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GPT-6 Luna

Budget

by OpenAI

GPT-6 Luna shipped alongside GPT-6 Sol on September 22, 2026 as gpt-6-luna, and OpenAI describes it as its most efficient model for focused, high-volume tasks. Pricing is $0.10 per million input tokens, $0.01 cached, and $0.50 output, down from $0.20 and $1.20 on GPT-5.6 Luna, so the output cut is closer to 58 percent than the 50 percent OpenAI's launch table rounds it to. Prompts above 272,000 input tokens bill the whole request at $0.20, $0.02, and $0.75. Context is 1,050,000 tokens with 128K output, text and image input, and the same reasoning effort ladder as Sol from none through max. OpenAI's claim is that Luna matches GPT-5.6 Sol at about a hundredth of its cost, which is a vendor comparison worth testing on your own tasks. The independent numbers are more measured and still strong for the price: Cognition puts it at 42.4 percent on FrontierCode v1.1 at max effort, the Vals Index at 58.45 percent, twentieth of 65, and Artificial Analysis at 37. On ChatGPT, Free and Go users get Luna in the desktop app, and Codex now suggests switching to it when a user nears a rate limit. OpenAI did not publish SWE-bench Verified, MMLU-Pro, GPQA Diamond, BrowseComp, or HLE figures for it.

Input Price

$0.10

per 1M tokens

Output Price

$0.50

per 1M tokens

Context Window

1.1M

tokens

Released

2026-09

API access

Capabilities

textvisiontool-usecodereasoning

Key Strengths

  • ✓$0.10/$0.50 per 1M tokens with cached input at $0.01
  • ✓1,050,000 token context window with 128K output
  • ✓FrontierCode v1.1 at 42.4 percent, measured by Cognition
  • ✓Vals Index 58.45 percent at a budget-tier price
  • ✓Full reasoning effort ladder from none through max

Best For

  • ▸High-volume classification, extraction, and routing
  • ▸Sub-agents under a Sol or Astra planner
  • ▸Cost-sensitive coding assistance
  • ▸Latency-sensitive chat at scale

Benchmark Scores

BenchmarkScoreDescription
FrontierCode v1.142.4Agentic coding on frontier software engineering tasks (Main split)

Scores sourced from public benchmark datasets. See full benchmark leaderboard for all models.

Pricing Details

Input tokens

$0.10

per 1M tokens

Output tokens

$0.50

per 1M tokens

Estimated cost per 1K requests

$0.35

~1K input + ~500 output tokens avg

Prices are subject to change. Check the official documentation for current pricing. See the cost calculator for detailed estimates.

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