GPT-6 Luna

GPT-6 Luna is OpenAI's most efficient GPT-6 model, built for focused, high-volume tasks, and the lowest-cost model in the GPT-6 family. Send the official OpenAI Responses or Chat Completions request to SeedRouter: change the base URL and the API key, keep the body.

Model ID

Input: text and images. Output: text. Knowledge cutoff: May 18, 2026. See the model page for current prices.

Quick example

curl https://api.seedrouter.ai/v1/responses \
  -H "Authorization: Bearer $SEEDROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-luna",
    "input": "Summarize the trade-offs of event sourcing in three bullet points."
  }'

Endpoints

The OpenAI endpoints forward your request body as sent and return the official response. Keep the API key in server-side code.

Parameters

Responses API fields:

Not available: background, service_tier (including Batch, Flex and fast mode) and safety_identifier.

Reasoning and effort

GPT-6 Luna defaults to medium. At none the model answers without reasoning, and only then are temperature, top_p and top_logprobs accepted. Higher effort usually means more output tokens, a longer wait and a higher cost. Reasoning tokens are billed as output tokens and count toward max_output_tokens.

Image input

Images go in a user message's content as input_image parts with an image_url:

{"role": "user", "content": [
  {"type": "input_image", "image_url": "https://example.com/chart.png"},
  {"type": "input_text", "text": "What does this chart show?"}
]}

Replace the example URL with a publicly reachable image of your own.

Billing dimensions

See the current rates on the model page. A request is billed by the tokens it uses:

• input tokens,
• output tokens, including reasoning,
• cached input tokens (usage.input_tokens_details.cached_tokens), and
• cache-write tokens (usage.input_tokens_details.cache_write_tokens).

When a prompt has more than 272K input tokens, the whole request is billed at the long-context rates. The charge is taken from the usage reported with the finished response. A request that fails is not charged. Your account's usage records show the exact charge for every request.

Output

A non-streaming request returns the official response object:

{
  "id": "resp_...",
  "object": "response",
  "status": "completed",
  "model": "gpt-6-luna",
  "output": [{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "..."}]}],
  "usage": {"input_tokens": 27, "input_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0}, "output_tokens": 102, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 129}
}

With "stream": true the response is a stream of the official events, including response.created, response.output_text.delta and response.completed; the final response.completed event carries the usage.

Chat Completions

Existing Chat Completions code only needs a new base URL and model ID:

from openai import OpenAI

client = OpenAI(api_key="YOUR_SEEDROUTER_KEY", base_url="https://api.seedrouter.ai/v1")

chat = client.chat.completions.create(
    model="gpt-6-luna",
    messages=[{"role": "user", "content": "Hello"}],
    max_completion_tokens=1024,
)
print(chat.choices[0].message.content)

Set the output limit with max_completion_tokens and the effort with reasoning_effort. On Chat Completions, GPT-6 Luna supports function calling only with reasoning_effort set to none; use the Responses API for reasoning with tools.

Anthropic Messages format

Code written for the Anthropic Messages API can call GPT-6 Luna too: send the Messages body to /v1/messages with "model": "gpt-6-luna". The request is converted to Chat Completions, so a field with no Chat Completions counterpart has no effect. The response carries the official Messages fields and may include a few extra usage fields; read usage.input_tokens, usage.output_tokens and the official fields.

Errors

Errors use {"error": {"code": ..., "message": "..."}}. The code is a code from the common error catalog. Failed requests are not charged.

Tips

• For classification and extraction, try none first: it is the fastest and cheapest setting.
• Set max_output_tokens high enough for the reasoning as well as the answer: it is one budget for both.
• Keep long, reused context at the start of the prompt so later requests read it from the cache at the lower rate.

Related

• GPT-6 Luna playground and pricing
• Download OpenAPI
• Copyable Markdown