[whatmodel.sh](../../README.md) / GPT-5.4
# GPT-5.4
OpenAI · released 2026-03 · closed weights
**score 75.9** ±0.5 · #25 of 732 · from 55 benchmark results across 20 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-5.4 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #33 of 313 24% 16
coding ▁▁▁▃▅▇█▇▅●▃▁ #64 of 469 8.9% 4
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #30 of 402* 2.3% 1
math ▁▁▃▅█▆▄█▄●▁▁ #20 of 431 30% 10
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #25 of 153 4.1% 4
web search ▁▁▁▅▄●▃▄·▁·▁ #27 of 84 4.2% 3
agent tasks · #33 of 313 · 24% of its score
Beats [GPT-5.6 Terra](gpt-5-6-terra.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [GLM-5.3](glm-5-3.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0136 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 33/59 avg -0.379 IPS 5.9% sharp
SWE-bench Verified 76.9% ▁··▁·▁▂▁▄●▄▁ beats 23/29 hard · avg 19% 3.1% sharp
Agents' Last Exam overall full-pa… 20.5% ▄···█▄▄▄█▄·● beats 9/10 very hard · avg 1.9% 2.1% typical
BrowseComp (maker) 82.7% ▄··●▄··▄·▄█▄ beats 1/8 medium · avg 38% 1.8% typical
τ³-bench banking_knowledge (v1.0.… 39.4% ▃▃▂▃·▆▃▂●▅▅▂ beats 16/25 very hard · avg 4.3% 1.6% at its level
Humanity's Last Exam with tools (… 52.1% ▃·▃·▅····●·█ beats 4/8 very hard · avg 12% 1.5% at its level
Toolathlon (Tool Decathlon) (make… 54.6% ▃··▃··▃··▃●▅ beats 6/8 very hard · avg 13% 1.5% at its level
Terminal-Bench 2.0 (maker) 75.1% ██···██··●██ beats 4/6 hard · avg 15% 1.2% at its level
SWE Atlas (Codebase Q&A, Test Wri… 38.0% ▄·▄··█···▄·● beats 5/6 very hard · avg 6.6% 1.1% at its level
Thinkingbox-bench pass@1 (507 sta… 65.4% ▅··▃·▃·█▃▅█● beats 14/15 very hard · avg 5.1% 1.0% sharp · at its level
OSWorld-Verified (maker) 75.0% █▄·····▄·▄●▄ beats 5/6 hard · avg 16% 0.9% at its level
KlinikeBench strict pass@1 (333 i… 23.4% ▁▁·▁▁▁▁▅▃█▅● beats 23/27 very hard · avg 2.4% 0.7% typical
EEGAgentBench overall (tool-based… 43.8% ▂·▁▁··●▇█▂·▁ beats 7/25 hard · avg 28% 0.7% blunt · at its level
SWE-bench Pro (maker) 57.7% ▄·····▄·▄▄▄● beats 5/6 hard · avg 29% 0.6% blunt · at its level
τ²-bench Telecom (maker) 92.8% ▃·▅▅▃▃··●▃▃▅ beats 8/13 hard · avg 16% 0.4% sharp · noisy
T1-Bench multi-domain conversatio… 20.9% ▂▂··▂·●·▂▂█▄ beats 3/11 hard · avg 25% 0.0% blunt · at its level · noisy
coding · #64 of 469 · 8.9% of its score
Trails [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md), [MiniMax-M3](minimax-m3.md), [GLM-5.1](glm-5-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1463.5 ▁▁▃▂▅█●▆▅▅▁▂ beats 65/129 avg 981 4.1% sharp
SWE-bench Verified 76.9% ▁··▁·▁▂▁▄●▄▁ beats 23/29 hard · avg 19% 3.1% sharp
SWE Atlas (Codebase Q&A, Test Wri… 38.0% ▄·▄··█···▄·● beats 5/6 very hard · avg 6.6% 1.1% at its level
SWE-bench Pro (maker) 57.7% ▄·····▄·▄▄▄● beats 5/6 hard · avg 29% 0.6% blunt · at its level
chat & writing · #30 of 402 · 2.3% of its score
Beats [GPT-6 Sol](gpt-6-sol.md), [GPT-5.6 Terra](gpt-5-6-terra.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [GLM-5.3-Flash](glm-5-3-flash.md), [Gemini 3.5 Flash](gemini-3-5-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1475.3 ▁▁▁▂▃▃▃▆▆▇●▃ beats 352/385 avg 1260 2.3% blunt
math · #20 of 431 · 30% 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) here, though they rank higher overall. Trails [GPT-5.6 Luna](gpt-5-6-luna.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 49.0% ▁▂▁▂█●▂▃▂▁▁▁ beats 49/67 very hard · avg 0.1% 10% sharp · at its level
FrontierMath tiers 1–3 v2 78.6% ▂··▁▁▃▄▄█●▄▃ beats 68/86 very hard · avg 1.2% 8.8% sharp · at its level
MathArena ArXivMath February 2026… 59.4% ▁▁▁▁█▇▄▃●▃·▃ beats 20/24 very hard · avg 9.4% 2.7% at its level
MathArena ArXivMath March 2026 (3… 67.8% █▃▅·█▃▃·●·▃▃ beats 11/14 hard · avg 16% 2.0% at its level
MathArena ArXivMath January 2026 … 76.1% ▂··▄▂▄█▆▆▄▄● beats 23/23 hard · avg 23% 1.6% at its level
MathArena ArXivMath December 2025… 60.3% ▃·▃▃█▁·▄·▁●▁ beats 17/18 hard · avg 19% 1.3% at its level
MathArena BrokenArXiv March 2026 … 36.6% █·▆█▂·▂●···▂ beats 13/14 very hard · avg 1.0% 1.1% sharp · at its level · noisy
MathArena BrokenArXiv February 20… 37.9% ▃▂▂▃█▃▂·●··▂ beats 15/16 very hard · avg 0.9% 1.1% sharp · at its level · noisy
MathArena ArXivLean March 2026 (4… 17.1% ▂····▂·····● beats 5/5 very hard · avg 1.2% 0.9% typical
OTIS mock AIME 2024–2025 95.3% ▃▃▄▃▃▃▇▇█●▂▅ beats 160/197 very hard · avg 7.2% 0.2% sharp · maxed out · noisy
vision & documents · #25 of 153 · 4.1% 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 [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) 1284.1 ▁▁▂▂▂▂▃▄▅▅█● beats 135/152 avg 1061 1.5% blunt
MMMU Pro (maker) 81.2% ▅·····▃·▃·●▃ beats 5/7 hard · avg 40% 1.5% typical
Arena document (style controlled) 1470.1 ▁▁▃▃▄▃▇█●▇▄▅ beats 24/37 avg 1331 0.8% blunt
SpaceCast-Bench predictive spatia… 37.7% ▃·▅▃●▃··▅··█ beats 4/11 very hard · avg 33% 0.3% at its level · noisy
web search · #27 of 84 · 4.2% of its score
Beats [Claude Opus 4.8](claude-opus-4-8.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) 82.7% ▄··●▄··▄·▄█▄ beats 1/8 medium · avg 38% 1.8% typical
Humanity's Last Exam with tools (… 52.1% ▃·▃·▅····●·█ beats 4/8 very hard · avg 12% 1.5% at its level
Arena search systems (style contr… 1196.3 ▁·▁▁▂▂▂●▆▃▂▂ beats 20/36 avg 1069 0.8% blunt
other tests (22)
test result vs the others in its run difficulty counts why
EBR-bench (Epoch) 25.4% ▄▂·▆█▂●▆▄▆·▆ beats 12/25 very hard · avg 0.3% 4.7% sharp · at its level
Mystery Game Puzzles (Epoch) 37.0% █··▂▃▇▄●▂▄▂▁ beats 60/77 very hard · avg 9.4% 4.2% sharp · at its level · noisy
Furniture Assembly (Epoch) 37.5% █▆▆●▆▂▆▂▂▂▂▅ beats 14/32 very hard · avg 1.2% 4.1% sharp · at its level
Humanity's Last Exam (maker) 39.8% ▃··▃·▃▃···●█ beats 4/7 very hard · avg 4.4% 2.6% at its level
ARC-AGI-2 public evaluation set (… 75.8% █··▂▅▁▃▁▃·▁● beats 22/24 very hard · avg 0.3% 2.6% sharp · at its level · noisy
Chess Puzzles (Epoch) 44.0% █▁▁▁▁▂▃▃▃●▁▁ beats 124/137 very hard · avg 5.8% 2.3% sharp · at its level · noisy
KINA (Knowledge Index of Noah's A… 48.5% ▁▁▂▁▃▄█▆▄▅▂● beats 37/39 very hard · avg 21% 2.0% at its level
DSEffi-Bench Pass (1,000 data-sci… 66.9% ▅··▃▅·█·▃██● beats 15/15 hard · avg 23% 1.5% at its level
EQ-Bench 3 (emotional intelligenc… 1328.0 ▁·▂▁▄▅▅▃█●▁▁ beats 57/62 avg 1034 1.4% typical
GPQA Diamond 93.3% ▁▁▁▁▂▄▆▄▅▇█● beats 197/211 hard · avg 39% 1.3% sharp
WildClawBench 50.3% ▃·▅▅█·▅█●▃▃▃ beats 14/17 very hard · avg 11% 1.2% at its level
AgentWorldBench (open-ended, over… 58.3% ▄▄··▄▄·▄█▄█● beats 11/11 hard · avg 26% 1.1% blunt · at its level
SimpleQA Verified 45.1% ▁▁▃▂▃▃▆●█▄▂▅ beats 42/81 very hard · avg 8.0% 0.9% at its level
HealthBench Professional (maker) 48.1% ●·▄··▄▄·▄█·█ beats 0/8 hard · avg 20% 0.8% blunt · at its level
ARC-AGI-1 public evaluation set (… 95.6% ▁·▇▃▁·█▇▁·●▃ beats 25/27 very hard · avg 4.0% 0.7% sharp · noisy
MirrorCode (Epoch) 15.6% ▄·█●▄▄██·▄▄█ beats 3/13 very hard · avg 1.6% 0.7% sharp · at its level · noisy
CyberGym (maker) 79.0% ▄···▄··▄█●█▄ beats 5/8 hard · avg 34% 0.5% small run
HealthBench Hard (maker) 30.3% ▂··▂····●▄·█ beats 2/8 very hard · avg 9.6% 0.5% blunt · at its level
HealthBench (maker) 55.7% ▃·▃·····●▃▃▅ beats 4/8 hard · avg 24% 0.5% blunt · at its level
DiagnosticIQ industrial maintenan… 73.3% ▁··▃▁▁▁▅█▅▁● beats 23/24 hard · avg 51% 0.4% blunt · at its level
FrontierScience-Research (maker) 33.0% ██·······●·█ beats 2/3 very hard · avg 8.1% 0.4% at its level · small run
Vectara hallucination leaderboard… 93.0% ▁▂▁▂▅█▂●▄▃▁▁ beats 70/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 11 41% ▇▇▇▇▇
LMArena leaderboard dataset 6 15% ▇▇
OpenAI vendor-reported benchmark scores… 14 15% ▇▇
MathArena ArXivMath / BrokenArXiv / ArX… 7 11% ▇
ARC Prize Foundation model_baseline out… 2 3% ▇
Agents' Last Exam 1 2% ▇
Knowledge Index of Noah's Ark 1 2% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 1 2% ▇
DSEffi-Bench: Demystifying Large Langua… 1 2% ▇
EQ-Bench 3 canonical leaderboard results 1 1% ▇
WildClawBench: A Benchmark for Real-Wor… 1 1% ▇
Qwen/Qwen-AgentWorld-35B-A3B model card 1 1% ▇
SWE Atlas: Benchmarking Coding Agents B… 1 1% ▇
One Success Isn't Reliability: Thinking… 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 1% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 0% ▇
SpaceCast-Bench: Evaluating Predictive … 1 0% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 75.9
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/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · Agents' Last Exam ([CC BY 4.0](../../licenses/paper-2606.05405v2-CC-BY-4.0.txt)) · Knowledge Index of Noah's Ark ([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)) · DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation ([CC BY 4.0](../../licenses/paper-2608.30248v1-CC-BY-4.0.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen-AgentWorld-35B-A3B model card ([Apache-2.0](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/60d2b0434a53d2e62a7c00a489586815d94ebffb/README.md)) · SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · One Success Isn't Reliability: Thinkingbox ([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/)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://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/)) · SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (OpenAI) · $5.63/M blended · $2.5 in / $15 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-5.4.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-5.4.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-4) · [JSON](https://whatmodel.sh/api?format=json&all=1)