.1-sol
      api_key: os.environ/OPENAI_API_KEY

2. Start the proxy

docker run -d \
  -p 4000:4000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:main-latest \
  --config /app/config.yaml

3. Test it

curl -X POST "http://0.0.0.0:4000/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_KEY" \
  -d '{
    "model": "gpt-6.1-sol",
    "messages": [
      {"role": "user", "content": "Write a Python function to check if a number is prime."}
    ],
    "reasoning_effort": "high"
  }'

LiteLLM Python SDK

from litellm import completion

response = completion(
    model="openai/gpt-6.1-sol",
    messages=[
        {"role": "user", "content": "Write a Python function to check if a number is prime."}
    ],
    reasoning_effort="high",
)

print(response.choices[0].message.content)

Pricing

Per 1M tokens (USD), short context (≤272K tokens) / long context (>272K tokens).

Model	Input	Cached input	Cache write	Output
gpt-6.1-sol	$2.00 / $4.00	$0.10 / $0.20	$2.50 / $5.00	$10.00 / $15.00

Batch and Flex run at half these rates and Fast mode at double; LiteLLM tracks all of them from the same cost map row.

Notes

OpenAI serves tool calling for this model on the Responses API only. LiteLLM bridges a /chat/completions request with tools to /v1/responses for you, so existing tool-calling code keeps working.

Reasoning effort runs low to max, defaulting to medium. Unlike GPT-6 Sol, none is not supported, so temperature is not available on this model.

Feedback

Running GPT-6.1 Sol through LiteLLM and hitting something unexpected? Share it on GitHub discussion #43742.