[whatmodel.sh](../../README.md) / GPT-5.4 mini
# GPT-5.4 mini
OpenAI · released 2026-03 · closed weights
**score 70.1** ±0.6 · #79 of 732 · from 30 benchmark results across 12 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 mini among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #68 of 313 17% 10
coding ▁▁▁▃▅▇█▇●▄▃▁ #95 of 469* 8.8% 2
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #57 of 402* 4.3% 1
math ▁▁▃▅█▆▄█●▂▁▁ #60 of 431 32% 3
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #46 of 153* 6.2% 2
web search ▁▁▁●▄█▃▄·▁·▁ #64 of 84 6.5% 3
agent tasks · #68 of 313 · 17% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [GPT-5 Pro](gpt-5-pro.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [GLM-5.1](glm-5-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 41.5% ▃·▃·●····▅·█ beats 2/8 very hard · avg 12% 2.8% at its level
Toolathlon (Tool Decathlon) (make… 42.9% ▃··▃··●··▃█▅ beats 2/8 very hard · avg 13% 2.7% at its level
Terminal-Bench 2.0 (maker) 60.0% ██···●█··███ beats 2/6 hard · avg 15% 2.6% at its level
K-BrowseComp-Verified (Korean web… 30.7% ▃····█··▃●·▃ beats 7/8 very hard · avg 3.6% 2.5% typical
OSWorld-Verified (maker) 72.1% █▄·····▄·●▄▄ beats 4/6 hard · avg 16% 1.9% at its level
τ²-bench Telecom (maker) 93.4% ▃·▅▅▃▃··●▃▃▅ beats 9/13 hard · avg 16% 1.4% sharp · noisy
Legal Research Bench (agentic, al… 12.6% ●█··▄▄▄██·▄█ beats 0/12 very hard · avg 6.3% 1.2% typical
SWE-bench Pro (maker) 54.4% ▄·····▄·●▄▄█ beats 2/6 hard · avg 29% 1.1% blunt · at its level
KlinikeBench strict pass@1 (333 i… 15.9% ▁▁·▁▁▁▁▅▃●▅█ beats 15/27 very hard · avg 2.4% 1.0% typical
T1-Bench multi-domain conversatio… 14.1% ▂▂··●·▂·▂▂█▄ beats 2/11 hard · avg 25% 0.0% blunt · at its level · noisy
coding · #95 of 469 · 8.8% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md), [o4-mini](o4-mini.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1397.3 ▁▁▃▂▅●▃▆▅▅▁▂ beats 43/129 avg 981 7.6% sharp
SWE-bench Pro (maker) 54.4% ▄·····▄·●▄▄█ beats 2/6 hard · avg 29% 1.1% blunt · at its level
chat & writing · #57 of 402 · 4.3% of its score
Beats [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Gemma 4 31B](gemma-4-31b.md), [Claude Sonnet 4.5](claude-sonnet-4-5.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
LMArena (text) 1447.2 ▁▁▁▂▃▃▃▆▆▇●▃ beats 314/385 avg 1260 4.3% blunt
math · #60 of 431 · 32% of its score
Beats [Qwen3.6 Max Preview](qwen3-6-max-preview.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md), [DeepSeek V4 Pro (Preview, Apr 2026)](deepseek-v4-pro-preview.md) here, though they rank higher overall. Trails [INTELLECT-3](intellect-3.md), [A.X K2 (688B-A33B)](ax-k2.md), [Inkling Small (266B-A12B)](inkling-small.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 51.2% ▂··▁▁▃▄▄●▆▄▃ beats 36/86 very hard · avg 1.2% 21% sharp · at its level
FrontierMath tier 4 v2 9.8% ▁▂▁●█▅▂▃▂▁▁▁ beats 11/67 very hard · avg 0.1% 10% sharp
OTIS mock AIME 2024–2025 88.9% ▃▃▄▃▃▃▇▇●▃▂▅ beats 142/197 very hard · avg 7.2% 1.4% sharp · noisy
vision & documents · #46 of 153 · 6.2% of its score
Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [Kimi K2.5](kimi-k2-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MMMU Pro (maker) 76.6% ▅·····●·▃·█▃ beats 2/7 hard · avg 40% 3.3% at its level
Arena vision (style controlled) 1250.4 ▁▁▂▂▂▂▃▄▅▅●▇ beats 107/152 avg 1061 2.9% blunt
web search · #64 of 84 · 6.5% of its score
Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.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
Humanity's Last Exam with tools (… 41.5% ▃·▃·●····▅·█ beats 2/8 very hard · avg 12% 2.8% at its level
K-BrowseComp-Verified (Korean web… 30.7% ▃····█··▃●·▃ beats 7/8 very hard · avg 3.6% 2.5% typical
Legal Research Bench (agentic, al… 12.6% ●█··▄▄▄██·▄█ beats 0/12 very hard · avg 6.3% 1.2% typical
other tests (13)
test result vs the others in its run difficulty counts why
GPQA Diamond 86.9% ▁▁▁▁▂▄▆▄▅▇●▅ beats 154/211 hard · avg 39% 4.5% sharp
Humanity's Last Exam (maker) 28.2% ▃··▃·●▃···▃█ beats 2/7 very hard · avg 4.4% 4.1% at its level
Mystery Game Puzzles (Epoch) 7.0% ●··▂▃▇▄█▂▄▂▁ beats 9/77 very hard · avg 9.4% 3.7% sharp · noisy
ARC-AGI-2 public evaluation set (… 0.8% ●··▂▅▁▃▁▃·▁▃ beats 6/24 very hard · avg 0.3% 3.7% sharp · noisy
DSEffi-Bench Pass (1,000 data-sci… 62.2% ▅··▃▅·█·▃█●▃ beats 14/15 hard · avg 23% 3.1% at its level
Chess Puzzles (Epoch) 24.0% █▁▁▁▁▂▃●▃▂▁▁ beats 94/137 very hard · avg 5.8% 2.8% sharp · noisy
ARC-AGI-1 public evaluation set (… 66.3% ▁·▇▃▁·█●▁·▇▃ beats 15/27 very hard · avg 4.0% 2.7% sharp · at its level · noisy
The Token Games Elo (10-model puz… 1068.0 █··▄●··▄·▄·▄ beats 4/7 avg 822 2.3% typical
SimpleQA Verified 29.4% ▁▁▃▂▃●▆▆█▄▂▅ beats 18/81 very hard · avg 8.0% 1.6% at its level
Legal Research Bench (single-shot… 2.2% ●▃█▃·▅···█▃▃ beats 0/12 very hard · avg 0.8% 1.4% too hard for it
SkillsBench 87-task subset 44.8% ▄··▄▄▄···●·█ beats 5/7 very hard · avg 9.1% 1.3% at its level
WideSearch corrected dataset 70.2% ▃······▃▃●█▃ beats 3/7 hard · avg 22% 1.2% at its level · noisy
Vectara hallucination leaderboard… 94.5% ▁▂▁▂▅█▂▃●▃▁▁ beats 81/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 7 45% ▇▇▇▇▇
OpenAI vendor-reported benchmark scores… 8 20% ▇▇
LMArena leaderboard dataset 3 15% ▇▇
ARC Prize Foundation model_baseline out… 2 6% ▇
DSEffi-Bench: Demystifying Large Langua… 1 3% ▇
Legal Research Bench 2 3% ▇
inclusionAI/Ling-3.0-flash model card 2 2% ▇
K-BrowseComp: A Web Browsing Agent Benc… 1 2% ▇
The Token Games: Evaluating Language Mo… 1 2% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 70.1
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/)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · 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)) · Legal Research Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · inclusionAI/Ling-3.0-flash model card ([MIT](../../licenses/cards-final-NOTICE.txt)) · K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([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) · $1.69/M blended · $0.75 in / $4.5 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-mini.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-mini.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-4-mini) · [JSON](https://whatmodel.sh/api?format=json&all=1)