[whatmodel.sh](../../README.md) / GPT-5
# GPT-5
OpenAI · released 2025-08 · closed weights
**score 71.2** ±0.5 · #66 of 732 · from 54 benchmark results across 28 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 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #85 of 313 19% 10
coding ▁▁▁▃▅▇█▇●▄▃▁ #83 of 469 20% 6
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #76 of 402* 3.0% 1
math ▁▁▃▅█▆▄█●▂▁▁ #48 of 431 26% 6
vision & documents ▁▁▂▂▁▃▄▂●▇█▄ #74 of 153 7.8% 8
web search ▁▁▁▅▄●▃▄·▁·▁ #43 of 84* 1.1% 1
agent tasks · #85 of 313 · 19% of its score
Trails [MiniMax-M2.7](minimax-m2-7.md), [Step 3.5 Flash (196B-A11B)](step-3-5-flash.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 73.6% ▁··▁·▁▂▁▄●▄▁ beats 13/29 hard · avg 19% 5.6% sharp
Terminal-Bench 2.0 with Terminus … 35.2% ▃··▃·▃▃██●▃▅ beats 10/14 very hard · avg 6.1% 2.7% sharp · at its level
BrowserGym WorkArena-L2 (AgentLab… 69.4% ▃▅▃·▅▃▃··█·● beats 11/11 very hard · avg 3.6% 2.4% sharp · at its level · noisy
τ²-bench Telecom (maker) 96.7% ▃·▅▅▃▃··█▃●▅ beats 11/13 very hard · avg 13% 2.3% sharp · several runs
ContractBench success rate (33 ob… 70.7% ▆··▆▄▂▄▂▆▆●▄ beats 21/23 very hard · avg 13% 2.2% sharp · at its level
τ²-bench Retail 81.6% ▁····▂▁▃▆█▃● beats 43/44 medium · avg 40% 1.3% typical
τ²-bench Airline 62.5% ▁▁·▂▁▃▂▁█●▂▃ beats 35/42 hard · avg 28% 1.1% at its level
BrowserGym MiniWoB (AgentLab Gene… 71.5% ▅····▅▅▃▃·▃● beats 11/11 medium · avg 53% 0.9% blunt
BFCL V4 (2025-11-03 archive, mode… 61.6% ▃··▅▃▃·▅·●·█ beats 7/10 hard · avg 33% 0.6% at its level
τ²-Bench-Verified (GPT-5.1 user) 79.9% ▂··▄···▂▄▄●▄ beats 10/13 medium · avg 45% 0.4% typical
coding · #83 of 469 · 20% of its score
Beats [GPT-5.2](gpt-5-2.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [GLM-4.7](glm-4-7.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 73.6% ▁··▁·▁▂▁▄●▄▁ beats 13/29 hard · avg 19% 5.6% sharp
Arena WebDev (Code Arena) 1417.3 ▁▁▃▂▅●▃▆▅▅▁▂ beats 51/129 avg 981 5.3% sharp
Aider polyglot (225 exercises, tw… 88.0% ▂·▂▃▅▂▃█▆▄▂● beats 49/49 very hard · avg 7.6% 4.1% sharp
Luau Leetcode fully solved (Torpe… 78.8% ▁▁▁▁·▁▂▃▅▃●▇ beats 31/49 hard · avg 17% 2.4% sharp · at its level
VisEditBench strict pass rate (1,… 65.7% ▄··█·▄··▄●·▄ beats 6/7 hard · avg 33% 1.4% at its level
WebApp1K-Duo-React pass@1 (onekq-… 79.4% ▁··▁·▁▁▁▆█▇● beats 45/45 hard · avg 29% 1.2% typical
chat & writing · #76 of 402 · 3.0% of its score
Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md), [Gemma 4 31B](gemma-4-31b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1434.7 ▁▁▁▂▃▃▃▆▆▇●▃ beats 294/385 avg 1260 3.0% blunt
math · #48 of 431 · 26% of its score
Beats [Grok 4.7](grok-4-7.md), [GLM-5.3-Flash](glm-5-3-flash.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 55.4% ▂··▁▁▃▄▄●▆▄▃ beats 39/86 very hard · avg 1.2% 14% sharp · at its level
FrontierMath tier 4 v2 22.0% ▁▂▁▂●▅▂▃▂▁▁▁ beats 22/67 very hard · avg 0.1% 8.5% sharp
MathNet-Solve-Test micro-average … 69.3% ▃▃···█▃·▃·●▃ beats 7/8 very hard · avg 15% 1.0% at its level
OTIS mock AIME 2024–2025 91.4% ▃▃▄▃▃▃▇▇●▃▂▅ beats 150/197 very hard · avg 7.2% 0.8% sharp · noisy
MATH level 5 98.1% ▂▂▅▆▄█▆▃▅▂▃● beats 92/92 hard · avg 28% 0.7% sharp · maxed out
FormalProofBench (200 graduate-le… 17.0% ▃▅·▃··▅▃●··▃ beats 7/10 very hard · avg 3.1% 0.6% noisy
vision & documents · #74 of 153 · 7.8% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [Qwen3.5 9B](qwen3-5-9b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Claude Haiku 4.5](claude-haiku-4-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1210.7 ▁▁▂▂▂▂▃▄▅●█▇ beats 78/152 avg 1061 2.0% blunt
ChartArena chart parsing (English… 45.6% ▁·▁·▃▃▁▇▃●·▄ beats 16/22 hard · avg 27% 1.6% blunt · at its level
VisEditBench strict pass rate (1,… 65.7% ▄··█·▄··▄●·▄ beats 6/7 hard · avg 33% 1.4% at its level
CharXiv descriptive questions 91.3% ▄····▄···█●▄ beats 4/5 easy · avg 69% 1.2% maxed out · several runs
CharXiv (reasoning questions) 71.5% ▄·····▄▄·▄·● beats 5/5 hard · avg 31% 0.9% typical
Molmo2-VideoCount accuracy (val) 35.8% ▃··▃·▃····●▃ beats 5/6 hard · avg 22% 0.3% blunt · at its level
Ai2 Molmo2 human preference Elo (… 1031.0 ▄··●··▄··▄█▄ beats 1/6 avg 968 0.2% blunt
Molmo2-CapTest dense video captio… 50.1% █···█·███●·█ beats 5/6 hard · avg 31% 0.1% blunt · at its level
web search · #43 of 84 · 1.1% of its score
Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [Qwen3.7 Max](qwen3-7-max.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), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena search systems (style contr… 1187.7 ▁·▁▁▂▂▂●▆▃▂▂ beats 12/36 avg 1069 1.1% blunt
other tests (21)
test result vs the others in its run difficulty counts why
GPQA Diamond 86.2% ▁▁▁▁▂▄▆▄▅▇●▅ beats 152/211 hard · avg 38% 6.8% sharp · several runs
EBR-bench (Epoch) 12.7% ▄▂·▆●▂█▆▄▆·▆ beats 6/25 very hard · avg 0.3% 3.7% sharp
Mystery Game Puzzles (Epoch) 23.0% █··▂▃▇●█▂▄▂▁ beats 40/77 very hard · avg 9.4% 3.1% sharp · noisy
KCL-Essay vanilla 57.5% ▁···▁▃▂█▂▃▃● beats 22/23 hard · avg 17% 2.3% at its level
Chess Puzzles (Epoch) 37.0% █▁▁▁▁▂▃▃●▂▁▁ beats 114/137 very hard · avg 5.8% 2.2% sharp · noisy
KCL-MCQA vanilla 53.9% ▂···█▂▆▆██●▂ beats 20/23 very hard · avg 29% 1.4% at its level
RPC-Bench overall, text input (Z.… 37.5% ▄··▄▄·▄▄█·▄● beats 8/8 very hard · avg 12% 1.3% at its level
LongSocialBench (full-context, 4-… 52.9% ▆▂·▄█·▂▂·▄●▂ beats 15/16 very hard · avg 36% 1.2% blunt · at its level
AraGen-v3 3C3H score (Inception, … 84.3% ▁▁·▁▂▂▄█▄▅▄● beats 65/66 hard · avg 29% 1.2% typical
SimpleQA Verified 50.1% ▁▁▃▂▃▃▆▆●▄▂▅ beats 55/81 very hard · avg 8.0% 1.1% at its level
RobloxQA v2.0 multiple choice (To… 94.4% ▁▁▃▁█▄●▃▆▂▂▂ beats 27/49 easy · avg 77% 0.9% typical
MMMLU (maker) 89.4% ▂▁▁▂▃▂▄▄▅██● beats 44/46 medium · avg 64% 0.9% small run
SimpleQA (maker) 55.0% ▄▄▄▆█▆███·▂● beats 32/32 very hard · avg 9.2% 0.8% at its level · noisy
MirrorCode (Epoch) 4.4% ●·█▄▄▄██·▄▄█ beats 0/13 very hard · avg 1.6% 0.7% sharp · noisy
DiagnosticIQ industrial maintenan… 65.9% ▁··▃▁▁▁▅█●▁▅ beats 19/24 hard · avg 51% 0.6% blunt · at its level
SOOHAK Challenge (340 problems, A… 26.4% █·█████··█·● beats 7/7 very hard · avg 4.3% 0.5% typical
HealthBench Hard (maker) 46.2% ▁····▁▁▂█·▁● beats 13/13 hard · avg 16% 0.4% blunt · at its level · small run
SOOHAK-Mini (702 problems, Avg@3) 72.2% ▄··▄·▄·▄▄█·● beats 7/7 medium · avg 36% 0.4% blunt · at its level
Open PT-LLM Leaderboard average (… 83.4% ▃▁▁▁▁▁▁▁▂█▅● beats 271/276 medium · avg 72% 0.3% noisy
Vectara hallucination leaderboard… 98.6% ▁▁▂▃▄▄█▆▃●▂▁ beats 131/140 very easy · avg 94% 0.1% blunt · maxed out · noisy
Vectara hallucination leaderboard… 84.9% ▁▂▁●▅█▂▃▄▃▁▁ beats 10/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 11 44% ▇▇▇▇▇
LMArena leaderboard dataset 4 11% ▇
OpenAI vendor-reported benchmark scores… 2 5% ▇
Aider polyglot benchmark runs 1 4% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 3 4% ▇
Korean Canonical Legal Benchmark: Towar… 2 4% ▇
ServiceNow BrowserGym leaderboard 2 3% ▇
Roblox LLM Leaderboard 2 3% ▇
Terminal-Bench: Benchmarking Agents on … 1 3% ▇
ContractBench: Can LLM Agents Preserve … 1 2% ▇
OpenAI (submitted / maker-reported) 3 2% ▇
ChartArena: Benchmarking Chart Parsing … 1 2% ▇
VisEditBench: Can Vision-Language Model… 1 1% ▇
RPC-Bench leaderboard 1 1% ▇
ServiceNow-AI/Apriel-1.6-15b-Thinker mo… 2 1% ▇
LongSocialBench: Do Long-Context LLMs U… 1 1% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
MathNet: a Global Multimodal Benchmark … 1 1% ▇
Soohak: A Mathematician-Curated Benchma… 2 1% ▇
Baidu - ERNIE 5.0 Technical Report 1 1% ▇
Molmo2: Open Weights and Data for Visio… 3 1% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
FormalProofBench 1 1% ▇
Amazon AGI 1 0% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
Vectara hallucination leaderboard 2 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 71.2
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/)) · Aider polyglot benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs’ Legal Reasoning Capabilities ([CC BY 4.0](../../licenses/cards-final-NOTICE.txt)) · ServiceNow BrowserGym leaderboard ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · ChartArena: Benchmarking Chart Parsing across Languages ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · RPC-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · ServiceNow-AI/Apriel-1.6-15b-Thinker model card ([MIT](../../licenses/cards-final-NOTICE.txt)) · LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval ([CC BY 4.0](../../licenses/paper-2604.18584v2-CC-BY-4.0.txt)) · Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs ([CC BY-SA 4.0](../../licenses/paper-2605.09063v3-CC-BY-SA-4.0.txt)) · Baidu - ERNIE 5.0 Technical Report ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · FormalProofBench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Amazon AGI ([MIT](../../licenses/tau2-verified-MIT.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (OpenAI) · $3.44/M blended · $1.25 in / $10 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.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.toml)
[interactive version](https://whatmodel.sh/model/gpt-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)