OpenAI, proprietary

# GPT-4.1 nano

> GPT-4.1 nano by OpenAI, released April 2025. Ranked #327 of 354 with a Noometry Index of 27.9. API: $0.10 in / $0.40 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-4-1-nano
- Last updated: 2026-10-10
- Title: GPT-4.1 nano Benchmarks, Price & Rank (October 2026)

GPT-4.1 nano by OpenAI ranks 327th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.9. Its strongest category is agentic & tool use, where it ranks 104th. API pricing starts at $0.10 per million input tokens and $0.40 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #327 of 354
- **Index score:** 27.9
- **Evidence:** Confirmed 38 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** April 14, 2025
- **Weights:** Proprietary
- **Reasoning:** No
- **Context window:** 1.05M
- **Max output:** 33K
- **Input price:** $0.10 / M
- **Output price:** $0.40 / M
- **Blended price:** $0.18 / M
- **Output speed:** 135 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #48 of 219
- **Knowledge cutoff:** April 2024
- **Input:** text, image

## Category scores

Each category score combines every public result we have in that category.

GPT-4.1 nano category scores

1.  Coding 24.1
2.  Agentic & Tool Use 26.5
3.  Reasoning 8.5
4.  Math 26.9
5.  Knowledge 21.8
6.  Multimodal 29.2
7.  Multilingual 41.6
8.  Instruction Following 67.8
9.  Long Context 23.7
10.  Writing & Preference 40.5
11.  020406080

GPT-4.1 nano category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 24.1 | #330 | 4 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 26.5 | #104 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 8.5 | #349 | 7 |
| [Math](https://noometry.com/best/math) | 26.9 | #252 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 21.8 | #273 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 29.2 | #113 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 41.6 | #205 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 67.8 | #193 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 23.7 | #296 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 40.5 | #243 | 5 |

## Strengths and weaknesses

Categories where GPT-4.1 nano places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-4.1 nano: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 67.8 | −3.5 | #193 of 305, top 64% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 26.5 | −3.8 | #104 of 154, top 68% |
| [Multilingual](https://noometry.com/best/multilingual) | 41.6 | −5.8 | #205 of 297, top 70% |

### Weakest categories

GPT-4.1 nano: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 23.7 | −17.2 | #296 of 296, top 100% |
| [Reasoning](https://noometry.com/best/reasoning) | 8.5 | −15.1 | #349 of 350, top 100% |
| [Coding](https://noometry.com/best/coding) | 24.1 | −14.6 | #330 of 340, top 98% |

## Closest competitors

The models ranked just above and below GPT-4.1 nano. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-4.1 nano
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Llama 3-70B](https://noometry.com/models/llama-3-70b) | #323 | 28.8 | — | 104 | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-llama-3-70b) |
| [GPT-4o](https://noometry.com/models/gpt-4o) | #324 | 28.6 | $4.38 | — | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-gpt-4o) |
| [Ministral 8B](https://noometry.com/models/ministral-8b) | #325 | 28.2 | $0.15 | — | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-ministral-8b) |
| [Gemma 3 4B](https://noometry.com/models/gemma-3-4b) | #326 | 28.1 | $0.05 | 72 | [Compare](https://noometry.com/compare/gemma-3-4b-vs-gpt-4-1-nano) |
| [Phi 3 Mini 4k Instruct](https://noometry.com/models/phi-3-mini-4k-instruct) | #328 | 27.9 | — | — | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-phi-3-mini-4k-instruct) |
| [Yi-34B](https://noometry.com/models/yi-34b) | #329 | 27.8 | — | — | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-yi-34b) |
| [Llama 4 Scout](https://noometry.com/models/llama-4-scout) | #330 | 27.7 | $0.15 | 272 | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-llama-4-scout) |
| [Llama 3.2 90B](https://noometry.com/models/llama-3-2-90b) | #331 | 27.5 | — | — | [Compare](https://noometry.com/compare/gpt-4-1-nano-vs-llama-3-2-90b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

GPT-4.1 nano Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 8.9% | #42 of 44, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 25.9% | #112 of 121, top 93% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 19% | #106 of 119, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1306 | #197 of 294, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

GPT-4.1 nano Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 33% | #30 of 49, top 62% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

GPT-4.1 nano Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% | #76 of 83, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 33.3% | #90 of 99, top 91% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 0% | #83 of 83, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #111 of 134, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1286 | #196 of 297, top 66% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 52.5% | #130 of 151, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 5.5% | #121 of 125, top 97% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 129.62 | #140 of 213, top 66% |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-14 |

### Math

GPT-4.1 nano Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 28.9% | #122 of 173, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 36.7% | #31 of 57, top 55% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1274 | #195 of 285, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 70% | #31 of 79, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 1% | #59 of 68, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |

### Knowledge

GPT-4.1 nano Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 48.9% | #131 of 186, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 6% | #77 of 77, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-31 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 55% | #48 of 58, top 83% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 50.7% | #33 of 57, top 58% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1272 | #187 of 273, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-4.1 nano Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1063 | #113 of 122, top 93% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |

### Multilingual

GPT-4.1 nano Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1260 | #205 of 297, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1270 | #199 of 285, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1288 | #154 of 231, top 67% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1198 | #162 of 211, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1261 | #204 of 283, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-4.1 nano Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 84.3% | #21 of 57, top 37% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1267 | #199 of 298, top 67% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-4.1 nano Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 25% | #47 of 47, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1283 | #201 of 291, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-4.1 nano Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1285 | #204 of 297, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1260 | #199 of 295, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 946 | #98 of 115, top 86% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 81.2% | #25 of 57, top 44% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1277 | #202 of 295, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-4.1 nano API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.10 | $0.40 | $0.025 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.10 | $0.40 | $0.025 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-4.1-nano) | $0.10 | $0.40 | $0.025 | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GPT-4.1 nano

-   [GPT-4.1 nano vs Gemma 3 4B](https://noometry.com/compare/gemma-3-4b-vs-gpt-4-1-nano)
-   [GPT-4.1 nano vs Phi 3 Mini 4k Instruct](https://noometry.com/compare/gpt-4-1-nano-vs-phi-3-mini-4k-instruct)
-   [GPT-4.1 nano vs Ministral 8B](https://noometry.com/compare/gpt-4-1-nano-vs-ministral-8b)
-   [GPT-4.1 nano vs Yi-34B](https://noometry.com/compare/gpt-4-1-nano-vs-yi-34b)
-   [GPT-4.1 nano vs GPT-4o](https://noometry.com/compare/gpt-4-1-nano-vs-gpt-4o)
-   [GPT-4.1 nano vs Llama 4 Scout](https://noometry.com/compare/gpt-4-1-nano-vs-llama-4-scout)
-   [GPT-4.1 nano vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-4-1-nano)
-   [GPT-4.1 nano vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-4-1-nano)
-   [GPT-4.1 nano vs Kimi K3](https://noometry.com/compare/gpt-4-1-nano-vs-kimi-k3)
-   [GPT-4.1 nano vs Grok 4.6](https://noometry.com/compare/gpt-4-1-nano-vs-grok-4-6)
-   [GPT-4.1 nano vs Qwen3.8 Max](https://noometry.com/compare/gpt-4-1-nano-vs-qwen3-8-max)
-   [GPT-4.1 nano vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-4-1-nano)
-   [GPT-4.1 nano vs Muse Spark 1.3](https://noometry.com/compare/gpt-4-1-nano-vs-muse-spark-1-3)
-   [GPT-4.1 nano vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-4-1-nano)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol)65.6
-   [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol)65.0
-   [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro)64.3
-   [GPT-5.5](https://noometry.com/models/gpt-5-5)63.4
-   [GPT-6 Sol](https://noometry.com/models/gpt-6-sol)61.8
-   [GPT-5.4](https://noometry.com/models/gpt-5-4)59.4
-   [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra)59.2

## Frequently asked questions

### How good is GPT-4.1 nano?

GPT-4.1 nano by OpenAI ranks 327th of 354 ranked models on the Noometry Index as of October 2026, with a score of 27.9. Its strongest category is agentic & tool use, where it ranks 104th. API pricing starts at $0.10 per million input tokens and $0.40 per million output tokens, with a 1.05M-token context window.

### How much does GPT-4.1 nano cost?

GPT-4.1 nano costs $0.10 per million input tokens and $0.40 per million output tokens on OpenAI's own API, with cached input at $0.025.

### What is GPT-4.1 nano's context window?

GPT-4.1 nano accepts up to 1.05M tokens of input and can write up to 33K tokens in one response.

### Is GPT-4.1 nano open source?

No. GPT-4.1 nano is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-4.1 nano?

GPT-4.1 nano generated about 135 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-4.1 nano's strengths and weaknesses?

Relative to other ranked models, GPT-4.1 nano places best in instruction following, agentic & tool use, multilingual and lowest in long context, reasoning, coding.

### What is GPT-4.1 nano best at?

Its best category is agentic & tool use, where it ranks 104th on Noometry.

### Cite this page

Noometry. (2026). GPT-4.1 nano benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-4-1-nano

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-4-1-nano.md).