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OpenAI · Efficient

GPT 6 Luna

Added Sep 22, 2026

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Use in the app

GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, suited for high-volume chat, classification, and lightweight agent workflows.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
Input
Text, Images
Output
Text
Reasoning effort
none · low · medium · high · xhigh · max
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.50
Cache read
$0.01
Cache write
$0.13

Reference prices as published by the model provider. API example

Specifications

Context window
1.1M
Max output
128K
Average output (7 days)
120 tokens
Longer than 12% of 473 models

Side by side

GPT 6 Luna compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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GPT 6 LunaOpenAIThis modelMiniMax M3MiniMaxRemove
ProviderOpenAIMiniMax
Discovery tierEfficientOpen weights
AddedSep 22, 2026Jun 1, 2026
Limits
Context window1.05MBest512K
Max output128KBest80K
Average output120 tokens491 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.23
Output$0.50Best$0.96
Cache read$0.01Best$0.05
Cache write$0.13—
Performance
Throughput225.9 tok/sBest97.7 tok/s
Time to first token3.4 s1.7 sBest
Request success (24h)99.9%99.9%
Benchmark indexes
Intelligence Index29.9Best29.2
Coding Index—58.6
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesNo
Reasoning effortnone, low, medium, high, xhigh, max—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • GPT 6 Luna
  • MiniMax M3

Intelligence Index

  • GPT 6 Luna29.9
  • MiniMax M329.2

GPT 6 Luna tested at medium reasoning effort

GPT 6 Luna: Better than 73% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GPT 6 Luna: —
    • MiniMax M3: 58.6

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GPT 6 Luna: 40.5%Top
    • MiniMax M3: 21.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GPT 6 Luna: 10.2%Top
    • MiniMax M3: 4.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GPT 6 Luna: —
    • MiniMax M3: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GPT 6 Luna: 1,097 EloTop
    • MiniMax M3: 1,089 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GPT 6 Luna: 1,263 EloTop
    • MiniMax M3: 1,243 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GPT 6 Luna: 13.8%Top
    • MiniMax M3: 9.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • GPT 6 Luna: 78.3%
    • MiniMax M3: 83.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GPT 6 Luna: 18.3%Top
    • MiniMax M3: 4.4%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GPT 6 Luna: 28.3%
    • MiniMax M3: 39.0%Top

  • GPQA Diamond

    Graduate-level science questions

    • GPT 6 Luna: —
    • MiniMax M3: 92.9%

  • IFBench

    Following precise output instructions

    • GPT 6 Luna: —
    • MiniMax M3: 82.9%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GPT 6 Luna: 2.5%Top
    • MiniMax M3: 2.0%

  • SciCode

    Scientific computing in Python

    • GPT 6 Luna: 50.9%Top
    • MiniMax M3: 47.1%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GPT 6 Luna: —
    • MiniMax M3: 42.4%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GPT 6 Luna: —
    • MiniMax M3: 88.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GPT 6 Luna: 78.3%
    • MiniMax M3: 83.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. GPT 6 Luna was tested at medium reasoning effort; compare scores measured at the same effort.Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
225.9 tok/s
Faster than 87% of 260 models
Time to first token
3.4 s
Quicker than 47% of 260 models
Prompt cache hits
56.4%
About $0.0492 per 1M input tokens with caching
Request success (24h)
99.9%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 98.5%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-6-luna",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="openai/gpt-6-luna",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

OpenAI

Other OpenAI models, starting with the same family.