[whatmodel.sh](../../README.md) / GPT-5.6 Luna
# GPT-5.6 Luna
OpenAI · released 2026-07 · closed weights
**score 75.0** ±0.5 · #34 of 732 · from 38 benchmark results across 10 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-5.6 Luna among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #44 of 313 21% 9
coding ▁▁▁▃▅▇█▇▅●▃▁ #49 of 469 10% 4
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #54 of 402* 2.6% 1
math ▁▁▃▅█▆▄█▄●▁▁ #17 of 431 39% 3
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #41 of 153 5.4% 5
web search ▁▁▁▅▄█●▄·▁·▁ #24 of 84* 2.1% 1
agent tasks · #44 of 313 · 21% of its score
Trails [GLM-5.2](glm-5-2.md), [GLM-5.3-Flash](glm-5-3-flash.md), [Grok 4.7](grok-4-7.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… -0.0233 IPS ▁▁▁▃▃●█▁▂▂▁▁ beats 20/59 avg -0.379 IPS 6.6% sharp
Terminal-Bench 4.0 17.3% █▅●▅▅·▅▃▃▅▅▅ beats 5/20 very hard · avg 0.5% 2.5% sharp
SWE-bench Pro (split unspecified)… 62.7% ▂·▁▁▁▁▂▃█●▂▁ beats 46/58 very hard · avg 4.9% 2.4% sharp · at its level
Terminal-Bench 2.1 (maker) 84.7% ▁·▁·▁▁▄▃▆█▅● beats 52/63 hard · avg 23% 2.3% sharp
BrowseComp (maker) 83.3% ▄··●▄··▄·▄█▄ beats 2/8 medium · avg 38% 2.1% typical
Toolathlon (Tool Decathlon) (make… 53.4% ▃··▃··▃··▃●▅ beats 5/8 very hard · avg 13% 1.7% at its level
DeepSWE v1.1 (resolved) (maker) 67.2% ●··▃▃····▃·▃ beats 2/6 hard · avg 22% 1.4% at its level
Agents' Last Exam (metric/harness… 50.3% ▂·▂·█▄▂·▂▂●▄ beats 11/14 very hard · avg 12% 0.9% at its level
KlinikeBench strict pass@1 (333 i… 7.5% ▁▁·▁▁▁▁●▃█▅█ beats 7/27 very hard · avg 2.4% 0.8% typical
coding · #49 of 469 · 10% of its score
Beats [GPT-5.5](gpt-5-5.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall. Trails [Qwen3.8 27B](qwen3-8-27b.md), [GLM-5.2](glm-5-2.md), [Gemini 3.6 Flash](gemini-3-6-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1518.9 ▁▁▃▂▅█▃●▅▅▁▂ beats 77/129 avg 981 4.5% sharp
SWE-bench Pro (split unspecified)… 62.7% ▂·▁▁▁▁▂▃█●▂▁ beats 46/58 very hard · avg 4.9% 2.4% sharp · at its level
Luau Leetcode fully solved (Torpe… 72.6% ▁▁▁▁·▁▂▃▅●█▇ beats 24/49 hard · avg 17% 1.7% sharp
DeepSWE v1.1 (resolved) (maker) 67.2% ●··▃▃····▃·▃ beats 2/6 hard · avg 22% 1.4% at its level
chat & writing · #54 of 402 · 2.6% of its score
Beats [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Claude Sonnet 4.5](claude-sonnet-4-5.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1453.0 ▁▁▁▂▃▃▃▆▆▇●▃ beats 323/385 avg 1260 2.6% blunt
math · #17 of 431 · 39% of its score
Beats [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md), [Qwen3.8 Max (2.4T-A95B)](qwen3-8-2-4t-a95b.md), [Claude Opus 4.8](claude-opus-4-8.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 61.0% ▁▂▁▂█▅●▃▂▁▁▁ beats 53/67 very hard · avg 0.1% 22% sharp · at its level · several runs
FrontierMath tiers 1–3 v2 82.1% ▂··▁▁▃▄▄█▆●▃ beats 72/86 very hard · avg 1.2% 17% sharp · at its level · several runs
OTIS mock AIME 2024–2025 98.3% ▃▃▄▃▃▃▇▇█▃●▅ beats 176/197 very hard · avg 7.2% 0.3% sharp · maxed out · noisy
vision & documents · #41 of 153 · 5.4% of its score
Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [Qwen3.6 Plus](qwen3-6-plus.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1256.6 ▁▁▂▂▂▂▃▄▅▅●▇ beats 113/152 avg 1061 1.7% blunt
MMMU Pro (maker) 78.4% ▅·····▃·●·█▃ beats 3/7 hard · avg 40% 1.7% typical
Arena document (style controlled) 1462.0 ▁▁▃▃▄▃▇●▅▇▄▅ beats 19/37 avg 1331 0.9% blunt
SciDocBench scientific-document w… 50.6% ▃▃···●▃▅▃·▃▅ beats 4/11 hard · avg 25% 0.9% blunt · at its level
SpaceCast-Bench predictive spatia… 46.6% ▃·▅▃▅▃··●··█ beats 7/11 very hard · avg 33% 0.3% at its level · noisy
web search · #24 of 84 · 2.1% of its score
Beats [GPT-5.5](gpt-5-5.md), [Claude Opus 4.8](claude-opus-4-8.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BrowseComp (maker) 83.3% ▄··●▄··▄·▄█▄ beats 2/8 medium · avg 38% 2.1% typical
other tests (16)
test result vs the others in its run difficulty counts why
Furniture Assembly (Epoch) 42.5% █▆▆▆●▂▆▂▂▂▂▅ beats 17/32 very hard · avg 1.2% 4.4% sharp · at its level
Mystery Game Puzzles (Epoch) 21.0% █··▂▃●▄█▂▄▂▁ beats 37/77 very hard · avg 9.4% 4.3% sharp · at its level · noisy
KMMLU-HARD (Daekeun Kim, zero-sho… 75.8% █···▁▁▁▂▃·●▂ beats 19/23 very hard · avg 29% 3.0% sharp · at its level
Chess Puzzles (Epoch) 40.0% █▁▁▁▁▂▃▃●▂▁▁ beats 121/137 very hard · avg 5.8% 2.5% sharp · at its level · noisy
GPQA Diamond 91.6% ▁▁▁▁▂▄▆▄▅▇●▅ beats 186/211 hard · avg 39% 1.6% sharp
CLIcK Korean (Daekeun Kim) 91.5% ▆▄▄▄█▄▂·▄●·▄ beats 19/23 medium · avg 56% 1.4% typical
KoBALT-700 (Daekeun Kim) 77.0% ▄▄●·····▄··▄ beats 3/5 hard · avg 39% 1.2% typical
KMMLU-Pro (Daekeun Kim) 85.5% ▄·▄●·····▄·▄ beats 2/5 medium · avg 43% 1.0% typical
HAE-RAE 1.0 (Daekeun Kim) 93.2% ▄▄▄▂▂█▂▄▂█●▄ beats 20/22 medium · avg 61% 1.0% maxed out
SimpleQA Verified 41.0% ▁▁▃▂▃▃▆●█▄▂▅ beats 35/81 very hard · avg 8.0% 1.0% at its level
Icelandic LLM Leaderboard average… 53.9% ▁▁▃▂▂▄▁▃▄●▄█ beats 23/38 very hard · avg 14% 1.0% at its level
HealthBench Professional (maker) 55.7% ▄·▄··●▄·▄█·█ beats 2/8 hard · avg 20% 1.0% blunt · at its level
MuSR Korean (Daekeun Kim) 71.5% ●·····███·██ beats 0/5 medium · avg 45% 0.7% typical
RobloxQA v2.0 multiple choice (To… 95.8% ▁▁▃▁█▄▃●▆▂▂▂ beats 32/49 easy · avg 77% 0.6% maxed out
HealthBench Hard (maker) 31.4% ▂··▂····▂●·█ beats 4/8 very hard · avg 9.6% 0.6% blunt · at its level
HealthBench (maker) 55.4% ▃·▃·····●▃▃▅ beats 2/8 hard · avg 24% 0.6% blunt · at its level
source results share of the score
Epoch AI 8 36% ▇▇▇▇
OpenAI vendor-reported benchmark scores… 12 32% ▇▇▇▇
LMArena leaderboard dataset 5 16% ▇▇
Daekeun Kim Korean LLM benchmark runs 6 8% ▇
Terminal-Bench 4.0 official leaderboard… 1 3% ▇
Roblox LLM Leaderboard 2 2% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
SciDocBench: A Workflow-Centered Benchm… 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
SpaceCast-Bench: Evaluating Predictive … 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 75.0
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (OpenAI) · $0.45/M blended · $0.2 in / $1.2 out per 1M tokens · [platform.openai.com](https://platform.openai.com/docs/models)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/openai/models/gpt-5.6-luna.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/openai/gpt-5.6-luna.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-6-luna) · [JSON](https://whatmodel.sh/api?format=json&all=1)