[whatmodel.sh](../../README.md) / GPT-5.6 Terra
# GPT-5.6 Terra
OpenAI · released 2026-07 · closed weights
**score 76.6** ±0.5 · #22 of 732 · from 39 benchmark results across 11 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 Terra among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #37 of 313 22% 10
coding ▁▁▁▃▅▇█▇▅●▃▁ #47 of 469 11% 5
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #37 of 402* 2.6% 1
math ▁▁▃▅█▆▄█▄▂●▁ #14 of 431 37% 3
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #31 of 153 5.0% 4
web search ▁▁▁▅▄█▃●·▁·▁ #14 of 84* 1.9% 1
agent tasks · #37 of 313 · 22% of its score
Trails [Grok 4.7](grok-4-7.md), [Claude Sonnet 5](claude-sonnet-5.md), [Grok 4.5](grok-4-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… -0.0139 IPS ▁▁▁▃▃●█▁▂▂▁▁ beats 22/59 avg -0.379 IPS 6.6% sharp
Terminal-Bench 4.0 21.5% █▅▅●▅·▅▃▃▅▅▅ beats 8/20 very hard · avg 0.5% 2.9% sharp · at its level
SWE-bench Pro (split unspecified)… 63.4% ▂·▁▁▁▁▂▃█●▂▁ beats 48/58 very hard · avg 4.9% 2.4% sharp · at its level
Terminal-Bench 2.1 (maker) 87.4% ▁·▁·▁▁▄▃▆█▅● beats 56/63 hard · avg 23% 2.1% sharp
BrowseComp (maker) 87.5% ▄··█▄··●·▄█▄ beats 4/8 medium · avg 38% 1.9% typical
Toolathlon (Tool Decathlon) (make… 53.1% ▃··▃··▃··▃●▅ beats 4/8 very hard · avg 13% 1.7% at its level
E2E-SWE (build a working codebase… 51.8% ▃··▅··▃█▃●▃▅ beats 8/12 very hard · avg 3.7% 1.6% sharp · at its level
DeepSWE v1.1 (resolved) (maker) 69.6% █··▃●····▃·▃ beats 4/6 hard · avg 22% 1.3% at its level
Agents' Last Exam (metric/harness… 50.4% ▂·▂·█▄▂·▂▂●▄ beats 12/14 very hard · avg 12% 0.9% at its level
KlinikeBench strict pass@1 (333 i… 8.4% ▁▁·▁▁▁▁●▃█▅█ beats 8/27 very hard · avg 2.4% 0.8% typical
coding · #47 of 469 · 11% of its score
Beats [GPT-5.5](gpt-5-5.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) 1520.1 ▁▁▃▂▅█▃●▅▅▁▂ beats 79/129 avg 981 4.6% sharp
SWE-bench Pro (split unspecified)… 63.4% ▂·▁▁▁▁▂▃█●▂▁ beats 48/58 very hard · avg 4.9% 2.4% sharp · at its level
E2E-SWE (build a working codebase… 51.8% ▃··▅··▃█▃●▃▅ beats 8/12 very hard · avg 3.7% 1.6% sharp · at its level
Luau Leetcode fully solved (Torpe… 81.3% ▁▁▁▁·▁▂▃▅▃●▇ beats 36/49 hard · avg 17% 1.5% sharp
DeepSWE v1.1 (resolved) (maker) 69.6% █··▃●····▃·▃ beats 4/6 hard · avg 22% 1.3% at its level
chat & writing · #37 of 402 · 2.6% of its score
Beats [GPT-6 Sol](gpt-6-sol.md) here, though they rank higher overall. Trails [Grok 4.20 (0309 reasoning)](grok-4-20.md), [Claude Opus 4.5](claude-opus-4-5.md), [Gemini 3 Flash (Preview)](gemini-3-flash-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1467.3 ▁▁▁▂▃▃▃▆▆▇●▃ beats 344/385 avg 1260 2.6% blunt
math · #14 of 431 · 37% of its score
Beats [GPT-5.4 Pro](gpt-5-4-pro.md), [Muse Wonder](muse-wonder.md), [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 70.7% ▁▂▁▂█▅▂●▂▁▁▁ beats 55/67 very hard · avg 0.1% 22% sharp · at its level · several runs
FrontierMath tiers 1–3 v2 86.0% ▂··▁▁▃▄▄█▆●▃ beats 76/86 very hard · avg 1.2% 15% sharp · several runs
OTIS mock AIME 2024–2025 99.7% ▃▃▄▃▃▃▇▇█▃▂● beats 194/197 very hard · avg 7.2% 0.2% sharp · maxed out · noisy
vision & documents · #31 of 153 · 5.0% of its score
Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [MiMo-V2.6-Pro](mimo-v2-6-pro.md) here, though they rank higher overall. Trails [Qwen3.6 Plus](qwen3-6-plus.md), [Qwen3.7 Plus](qwen3-7-plus.md), [Gemini 3 Flash (Preview)](gemini-3-flash-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1268.0 ▁▁▂▂▂▂▃▄▅▅●▇ beats 123/152 avg 1061 1.7% blunt
MMMU Pro (maker) 80.7% ▅·····▃·▃·●▃ beats 4/7 hard · avg 40% 1.6% typical
Arena document (style controlled) 1476.7 ▁▁▃▃▄▃▇█▅●▄▅ beats 27/37 avg 1331 0.9% blunt
SciDocBench scientific-document w… 54.2% ▃▃···█▃●▃·▃▅ beats 7/11 hard · avg 25% 0.8% blunt · at its level
web search · #14 of 84 · 1.9% of its score
Beats [Claude Fable 5.1](claude-fable-5-1.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md), [Claude Opus 5](claude-opus-5.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) 87.5% ▄··█▄··●·▄█▄ beats 4/8 medium · avg 38% 1.9% typical
other tests (17)
test result vs the others in its run difficulty counts why
Mystery Game Puzzles (Epoch) 35.0% █··▂▃▇▄●▂▄▂▁ beats 57/77 very hard · avg 9.4% 5.0% sharp · at its level · noisy
Furniture Assembly (Epoch) 54.2% █▆▆▆▆▂●▂▂▂▂▅ beats 23/32 very hard · avg 1.2% 4.7% sharp · at its level
Chess Puzzles (Epoch) 54.0% █▁▁▁▁▂▃▃▃▂●▁ beats 132/137 very hard · avg 5.8% 2.7% sharp · at its level · noisy
KMMLU-HARD (Daekeun Kim, zero-sho… 77.9% █···▁▁▁▂▃·●▂ beats 21/23 very hard · avg 29% 2.6% sharp
GPQA Diamond 93.3% ▁▁▁▁▂▄▆▄▅▇█● beats 198/211 hard · avg 39% 1.3% sharp
CLIcK Korean (Daekeun Kim) 92.2% ▆▄▄▄█▄▂·▄●·▄ beats 21/23 medium · avg 56% 1.2% maxed out
KoBALT-700 (Daekeun Kim) 76.6% ▄▄●·····▄··▄ beats 2/5 hard · avg 39% 1.1% typical
SimpleQA Verified 43.2% ▁▁▃▂▃▃▆●█▄▂▅ beats 40/81 very hard · avg 8.0% 1.0% at its level
KMMLU-Pro (Daekeun Kim) 84.4% ▄·●█·····▄·▄ beats 1/5 medium · avg 43% 1.0% typical
HealthBench Professional (maker) 57.7% ▄·▄··▄●·▄█·█ beats 3/8 hard · avg 20% 0.9% blunt · at its level
Icelandic LLM Leaderboard average… 53.2% ▁▁▃▂▂▄▁▃▄●▄█ beats 22/38 very hard · avg 14% 0.9% at its level
HAE-RAE 1.0 (Daekeun Kim) 92.8% ▄▄▄▂▂█▂▄▂●▂▄ beats 19/22 medium · avg 61% 0.9% maxed out
AutomationBench public set (Zapie… 37.2% ●······▄█··▄ beats 0/4 very hard · avg 11% 0.8% at its level
MuSR Korean (Daekeun Kim) 83.2% █·····██●·██ beats 3/5 medium · avg 45% 0.7% typical
HealthBench Hard (maker) 34.3% ▂··▂····▂▄·● beats 8/8 very hard · avg 9.6% 0.6% blunt · at its level
HealthBench (maker) 58.7% ▃·▃·····█▃▃● beats 8/8 hard · avg 24% 0.6% blunt · at its level
RobloxQA v2.0 multiple choice (To… 95.8% ▁▁▃▁█▄▃●▆▂▂▂ beats 31/49 easy · avg 77% 0.5% maxed out
source results share of the score
Epoch AI 8 36% ▇▇▇▇
OpenAI vendor-reported benchmark scores… 12 30% ▇▇▇▇
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% ▇
E2E-SWE: Benchmarking LLMs on Building … 1 2% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
SciDocBench: A Workflow-Centered Benchm… 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
Zapier AutomationBench README: public-s… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 76.6
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)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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/)) · Zapier AutomationBench README: public-set pass rates ([MIT](../../licenses/zapier-automationbench-readme-MIT.txt))
## How to run it
### Through an API
api (OpenAI) · $4.50/M blended · $2 in / $12 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-terra.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-terra.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-6-terra) · [JSON](https://whatmodel.sh/api?format=json&all=1)