[whatmodel.sh](../../README.md) / GPT-4.1 nano
# GPT-4.1 nano
OpenAI · released 2025-04 · proprietary
**score 53.2** ±0.7 · #314 of 732 · from 35 benchmark results across 17 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-4.1 nano among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #253 of 313 8.2% 7
coding ▁▁▁▃▅▇●▇▅▄▃▁ #265 of 469* 7.7% 2
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #206 of 402 9.3% 3
math ▁▁▃▅█●▄█▄▂▁▁ #216 of 431 19% 4
vision & documents ▁▁▂▂▁●▄▂▆▇█▄ #122 of 153 11% 3
web search ▁▁▁●▄█▃▄·▁·▁ #65 of 84* 1.4% 1
agent tasks · #253 of 313 · 8.2% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Sky-T1 32B Preview](sky-t1-32b-preview.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md) here, though they rank higher overall. Trails [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md), [Qwen3-VL 2B Instruct](qwen3-vl-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 33.1% ▁▁▁▂█●▅▅▆▂▂▂ beats 37/77 hard · avg 31% 2.1% at its level
τ²-bench Telecom (maker) 12.1% ●·▅▅▃▃··█▃▃▅ beats 0/13 hard · avg 16% 1.6% sharp · noisy
Finance Agent Benchmark v1 (537 q… 2.4% ●·▃▃·█▅▅▅▅▃▅ beats 0/18 very hard · avg 5.5% 1.4% sharp
BFCL v3 53.0% ▁▁▁▁▁▃▅●▅▄▁▁ beats 50/114 medium · avg 51% 1.1% blunt · at its level
Galileo Agent Leaderboard v2 (act… 38.0% ▅▃·▃▃▅●█▃█▅▅ beats 7/19 hard · avg 26% 0.9% at its level
Galileo Agent Leaderboard v1 (too… 80.3% ▃▃▃▃●▅▆▅█▂▅▂ beats 9/31 easy · avg 78% 0.8% typical
KlinikeBench strict pass@1 (333 i… 0.9% ▁●·▁▁▁▁▅▃█▅█ beats 1/27 very hard · avg 2.4% 0.3% too hard for it
coding · #265 of 469 · 7.7% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider polyglot (225 exercises, tw… 8.9% ▂·●▃▅▂▃█▆▄▂▂ beats 2/49 very hard · avg 7.6% 6.0% sharp
HumanEval (simple-evals) (maker) 87.0% ●█▆▆▂··▂···▂ beats 2/15 easy · avg 83% 1.7% typical
chat & writing · #206 of 402 · 9.3% of its score
Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1322.3 ▁▁▁▂▃▃▃●▆▇█▃ beats 157/385 avg 1260 4.8% blunt
Arena-Hard v2.0 hard prompts (sty… 13.7% ▂·▃●▅·▃█▆▂▂▂ beats 3/22 very hard · avg 12% 3.6% sharp
Arena-Hard v2.0 creative writing … 10.7% ●▂▂▆▂▂▃█▂▃·▂ beats 3/22 hard · avg 19% 0.9% sharp · at its level · noisy
math · #216 of 431 · 19% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 70.0% ▂▂▅▆▄█●▃▅▂▃▄ beats 60/92 hard · avg 28% 10% sharp · at its level
MATH-500 (simple-evals) (maker) 62.3% ●··▅▅▃···▃·█ beats 0/9 medium · avg 61% 5.0% sharp · at its level
MGSM (simple-evals) (maker) 73.0% ●····▁▅▁█▁▃▁ beats 0/16 easy · avg 80% 2.0% typical
OTIS mock AIME 2024–2025 28.9% ▃▃▄▃●▃▇▇█▃▂▅ beats 63/197 very hard · avg 7.2% 1.9% sharp · noisy
vision & documents · #122 of 153 · 11% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Qwen2-VL 72B Instruct](qwen2-vl-72b.md) here, though they rank higher overall. Trails [Qwen3.5 2B](qwen3-5-2b.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv descriptive questions (ma… 73.9% ●▄·····▄·▄█▄ beats 0/6 easy · avg 71% 4.6% typical
Arena vision (style controlled) 1088.5 ▁▁▂▂▂●▃▄▅▅█▇ beats 28/152 avg 1061 3.2% blunt
CharXiv (reasoning questions) (ma… 40.5% ●··▂█··▂···▄ beats 1/9 hard · avg 32% 3.0% at its level
web search · #65 of 84 · 1.4% of its score
Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [Grok 4.3](grok-4-3.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Finance Agent Benchmark v1 (537 q… 2.4% ●·▃▃·█▅▅▅▅▃▅ beats 0/18 very hard · avg 5.5% 1.4% sharp
other tests (16)
test result vs the others in its run difficulty counts why
CLIcK Korean (Daekeun Kim) 61.2% ▆▄●▄█▄▂·▄█·▄ beats 6/23 medium · avg 56% 7.5% at its level
GPQA Diamond (simple-evals) (make… 50.3% █▃▃●·▃▃▅▃▃▃▅ beats 6/16 hard · avg 39% 6.8% sharp · at its level
HAE-RAE 1.0 (Daekeun Kim) 68.0% ▄▄▄●▂█▂▄▂█▂▄ beats 6/22 medium · avg 61% 6.4% at its level
GPQA Diamond 48.9% ▁▁▁▁▂▄●▄▅▇█▅ beats 65/211 hard · avg 39% 6.3% sharp · at its level
MMLU (simple-evals, zero-shot CoT… 80.1% ●▂·█▄▄▂·█▂·▂ beats 0/16 easy · avg 79% 3.8% typical
EQ-Bench 3 (emotional intelligenc… 1134.9 ▁·▂▁▄●▅▃█▄▁▁ beats 16/62 avg 1034 2.9% typical
HindiGen-v1 3C3H score (Inception… 56.9% ▁▁▁▁·▂·●▆█▇▁ beats 12/31 medium · avg 46% 2.5% at its level
Open Persian LLM Leaderboard v2 a… 60.1% ▂▄▂▃█▄▃●▃▅▂▂ beats 50/81 medium · avg 57% 2.1% blunt · at its level
KMMLU-HARD (Daekeun Kim, zero-sho… 24.3% ●···▁▁▁▂▃·▆▂ beats 2/23 very hard · avg 29% 2.1% sharp · too hard for it
ARC-AGI-1 public evaluation set (… 1.8% ●·▇▃▁·█▇▁·▇▃ beats 0/27 very hard · avg 4.0% 1.1% sharp · too hard for it · noisy
Icelandic LLM Leaderboard average… 17.6% ▁▁▃●▂▄▁▃▄▄▄█ beats 6/38 very hard · avg 14% 1.0% typical
SimpleQA Verified 6.0% ●▁▃▂▃▃▆▆█▄▂▅ beats 0/81 very hard · avg 8.0% 0.7% typical
DROP F1, 3-shot (simple-evals) (m… 82.2% ▂▂▄▆●█·▂···▄ beats 9/16 easy · avg 78% 0.5% blunt
SimpleQA (simple-evals) (maker) 7.6% ●·▁▁▁▁··█▁·▁ beats 0/14 hard · avg 17% 0.3% noisy
Vectara hallucination leaderboard… 98.0% ▁▁▂▃▄▄█▆●▂▂▁ beats 122/140 very easy · avg 94% 0.3% blunt · maxed out · noisy
ARC-AGI-2 public evaluation set (… 0.0% ●··▂▅▁▃▁▃·▁▃ beats 0/24 very hard · avg 0.3% 0.1% sharp · too hard for it · noisy
source results share of the score
OpenAI simple-evals README benchmark re… 7 20% ▇▇
Epoch AI 4 19% ▇▇
Daekeun Kim Korean LLM benchmark runs 3 16% ▇▇
OpenAI vendor-reported benchmark scores… 3 9% ▇
LMArena leaderboard dataset 2 8% ▇
Aider polyglot benchmark runs 1 6% ▇
Arena-Hard-Auto v2.0-Preview leaderboard 2 5% ▇
Berkeley Function Calling Leaderboard r… 2 3% ▇
EQ-Bench 3 canonical leaderboard results 1 3% ▇
Inception Arabic/Hindi leaderboards 1 3% ▇
Open Persian LLM Leaderboard v2 1 2% ▇
Galileo Agent Leaderboard 2 2% ▇
Finance Agent Benchmark 1 1% ▇
ARC Prize Foundation model_baseline out… 2 1% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 53.2
Licences: OpenAI simple-evals README benchmark results ([MIT](../../licenses/openai-simple-evals-MIT.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · 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)) · Arena-Hard-Auto v2.0-Preview leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · Finance Agent Benchmark ([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)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([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) · $0.18/M blended · $0.1 in / $0.4 out per 1M tokens · [developers.openai.com](https://developers.openai.com/api/docs/models/gpt-4.1-nano)
[price source](https://developers.openai.com/api/docs/models/gpt-4.1-nano): USD per 1M tokens, OpenAI API standard processing, model page 'gpt-4.1-nano' whose default snapshot is gpt-4.1-nano-2025-04-14: input $0.1, output $0.4; cached-input, Batch and other tiers excluded. Currency: USD. OpenAI official model page, observed 2026-09-28. · [context source](https://developers.openai.com/api/docs/models/gpt-4.1-nano): OpenAI model page for gpt-4.1-nano (default snapshot gpt-4.1-nano-2025-04-14): 1,047,576 context window. Observed 2026-09-28.
[interactive version](https://whatmodel.sh/model/gpt-4-1-nano-2025-04-14) · [JSON](https://whatmodel.sh/api?format=json&all=1)