[whatmodel.sh](../../README.md) / GPT-5.4 Pro # GPT-5.4 Pro OpenAI · released 2026-03 · proprietary **score 78.8** ±0.9 · #16 of 732 · from 12 benchmark results across 5 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 Pro among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  ▁▁▃▆█▄▅▅▄▄●▁   #16 of 313*     11%      2
math         ▁▁▃▅█▆▄█▄●▁▁    #16 of 431     62%      3
web search   ▁▁▁▅▄█▃●·▁·▁    #16 of 84*     11%      2
agent tasks · #16 of 313 · 11% of its score 
test                                result  vs the others in its run  difficulty           counts  why
Humanity's Last Exam with tools (…   58.7%  ▃·▃·▅····▅·● beats 8/8    very hard · avg 12%    5.3%  at its level
BrowseComp (maker)                   89.3%  ▄··█▄··▄·●█▄ beats 5/8    medium · avg 38%       5.3%  typical
math · #16 of 431 · 62% of its score Trails [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.5](gpt-5-5.md), [Muse Wonder](muse-wonder.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
FrontierMath tier 4 v2               58.5%  ▁▂▁▂█▅●▃▂▁▁▁ beats 52/67  very hard · avg 0.1%     32%  sharp · at its level
FrontierMath tiers 1–3 v2            82.5%  ▂··▁▁▃▄▄█▆●▃ beats 73/86  very hard · avg 1.2%     21%  sharp
MathArena ArXivMath February 2026…   75.8%  ▁▁▁▁█▇▄▃▁▃·● beats 24/24  very hard · avg 9.4%    8.7%  at its level
web search · #16 of 84 · 11% 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
Humanity's Last Exam with tools (…   58.7%  ▃·▃·▅····▅·● beats 8/8    very hard · avg 12%    5.3%  at its level
BrowseComp (maker)                   89.3%  ▄··█▄··▄·●█▄ beats 5/8    medium · avg 38%       5.3%  typical
other tests (7) 
test                                result  vs the others in its run    difficulty            counts  why
Humanity's Last Exam (maker)         42.7%  ▃··▃·▃▃···▃● beats 6/7      very hard · avg 4.4%    9.4%  at its level
Chess Puzzles (Epoch)                58.6%  █▁▁▁▁▂▃▃▃▂●▁ beats 133/137  very hard · avg 5.8%    8.7%  sharp · at its level · noisy
GPQA Diamond                         94.6%  ▁▁▁▁▂▄▆▄▅▇█● beats 206/211  hard · avg 39%          3.1%  sharp · maxed out
SimpleQA Verified                    46.3%  ▁▁▃▂▃▃▆▆●▄▂▅ beats 45/81    very hard · avg 8.0%    3.0%  at its level
ARC-AGI-1 public evaluation set (…   98.3%  ▁·▇▃▁·█▇▁·▇● beats 27/27    very hard · avg 4.0%    1.4%  sharp · maxed out · noisy
FrontierScience-Research (maker)     36.7%  ██·······█·● beats 3/3      very hard · avg 8.1%    1.3%  at its level · small run
Vectara hallucination leaderboard…   91.7%  ▁▂▁▂▅█●▃▄▃▁▁ beats 60/98    very easy · avg 90%     0.0%  blunt
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Epoch AI                                        5   68% ▇▇▇▇▇▇▇▇
OpenAI vendor-reported benchmark scores…        4   21% ▇▇▇
MathArena ArXivMath / BrokenArXiv / ArX…        1    9% ▇
ARC Prize Foundation model_baseline out…        1    1% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 78.8
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)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### Through an API api (OpenAI) · $67.50/M blended · $30 in / $180 out per 1M tokens · [developers.openai.com](https://developers.openai.com/api/docs/models/gpt-5.4-pro) [price source](https://developers.openai.com/api/docs/models/gpt-5.4-pro): USD per 1M tokens, OpenAI API standard processing, model page 'gpt-5.4-pro' whose default snapshot is gpt-5.4-pro-2026-03-05: input $30, output $180; cached-input, Batch and other tiers excluded. Prompts over 272K input tokens are billed at 2x input and 1.5x output (excluded). Currency: USD. OpenAI official model page, observed 2026-09-28. · [context source](https://developers.openai.com/api/docs/models/gpt-5.4-pro): OpenAI model page for gpt-5.4-pro (default snapshot gpt-5.4-pro-2026-03-05): 1,050,000 context window. Observed 2026-09-28. [interactive version](https://whatmodel.sh/model/gpt-5-4-pro) · [JSON](https://whatmodel.sh/api?format=json&all=1)