[whatmodel.sh](../../README.md) / GPT-5.5
# GPT-5.5
OpenAI · released 2026-04 · closed weights
**score 78.1** ±0.4 · #18 of 732 · from 70 benchmark results across 22 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-5.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #24 of 313 26% 22
coding ▁▁▁▃▅▇█▇▅●▃▁ #51 of 469 9.0% 6
chat & writing ▁▁▁▃▆▄▆█▆▇▇● #20 of 402* 2.2% 2
math ▁▁▃▅█▆▄█▄▂●▁ #12 of 431 36% 18
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #22 of 153 3.1% 3
web search ▁▁▁▅▄●▃▄·▁·▁ #25 of 84 6.1% 6
agent tasks · #24 of 313 · 26% of its score
Trails [Grok 4.7](grok-4-7.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0312 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 43/59 avg -0.379 IPS 5.0% sharp
Agents' Last Exam overall full-pa… 21.1% ▄···█▄▄▄█▄·● beats 10/10 very hard · avg 1.9% 2.0% typical
SWE-bench Pro (split unspecified)… 59.4% ▂·▁▁▁▁▂▃█●▂▁ beats 42/58 very hard · avg 4.9% 1.7% sharp · at its level
Terminal-Bench 2.1 (maker) 85.6% ▁·▁·▁▁▄▃▆█▅● beats 54/63 hard · avg 23% 1.5% sharp
τ³-bench banking_knowledge (v1.0.… 44.6% ▃▃▂▃·▆▃▂█●▅▂ beats 21/25 very hard · avg 4.3% 1.5% at its level
K-BrowseComp-Verified (Korean web… 45.7% ▃····█··▃█·● beats 8/8 very hard · avg 3.6% 1.4% at its level
BrowseComp (maker) 84.4% ▄··█●··▄·▄█▄ beats 3/8 medium · avg 38% 1.4% typical
Humanity's Last Exam with tools (… 52.2% ▃·▃·▅····●·█ beats 5/8 very hard · avg 12% 1.3% at its level
Toolathlon (Tool Decathlon) (make… 55.6% ▃··▃··▃··▃█● beats 7/8 very hard · avg 13% 1.3% at its level
E2E-SWE (build a working codebase… 46.1% ▃··▅··▃█●▅▃▅ beats 7/12 very hard · avg 3.7% 1.2% sharp · at its level
DeepSWE v1.1 (resolved) (maker) 67.0% ●··▃▃····▃·▃ beats 1/6 hard · avg 22% 1.0% at its level
Terminal-Bench 2.0 (maker) 82.7% ██···██··██● beats 6/6 hard · avg 15% 0.9% typical
MCP-Atlas (public set) 75.3% █··█·██·●███ beats 4/7 hard · avg 30% 0.8% typical
OSWorld-Verified (maker) 78.7% █▄·····▄·▄▄● beats 6/6 hard · avg 16% 0.7% at its level
Legal Research Bench (agentic, al… 40.0% ▄█··▄▄▄██·▄● beats 11/12 very hard · avg 6.3% 0.7% at its level
Agents' Last Exam (metric/harness… 46.9% ▂·▂·█▄▂·▂●▄▄ beats 10/14 very hard · avg 12% 0.7% at its level
KlinikeBench strict pass@1 (333 i… 16.8% ▁▁·▁▁▁▁▅▃●▅█ beats 16/27 very hard · avg 2.4% 0.7% typical
EEGAgentBench overall (tool-based… 50.0% ▂·▁▁··▇▇●▂·▁ beats 19/25 hard · avg 28% 0.6% blunt · at its level
RWSearch 85.3% ████··█····● beats 5/5 medium · avg 45% 0.6% typical
SWE-bench Pro (maker) 58.6% ▄·····▄·▄▄▄● beats 6/6 hard · avg 29% 0.5% blunt · at its level
TerminalWorld-Verified (200 tasks… 53.5% ▄··●·▄·█···▄ beats 1/5 hard · avg 28% 0.4% blunt · at its level
τ²-bench Telecom (maker) 98.0% ▃·▅▅▃▃··█▃▃● beats 12/13 hard · avg 16% 0.3% sharp · maxed out · noisy
coding · #51 of 469 · 9.0% of its score
Trails [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Qwen3.8 27B](qwen3-8-27b.md), [GLM-5.2](glm-5-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1512.1 ▁▁▃▂▅█▃●▅▅▁▂ beats 75/129 avg 981 3.5% sharp
SWE-bench Pro (split unspecified)… 59.4% ▂·▁▁▁▁▂▃█●▂▁ beats 42/58 very hard · avg 4.9% 1.7% sharp · at its level
E2E-SWE (build a working codebase… 46.1% ▃··▅··▃█●▅▃▅ beats 7/12 very hard · avg 3.7% 1.2% sharp · at its level
Luau Leetcode fully solved (Torpe… 85.1% ▁▁▁▁·▁▂▃▅▃█● beats 45/49 hard · avg 17% 1.1% sharp
DeepSWE v1.1 (resolved) (maker) 67.0% ●··▃▃····▃·▃ beats 1/6 hard · avg 22% 1.0% at its level
SWE-bench Pro (maker) 58.6% ▄·····▄·▄▄▄● beats 6/6 hard · avg 29% 0.5% blunt · at its level
chat & writing · #20 of 402 · 2.2% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [Muse Spark 1.2](muse-spark-1-2.md), [Muse Spark 1.1](muse-spark-1-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1481.7 ▁▁▁▂▃▃▃▆▆▇█● beats 363/385 avg 1260 2.0% blunt
IFEval (aggregation unspecified) 95.0% █·██·█···●·█ beats 4/5 easy · avg 72% 0.2% maxed out
math · #12 of 431 · 36% of its score
Beats [Claude Opus 5](claude-opus-5.md), [GPT-5.4 Pro](gpt-5-4-pro.md), [Muse Wonder](muse-wonder.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 72.5% ▁▂▁▂█▅▂●▂▁▁▁ beats 56/67 very hard · avg 0.1% 8.4% sharp · at its level
FrontierMath tiers 1–3 v2 85.3% ▂··▁▁▃▄▄█▆●▃ beats 74/86 very hard · avg 1.2% 5.8% sharp
MathArena ArXivMath April 2026 (4… 68.8% ▁·····▅██●·▁ beats 17/20 very hard · avg 7.6% 3.6% sharp · at its level
MathArena ArXivMath June 2026 (48… 83.6% ▂···▄·██▆●▄▂ beats 15/19 very hard · avg 5.1% 3.4% sharp
MathArena ArXivMath February 2026… 73.4% ▁▁▁▁█▇▄▃▁▃·● beats 23/24 very hard · avg 9.4% 2.2% at its level
MathArena BrokenArXiv April 2026 … 72.1% ▂▂▆█▃▃▂●▂··▂ beats 16/18 very hard · avg 0.8% 1.7% sharp · at its level · noisy
MathArena ArXivMath March 2026 (3… 77.5% █▃▅·█▃▃·▅·▃● beats 14/14 hard · avg 16% 1.6% at its level
MathArena ArXivMath January 2026 … 73.9% ▂··▄▂▄█▆▆▄▄● beats 22/23 hard · avg 23% 1.3% typical
MathArena ArXivMath May 2026 (40 … 77.5% ▁··▁▄█▄●·▃·▁ beats 14/17 very hard · avg 11% 1.2% at its level
MathArena BrokenArXiv May 2026 (5… 50.0% ██·▄▄▄····▄● beats 8/8 very hard · avg 1.9% 1.1% sharp · at its level
MathArena BrokenArXiv June 2026 (… 69.4% ▄··█▂▂▂▆●··▂ beats 13/14 very hard · avg 1.3% 1.1% sharp · at its level · noisy
MathArena BrokenArXiv March 2026 … 73.7% █·▆█▂·▂▂···● beats 14/14 very hard · avg 1.0% 1.1% sharp · at its level · noisy
MathArena BrokenArXiv February 20… 68.1% ▃▂▂▃█▃▂·▃··● beats 16/16 very hard · avg 0.9% 1.0% sharp · at its level · noisy
MathArena ArXivLean March 2026 (4… 17.1% ▂····▂·····● beats 4/5 very hard · avg 1.2% 0.8% typical
IMO-AnswerBench 79.5% ▁▁·▁▁▁▄▆●▄▂▃ beats 23/44 hard · avg 25% 0.5% typical
MathConstraint full release, no t… 66.9% ▄··▄▄·▄▄█▄·● beats 8/8 very hard · avg 11% 0.5% at its level
FrontierMath: Erdős problems 0.0% ●··········▆ beats 0/6 very hard · avg 0.0% 0.2% sharp · too hard for it · noisy
OTIS mock AIME 2024–2025 84.4% ▃▃▄▃▃▃▇●█▃▂▅ beats 125/197 very hard · avg 7.2% 0.1% sharp · maxed out · noisy
vision & documents · #22 of 153 · 3.1% of its score
Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [Muse Spark 1.2](muse-spark-1-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1282.2 ▁▁▂▂▂▂▃▄▅▅█● beats 133/152 avg 1061 1.3% blunt
MMMU Pro (maker) 81.2% ▅·····▃·▃·●▃ beats 6/7 hard · avg 40% 1.1% typical
Arena document (style controlled) 1480.2 ▁▁▃▃▄▃▇█▅●▄▅ beats 30/37 avg 1331 0.7% blunt
web search · #25 of 84 · 6.1% of its score
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
K-BrowseComp-Verified (Korean web… 45.7% ▃····█··▃█·● beats 8/8 very hard · avg 3.6% 1.4% at its level
BrowseComp (maker) 84.4% ▄··█●··▄·▄█▄ beats 3/8 medium · avg 38% 1.4% typical
Humanity's Last Exam with tools (… 52.2% ▃·▃·▅····●·█ beats 5/8 very hard · avg 12% 1.3% at its level
Arena search systems (style contr… 1226.5 ▁·▁▁▂▂▂█▆●▂▂ beats 30/36 avg 1069 0.7% blunt
Legal Research Bench (agentic, al… 40.0% ▄█··▄▄▄██·▄● beats 11/12 very hard · avg 6.3% 0.7% at its level
RWSearch 85.3% ████··█····● beats 5/5 medium · avg 45% 0.6% typical
other tests (22)
test result vs the others in its run difficulty counts why
EBR-bench (Epoch) 34.3% ▄▂·▆█▂█●▄▆·▆ beats 16/25 very hard · avg 0.3% 4.7% sharp · at its level
Mystery Game Puzzles (Epoch) 56.0% █··▂▃▇▄█▂●▂▁ beats 69/77 very hard · avg 9.4% 4.1% sharp · at its level · noisy
Furniture Assembly (Epoch) 44.2% █▆▆▆●▂▆▂▂▂▂▅ beats 20/32 very hard · avg 1.2% 3.6% sharp · at its level
Humanity's Last Exam (maker) 41.4% ▃··▃·▃▃···▃● beats 5/7 very hard · avg 4.4% 2.3% at its level
Chess Puzzles (Epoch) 26.0% █▁▁▁▁▂▃●▃▂▁▁ beats 99/137 very hard · avg 5.8% 2.1% sharp · at its level · noisy
EQ-Bench 3 (emotional intelligenc… 1327.9 ▁·▂▁▄▅▅▃█●▁▁ beats 56/62 avg 1034 1.2% typical
Legal Research Bench (single-shot… 6.8% ▃▃█▃·▅···●▃▃ beats 10/12 very hard · avg 0.8% 1.1% too hard for it
The Token Games Elo (10-model puz… 1167.0 █··▄█··▄·▄·● beats 7/7 avg 822 1.0% typical
WildClawBench 58.2% ▃·▅▅█·▅█▅▃●▃ beats 16/17 very hard · avg 11% 1.0% at its level
GPQA Diamond 90.7% ▁▁▁▁▂▄▆▄▅▇●▅ beats 178/211 hard · avg 39% 0.8% sharp · maxed out
SimpleQA Verified 63.0% ▁▁▃▂▃▃▆▆█▄●▅ beats 69/81 very hard · avg 8.0% 0.8% at its level
HealthBench Professional (maker) 51.8% ▄·●··▄▄·▄█·█ beats 1/8 hard · avg 20% 0.7% blunt · at its level
FORTE (AGI-Eval) 77.8% ▄···█·····▄● beats 5/5 medium · avg 38% 0.6% typical
MirrorCode (Epoch) 10.0% ▄·●▄▄▄██·▄▄█ beats 2/13 very hard · avg 1.6% 0.6% sharp · at its level · noisy
WritingBench 84.7% ▁·▁▂▁··▅●▂▂▁ beats 10/20 medium · avg 54% 0.6% typical
SCHEDBench subset, solver-mediate… 53.3% ▄·▄·····▄··● beats 3/4 very hard · avg 5.4% 0.5% at its level · noisy
HealthBench Hard (maker) 33.8% ▂··▂····▂▄·● beats 6/8 very hard · avg 9.6% 0.5% blunt · at its level
CyberGym (maker) 81.8% ▄···▄··▄█▄●▄ beats 6/8 hard · avg 34% 0.4% small run
HealthBench (maker) 58.4% ▃·▃·····█▃▃● beats 7/8 hard · avg 24% 0.4% blunt · at its level
RobloxQA v2.0 multiple choice (To… 97.5% ▁▁▃▁█▄▃▃▆●▂▂ beats 43/49 easy · avg 77% 0.4% maxed out
NTCIR SciClaimEval Subtask 1 pair… 69.9% ▃▃···▃·▃▃●·▃ beats 6/8 medium · avg 45% 0.3% blunt · at its level
Vectara hallucination leaderboard… 90.7% ▁▂▁▂▅●▂▃▄▃▁▁ beats 55/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 11 31% ▇▇▇▇
MathArena ArXivMath / BrokenArXiv / ArX… 12 20% ▇▇
OpenAI vendor-reported benchmark scores… 17 17% ▇▇
LMArena leaderboard dataset 6 13% ▇▇
meituan-longcat/LongCat-2.0 model card 5 3% ▇
Agents' Last Exam 1 2% ▇
Legal Research Bench 2 2% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 1 1% ▇
Roblox LLM Leaderboard 2 1% ▇
K-BrowseComp: A Web Browsing Agent Benc… 1 1% ▇
E2E-SWE: Benchmarking LLMs on Building … 1 1% ▇
EQ-Bench 3 canonical leaderboard results 1 1% ▇
The Token Games: Evaluating Language Mo… 1 1% ▇
WildClawBench: A Benchmark for Real-Wor… 1 1% ▇
zai-org/GLM-5.2 model card 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 1% ▇
Improving Natural-Language Combinatoria… 1 1% ▇
MathConstraint: Automated Generation of… 1 0% ▇
TerminalWorld: Benchmarking Agents on R… 1 0% ▇
SciTrue: Reliable Scientific Claim Vali… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 78.1
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · meituan-longcat/LongCat-2.0 model card ([mit](../../licenses/meituan-longcat--LongCat-2.0.txt)) · Agents' Last Exam ([CC BY 4.0](../../licenses/paper-2606.05405v2-CC-BY-4.0.txt)) · Legal Research Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · The Token Games: Evaluating Language Model Reasoning with Puzzle Duels ([CC BY-SA 4.0](../../licenses/paper-2602.17831v2-CC-BY-SA-4.0.txt)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · zai-org/GLM-5.2 model card ([mit](../../licenses/zai-org--GLM-5.2.txt)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathConstraint: Automated Generation of Verified Combinatorial Reasoning Instances for LLMs ([CC BY 4.0](../../licenses/paper-2605.08498v1-CC-BY-4.0.txt)) · TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks ([CC BY 4.0](../../licenses/paper-2605.22535v1-CC-BY-4.0.txt)) · SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task ([CC BY 4.0](../../licenses/paper-2609.00654v1-CC-BY-4.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (OpenAI) · $11.25/M blended · $5 in / $30 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-5.5.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-5.5.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)