OpenAI

GPT-5.6 Luna

Type
language model
Context
1,050K tokens
Max output
128K
Released
9 July 2026
Knowledge cutoff
February 2026
API string
openai/gpt-5.6-luna

Prices by provider

Provider Input, $ per 1M Output, $ per 1M In our data since
opencode-go $0.1 $0.6 31 Jul 2026
OpenRouter $0.1 $0.6 31 Jul 2026
Azure Cognitive Services $0.2 $1.2 31 Jul 2026
CrossModel $0.2 $1.2 1 Aug 2026
LLM Gateway $0.2 $1.2 1 Aug 2026
Microsoft Foundry gpt-5.6-luna $0.2 $1.2 31 Jul 2026
OpenAI $0.2 $1.2 31 Jul 2026
OpenCode Zen $0.2 $1.2 31 Jul 2026
Vercel $0.2 $1.2 31 Jul 2026
bedrock_mantle $0.22 $1.32 31 Jul 2026
Amazon Bedrock $0.22 $1.32 31 Jul 2026
routing-run $0.7 $4.2 31 Jul 2026
Merge Gateway $0.75 $4.5 31 Jul 2026
vivgrid $1 $6 31 Jul 2026
ai-router $1 $6 31 Jul 2026
ofox $1 $6 31 Jul 2026
AIHubMix $1 $6 31 Jul 2026
Abacus.AI $1 $6 31 Jul 2026
Cloudflare AI Gateway $1 $6 31 Jul 2026
Databricks $1 $6 31 Jul 2026
GitHub Copilot $1 $6 31 Jul 2026
Microsoft Foundry azure/gpt-5.6-luna $1 $6 31 Jul 2026
ZenMux $1 $6 31 Jul 2026
Microsoft Foundry azure/eu/gpt-5.6-luna $1.1 $6.6 31 Jul 2026

Only the base tier and only per-token prices are shown. Batch, discounted and cached rates, as well as prices per image or per second of video, do not go into this table: they cannot stand in the same column as a price per million tokens.

A single provider appears several times if it sells this model under different identifiers and at different prices — for example, at different weight precision. The identifier is shown next to the name. Identical offers that arrived from two sources under different spellings are merged into one row.

The date in the last column is the day this price first entered our collection. The price may well be older: before that day we simply were not recording it. It has not changed since — otherwise a new row with a new date would stand in its place.

Measurement results

Task set Result Run conditions Measured by
Arena Score, web development 1,522.94 1,512–1,534 effort: xhigh —
Arena Score, expert questions 1,480.28 1,460–1,500 effort: xhigh —
Arena Score, programming 1,471.79 1,460–1,484 effort: xhigh —
Arena Score, hard prompts 1,452.94 1,445–1,461 effort: xhigh —
Arena Score, mathematics 1,450.15 1,421–1,479 effort: xhigh —
Arena Score in English 1,445.19 1,435–1,455 effort: xhigh —
Arena Score, instruction following 1,441.56 1,430–1,453 effort: xhigh —
Arena Score, long queries 1,439.65 1,430–1,450 effort: xhigh —
Arena Score, multi-turn dialogue 1,438.94 1,423–1,454 effort: xhigh —
Arena Score in Spanish 1,431.67 1,393–1,471 effort: xhigh —
Arena Score in French 1,430.38 1,396–1,465 effort: xhigh —
Arena Score, overall 1,429.21 1,422–1,436 effort: xhigh —
Arena Score in Russian 1,421.72 1,402–1,442 effort: xhigh —
Arena Score, creative writing 1,403.67 1,388–1,419 effort: xhigh —
Arena Score, understanding diagrams 1,267.95 1,243–1,293 effort: xhigh —
Arena Score, text recognition in images 1,259.21 1,244–1,275 effort: xhigh —
Arena Score, working with images 1,248.77 1,235–1,262 effort: xhigh —
Mock AIME 2024–2025 — olympiad problems 98.33 % effort: max · with a tuned harness Epoch evaluations
GPQA Diamond — graduate-level questions 88.81 % effort: max · with a tuned harness Epoch evaluations
ARC-AGI — generalising to unseen patterns 88 % effort: max · with a tuned harness https://arcprize.org/leaderboard
FrontierMath, levels 1–3 82.11 % effort: max Epoch evaluations
FrontierMath, level 4 — research-grade problems 60.98 % effort: max Epoch evaluations
WeirdML — unusual machine learning tasks 60.86 % effort: high https://htihle.github.io/weirdml.html
ARC-AGI-2 59.54 % effort: max —
SimpleQA Verified — factual accuracy 41.7 % effort: max Epoch evaluations
FrontierCode — patches fit to be merged into a project 39.8 % — https://cognition.com/frontiercode
Chess puzzles 36.87 % effort: max Epoch evaluations
SimpleBench — trick questions 36.16 % — SimpleBench Leaderboard
CritPt — physics problems 20.6 % effort: max —
Arena Score, agent tasks 0.04 0–0 effort: xhigh —