[whatmodel.sh](../../README.md) / GPT-5.4 nano
# GPT-5.4 nano
OpenAI · released 2026-03 · closed weights
**score 67.7** ±0.7 · #105 of 732 · from 23 benchmark results across 7 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 nano among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #97 of 313 17% 7
coding ▁▁▁▃▅▇█▇●▄▃▁ #88 of 469* 6.5% 2
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #114 of 402* 5.6% 1
math ▁▁▃▅█▆▄█●▂▁▁ #64 of 431 35% 3
vision & documents ▁▁▂▂▁▃▄▂●▇█▄ #86 of 153* 8.3% 2
web search ▁▁▁●▄█▃▄·▁·▁ #72 of 84* 3.5% 1
agent tasks · #97 of 313 · 17% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.1](gpt-5-1.md), [o3-pro](o3-pro.md) here, though they rank higher overall. Trails [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md), [Hy3 preview (295B-A21B)](hy3-preview.md), [MiniMax-M2.7](minimax-m2-7.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Terminal-Bench 2.0 (maker) 46.3% █●···██··███ beats 1/6 hard · avg 15% 3.5% at its level
Humanity's Last Exam with tools (… 37.7% ▃·●·▅····▅·█ beats 1/8 very hard · avg 12% 3.5% at its level
Toolathlon (Tool Decathlon) (make… 35.5% ▃··●··▃··▃█▅ beats 1/8 very hard · avg 13% 3.5% at its level
OSWorld-Verified (maker) 39.0% ●▄·····▄·▄▄▄ beats 1/6 hard · avg 16% 2.6% at its level
τ²-bench Telecom (maker) 92.5% ▃·▅▅▃▃··●▃▃▅ beats 7/13 hard · avg 16% 2.1% sharp · at its level · noisy
SWE-bench Pro (maker) 52.4% ▄·····●·▄▄▄█ beats 1/6 hard · avg 29% 1.5% blunt · at its level
T1-Bench multi-domain conversatio… 7.2% ●▂··▂·▂·▂▂█▄ beats 0/11 hard · avg 25% 0.0% blunt · at its level · noisy
coding · #88 of 469 · 6.5% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.4 mini](gpt-5-4-mini.md), [GPT-5.1](gpt-5-1.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) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Luau Leetcode fully solved (Torpe… 64.9% ▁▁▁▁·▁▂▃●▃█▇ beats 17/49 hard · avg 17% 5.0% sharp · at its level
SWE-bench Pro (maker) 52.4% ▄·····●·▄▄▄█ beats 1/6 hard · avg 29% 1.5% blunt · at its level
chat & writing · #114 of 402 · 5.6% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md) here, though they rank higher overall. Trails [Nemotron-Cascade-8B](nemotron-cascade-8b.md), [Mistral Large 3 (675B-A41B)](mistral-large-3.md), [Gemini 2.5 Flash](gemini-2-5-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1401.2 ▁▁▁▂▃▃▃▆▆●█▃ beats 247/385 avg 1260 5.6% blunt
math · #64 of 431 · 35% of its score
Beats [Qwen3.6 Max Preview](qwen3-6-max-preview.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md) here, though they rank higher overall. Trails [INTELLECT-3](intellect-3.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 44.9% ▂··▁▁▃▄●█▆▄▃ beats 31/86 very hard · avg 1.2% 24% sharp · at its level
FrontierMath tier 4 v2 12.2% ▁▂▁●█▅▂▃▂▁▁▁ beats 13/67 very hard · avg 0.1% 7.8% sharp
OTIS mock AIME 2024–2025 87.8% ▃▃▄▃▃▃▇▇●▃▂▅ beats 136/197 very hard · avg 7.2% 2.7% sharp · noisy
vision & documents · #86 of 153 · 8.3% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [GPT-4.1 mini](gpt-4-1-mini.md), [Gemini 2.5 Flash](gemini-2-5-flash.md), [Gemma 4 12B](gemma-4-12b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MMMU Pro (maker) 66.1% ●·····▃·▃·█▃ beats 0/7 hard · avg 40% 4.5% at its level
Arena vision (style controlled) 1198.8 ▁▁▂▂▂▂▃▄●▅█▇ beats 71/152 avg 1061 3.8% blunt
web search · #72 of 84 · 3.5% of its score
Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall. Trails [GPT-4.1 nano](gpt-4-1-nano-2025-04-14.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 37.7% ▃·●·▅····▅·█ beats 1/8 very hard · avg 12% 3.5% at its level
other tests (9)
test result vs the others in its run difficulty counts why
GPQA Diamond 78.5% ▁▁▁▁▂▄▆▄▅●█▅ beats 122/211 hard · avg 39% 7.2% sharp
Humanity's Last Exam (maker) 24.3% ▃··●·▃▃···▃█ beats 1/7 very hard · avg 4.4% 4.8% typical
ARC-AGI-1 public evaluation set (… 51.6% ▁·▇▃▁·●▇▁·▇▃ beats 9/27 very hard · avg 4.0% 4.1% sharp · at its level · noisy
ARC-AGI-2 public evaluation set (… 3.5% █··●▅▁▃▁▃·▁▃ beats 7/24 very hard · avg 0.3% 3.5% sharp · noisy
Mystery Game Puzzles (Epoch) 5.0% ●··▂▃▇▄█▂▄▂▁ beats 11/77 very hard · avg 9.4% 3.2% sharp · too hard for it · noisy
Chess Puzzles (Epoch) 30.0% █▁▁▁▁▂▃▃●▂▁▁ beats 103/137 very hard · avg 5.8% 2.8% sharp · noisy
RobloxQA v2.0 multiple choice (To… 89.0% ▁▁▃▁●▄▃▃▆▂▂▂ beats 10/49 easy · avg 77% 2.0% typical
SimpleQA Verified 11.7% ▁▁●▂▃▃▆▆█▄▂▅ beats 4/81 very hard · avg 8.0% 1.9% at its level
Vectara hallucination leaderboard… 96.9% ▁▂▁▂▅█▂▃▄▃▁● beats 98/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 7 50% ▇▇▇▇▇▇
OpenAI vendor-reported benchmark scores… 8 26% ▇▇▇
LMArena leaderboard dataset 2 9% ▇
ARC Prize Foundation model_baseline out… 2 8% ▇
Roblox LLM Leaderboard 2 7% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 67.7
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)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · 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) · $0.46/M blended · $0.2 in / $1.25 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-nano.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-nano.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-4-nano) · [JSON](https://whatmodel.sh/api?format=json&all=1)