Day 0 Support: GPT-6 Sol and GPT-6 Luna

LiteLLM now supports GPT-6 Sol and GPT-6 Luna. Route traffic to them through the LiteLLM AI Gateway with the same config you use for every other OpenAI model.

Sol and Luna join GPT-6 Astra, trained with the same methods and priced for work at scale. gpt-6-sol is built for complex coding and agentic workflows at $2 input and $10 output per 1M tokens, and gpt-6-luna is the high-volume tier at $0.10 and $0.50. OpenAI prices both at half their GPT-5.6 counterparts' current rates. Per OpenAI, Sol at xhigh scores 33.2% on AutomationBench at $0.27 a task and 68.8% on DeepSWE v1.1 at max, while Luna reaches 66.6% on DeepSWE. Both keep a 1,050,000-token context window with 922K input and 128K output, take text and image input, and run reasoning effort from none to max, defaulting to medium. There is no GPT-6 Terra; Astra stays the top of the range.

note

No image upgrade needed. LiteLLM already treats GPT-6 names as the GPT-5 request family, so max_completion_tokens and the reasoning params are handled on any version from v1.101.0. Pricing landed in PR #42515; 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-sol
    litellm_params:
      model: openai/gpt-6-sol
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-6-luna
    litellm_params:
      model: openai/gpt-6-luna
      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-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-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)

# gpt-6-luna for focused, high-volume work
response = completion(
    model="openai/gpt-6-luna",
    messages=[
        {"role": "user", "content": "Classify this ticket as bug, feature, or question."}
    ],
    reasoning_effort="none",
    temperature=0,
)

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

Responses API

For agentic and multi-turn workflows, use /v1/responses to preserve reasoning state across turns.

curl -X POST "http://0.0.0.0:4000/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_KEY" \
  -d '{
    "model": "gpt-6-sol",
    "input": "Plan and write a Python script that scrapes a webpage and summarizes it."
  }'

Pricing

Prices are per 1M tokens (USD), shown as short context (≤272K tokens) / long context (>272K tokens).

Model	Input	Cached input	Cache write	Output
gpt-6-sol	$2.00 / $4.00	$0.20 / $0.40	$2.50 / $5.00	$10.00 / $15.00
gpt-6-luna	$0.10 / $0.20	$0.01 / $0.02	$0.125 / $0.25	$0.50 / $0.75

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

Notes

temperature only applies when reasoning is off, so send reasoning_effort="none" alongside it, as in the Luna example above. Without it LiteLLM drops or refuses the parameter, since the default effort is medium.

Both models accept OpenAI's explicit prompt cache breakpoints, and LiteLLM passes them through on /chat/completions, /responses and /v1/messages; see prompt caching. OpenAI also says changing reasoning effort or tools mid-conversation no longer breaks the cache, which matters if you route different effort levels to the same deployment.

Feedback

Running GPT-6 Sol or Luna through LiteLLM and hitting something unexpected? Share it on GitHub discussion #42523.