OpenAI, proprietary

# GPT-4.1

> GPT-4.1 by OpenAI, released April 2025. Ranked #219 of 354 with a Noometry Index of 35.9. API: $2 in / $8 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-4-1
- Last updated: 2026-10-10
- Title: GPT-4.1 Benchmarks, Price & Rank (October 2026) | Noometry

GPT-4.1 by OpenAI ranks 219th of 354 ranked models on the Noometry Index as of October 2026, with a score of 35.9. Its strongest category is agentic & tool use, where it ranks 43rd. API pricing starts at $2 per million input tokens and $8 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #219 of 354
- **Index score:** 35.9
- **Evidence:** Confirmed 52 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:** $2 / M
- **Output price:** $8 / M
- **Blended price:** $3.50 / M
- **Output speed:** 116 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #184 of 219
- **Knowledge cutoff:** April 2024
- **Input:** text, image, pdf

## Category scores

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

GPT-4.1 category scores

1.  Coding 34.4
2.  Agentic & Tool Use 34.7
3.  Reasoning 11.7
4.  Math 22.3
5.  Knowledge 37.1
6.  Multimodal 38.2
7.  Multilingual 49.4
8.  Instruction Following 71.3
9.  Long Context 40.0
10.  Writing & Preference 57.6
11.  020406080

GPT-4.1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 34.4 | #238 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.7 | #43 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 11.7 | #339 | 9 |
| [Math](https://noometry.com/best/math) | 22.3 | #280 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 37.1 | #160 | 7 |
| [Multimodal](https://noometry.com/best/multimodal) | 38.2 | #67 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 49.4 | #133 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 71.3 | #153 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 40.0 | #163 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 57.6 | #125 | 5 |

## Strengths and weaknesses

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

### Strongest categories

GPT-4.1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.7 | +4.3 | #43 of 154, top 28% |
| [Writing & Preference](https://noometry.com/best/writing) | 57.6 | +3.8 | #125 of 312, top 41% |
| [Multilingual](https://noometry.com/best/multilingual) | 49.4 | +2.0 | #133 of 297, top 45% |

### Weakest categories

GPT-4.1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 11.7 | −11.9 | #339 of 350, top 97% |
| [Math](https://noometry.com/best/math) | 22.3 | −14.3 | #280 of 327, top 86% |
| [Coding](https://noometry.com/best/coding) | 34.4 | −4.4 | #238 of 340, top 70% |

## Closest competitors

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

Models ranked closest to GPT-4.1
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Command A](https://noometry.com/models/command-a) | #215 | 36.5 | $4.38 | 28 | [Compare](https://noometry.com/compare/command-a-vs-gpt-4-1) |
| [Grok Build 0.1](https://noometry.com/models/grok-build-0-1) | #216 | 36.4 | $1.25 | — | [Compare](https://noometry.com/compare/gpt-4-1-vs-grok-build-0-1) |
| [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | #217 | 36.3 | $0.0703 | 55 | [Compare](https://noometry.com/compare/gpt-4-1-vs-gpt-oss-120b) |
| [Mistral Medium](https://noometry.com/models/mistral-medium) | #218 | 36.3 | $3 | 68 | [Compare](https://noometry.com/compare/gpt-4-1-vs-mistral-medium) |
| [Deepseek Coder v2](https://noometry.com/models/deepseek-coder-v2) | #220 | 35.9 | — | — | [Compare](https://noometry.com/compare/deepseek-coder-v2-vs-gpt-4-1) |
| [C4ai Aya Expanse 32b](https://noometry.com/models/c4ai-aya-expanse-32b) | #221 | 35.9 | — | — | [Compare](https://noometry.com/compare/c4ai-aya-expanse-32b-vs-gpt-4-1) |
| [Llama 3.1 Nemotron 51b Instruct](https://noometry.com/models/llama-3-1-nemotron-51b-instruct) | #222 | 35.9 | — | — | [Compare](https://noometry.com/compare/gpt-4-1-vs-llama-3-1-nemotron-51b-instruct) |
| [Nemotron 4 340b Instruct](https://noometry.com/models/nemotron-4-340b-instruct) | #223 | 35.9 | — | — | [Compare](https://noometry.com/compare/gpt-4-1-vs-nemotron-4-340b-instruct) |

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 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 48.5% | #31 of 32, top 97% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-08 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 39.6% | #30 of 39, top 77% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 52.4% | #21 of 44, top 48% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 39% | #81 of 119, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1391 | #142 of 294, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [CadEval](https://noometry.com/benchmarks/cadeval) | 42% | #8 of 14, top 58% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 558.1 | #84 of 105, top 80% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-4.1 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 54% | #15 of 49, top 31% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

GPT-4.1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0.4% | #73 of 83, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 27% | #64 of 77, top 84% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 52.3% | #59 of 99, top 60% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 5.5% | #76 of 83, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 6% | #90 of 129, top 70% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 2.2% | #30 of 38, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1384 | #135 of 297, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 68.3% | #96 of 151, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 25.6% | #85 of 125, top 68% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 136.78 | #119 of 213, top 56% |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-14 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61.5 | #8 of 72, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-4.1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 6% | #77 of 81, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 38.3% | #116 of 173, top 68% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 47.1% | #18 of 57, top 32% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1370 | #150 of 285, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 83% | #23 of 79, top 30% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 5.5% | #45 of 68, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 0% | #50 of 55, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-07-01 |

### Knowledge

GPT-4.1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 66.9% | #103 of 186, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-14 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 5.4% | #35 of 41, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 31.1% | #56 of 77, top 73% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-31 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 81.1% | #14 of 58, top 25% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 5.6% | #18 of 96, top 19% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 65.9% | #16 of 57, top 29% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1364 | #144 of 273, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-4.1 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1211 | #75 of 122, top 62% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 72% | #11 of 25, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

GPT-4.1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1370 | #133 of 297, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1382 | #148 of 285, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1382 | #130 of 223, top 59% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1381 | #110 of 231, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1319 | #113 of 211, top 54% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1339 | #110 of 213, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1377 | #130 of 283, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1376 | #128 of 226, top 57% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-4.1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 83.8% | #25 of 57, top 44% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1367 | #133 of 298, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-4.1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 63.9% | #24 of 47, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1385 | #129 of 291, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-4.1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1383 | #134 of 297, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1363 | #117 of 295, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1420 | #67 of 115, top 59% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 85.4% | #10 of 57, top 18% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1398 | #121 of 295, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-4.1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $2 | $8 | $0.50 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $2 | $8 | $0.50 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-4.1) | $2 | $8 | $0.50 | 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

-   [GPT-4.1 vs GPT-4.5](https://noometry.com/compare/gpt-4-1-vs-gpt-4-5)
-   [GPT-4.1 vs Mistral Medium](https://noometry.com/compare/gpt-4-1-vs-mistral-medium)
-   [GPT-4.1 vs Deepseek Coder v2](https://noometry.com/compare/deepseek-coder-v2-vs-gpt-4-1)
-   [GPT-4.1 vs gpt-oss-120b](https://noometry.com/compare/gpt-4-1-vs-gpt-oss-120b)
-   [GPT-4.1 vs C4ai Aya Expanse 32b](https://noometry.com/compare/c4ai-aya-expanse-32b-vs-gpt-4-1)
-   [GPT-4.1 vs Grok Build 0.1](https://noometry.com/compare/gpt-4-1-vs-grok-build-0-1)
-   [GPT-4.1 vs Llama 3.1 Nemotron 51b Instruct](https://noometry.com/compare/gpt-4-1-vs-llama-3-1-nemotron-51b-instruct)
-   [GPT-4.1 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-4-1)
-   [GPT-4.1 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-4-1)
-   [GPT-4.1 vs Kimi K3](https://noometry.com/compare/gpt-4-1-vs-kimi-k3)
-   [GPT-4.1 vs Grok 4.6](https://noometry.com/compare/gpt-4-1-vs-grok-4-6)
-   [GPT-4.1 vs Qwen3.8 Max](https://noometry.com/compare/gpt-4-1-vs-qwen3-8-max)
-   [GPT-4.1 vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-4-1)
-   [GPT-4.1 vs Muse Spark 1.3](https://noometry.com/compare/gpt-4-1-vs-muse-spark-1-3)

## 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?

GPT-4.1 by OpenAI ranks 219th of 354 ranked models on the Noometry Index as of October 2026, with a score of 35.9. Its strongest category is agentic & tool use, where it ranks 43rd. API pricing starts at $2 per million input tokens and $8 per million output tokens, with a 1.05M-token context window.

### How much does GPT-4.1 cost?

GPT-4.1 costs $2 per million input tokens and $8 per million output tokens on OpenAI's own API, with cached input at $0.50.

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

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

### Is GPT-4.1 open source?

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

### How fast is GPT-4.1?

GPT-4.1 generated about 116 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's strengths and weaknesses?

Relative to other ranked models, GPT-4.1 places best in agentic & tool use, writing & preference, multilingual and lowest in reasoning, math, coding.

### What is GPT-4.1 best at?

Its best category is agentic & tool use, where it ranks 43rd on Noometry.

### Cite this page

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

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