OpenAI, proprietary

# GPT-5.1

> GPT-5.1 by OpenAI, released November 2025. Ranked #53 of 354 with a Noometry Index of 49.0. API: $1.25 in / $10 out per M tokens. 400K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-1
- Last updated: 2026-10-10
- Title: GPT-5.1 Benchmarks, Price & Rank (October 2026) | Noometry

GPT-5.1 by OpenAI ranks 53rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 49.0. Its strongest category is instruction following, where it ranks 1st. API pricing starts at $1.25 per million input tokens and $10 per million output tokens, with a 400K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #53 of 354
- **Index score:** 49.0
- **Evidence:** Confirmed 63 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** November 13, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 400K
- **Max output:** 128K
- **Input price:** $1.25 / M
- **Output price:** $10 / M
- **Blended price:** $3.44 / M
- **Output speed:** Not measured
- **Value:** #165 of 219
- **Knowledge cutoff:** September 2024
- **Input:** text, image

## Category scores

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

GPT-5.1 category scores

1.  Coding 46.4
2.  Agentic & Tool Use 32.7
3.  Reasoning 39.8
4.  Math 52.2
5.  Knowledge 50.6
6.  Multimodal 44.8
7.  Multilingual 53.8
8.  Instruction Following 83.9
9.  Long Context 47.6
10.  Writing & Preference 64.5
11.  050100

GPT-5.1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 46.4 | #66 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.7 | #60 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 39.8 | #58 | 12 |
| [Math](https://noometry.com/best/math) | 52.2 | #51 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 50.6 | #71 | 7 |
| [Multimodal](https://noometry.com/best/multimodal) | 44.8 | #19 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 53.8 | #56 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 83.9 | #1 | 3 |
| [Long Context](https://noometry.com/best/long-context) | 47.6 | #14 | 3 |
| [Writing & Preference](https://noometry.com/best/writing) | 64.5 | #55 | 5 |

## Strengths and weaknesses

Categories where GPT-5.1 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-5.1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 83.9 | +12.6 | #1 of 305, top 1% |
| [Long Context](https://noometry.com/best/long-context) | 47.6 | +6.7 | #14 of 296, top 5% |
| [Multimodal](https://noometry.com/best/multimodal) | 44.8 | +6.2 | #19 of 128, top 15% |

### Weakest categories

GPT-5.1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.7 | +2.4 | #60 of 154, top 39% |
| [Knowledge](https://noometry.com/best/knowledge) | 50.6 | +13.3 | #71 of 314, top 23% |
| [Coding](https://noometry.com/best/coding) | 46.4 | +7.7 | #66 of 340, top 20% |

## Closest competitors

The models ranked just above and below GPT-5.1. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-5.1
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [MiMo-V2.6-Pro](https://noometry.com/models/mimo-v2-6-pro) | #49 | 50.3 | $0.54 | — | [Compare](https://noometry.com/compare/gpt-5-1-vs-mimo-v2-6-pro) |
| [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | #50 | 50.3 | $6 | — | [Compare](https://noometry.com/compare/claude-sonnet-4-6-vs-gpt-5-1) |
| [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) | #51 | 49.9 | $2 | — | [Compare](https://noometry.com/compare/gpt-5-1-vs-muse-spark-1-1) |
| [Claude Haiku 5.5](https://noometry.com/models/claude-haiku-5-5) | #52 | 49.5 | $0.20 | — | [Compare](https://noometry.com/compare/claude-haiku-5-5-vs-gpt-5-1) |
| [Grok 4.20 (Non-Reasoning)](https://noometry.com/models/grok-4-20) | #54 | 48.6 | $1.56 | 61 | [Compare](https://noometry.com/compare/gpt-5-1-vs-grok-4-20) |
| [MiMo-V2.6-Flash](https://noometry.com/models/mimo-v2-6-flash) | #55 | 48.5 | $0.18 | — | [Compare](https://noometry.com/compare/gpt-5-1-vs-mimo-v2-6-flash) |
| [Grok 4](https://noometry.com/models/grok-4) | #56 | 48.1 | — | 1 | [Compare](https://noometry.com/compare/gpt-5-1-vs-grok-4) |
| [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | #57 | 48.1 | $0.90 | 66 | [Compare](https://noometry.com/compare/gpt-5-1-vs-kimi-k2-5) |

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-5.1 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 68% | #25 of 32, top 79% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-18 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 66% | #14 of 39, top 36% | medium | [SWE-bench](https://www.swebench.com/) | 2025-11-20 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1395 | #75 of 113, top 67% | medium | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 43.3% | #65 of 121, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 13.7% | #16 of 31, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 13.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 60.8% | #29 of 119, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 72.5% | #5 of 39, top 13% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1454 | #81 of 294, top 28% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,192 | #30 of 105, top 29% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5.1 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 47.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 47.6% | #18 of 41, top 44% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 42.8% | #19 of 24, top 80% | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1199 | #14 of 32, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 1,473 | #44 of 60, top 74% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-5.1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 17.6% | #44 of 83, top 54% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 6.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0.4% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 53.2% | #37 of 77, top 49% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 72.8% | #42 of 83, top 51% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 33.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 57.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 5.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 4.9% | #58 of 134, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 32% | #32 of 129, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 14% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 17% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 11.2% | #12 of 38, top 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 1.9% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 95.8% | Best of 39 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1457 | #52 of 297, top 18% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 19% | #45 of 74, top 61% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 16% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 15% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 90.1% | #37 of 151, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 72.1% | #3 of 39, top 8% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 43.9% | #39 of 125, top 32% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 149.64 | #56 of 213, top 27% |  | [Epoch AI](https://epoch.ai/eci) | 2025-11-13 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 58.1 | #51 of 72, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 78.8% | #2 of 39, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-5.1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 88.6% | #58 of 173, top 34% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 63.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 85.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-17 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 37.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 46.4% | #21 of 57, top 37% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 94.5% | Best of 39 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1447 | #65 of 285, top 23% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 31% | #18 of 68, top 27% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 17.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 26.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-17 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 2.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 12.5% | #19 of 55, top 35% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 4.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-17 |

### Knowledge

GPT-5.1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 87.6% | #52 of 186, top 28% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 85% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-17 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 66.7% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 23.7% | #16 of 41, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 6.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 48% | #30 of 77, top 39% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 57.9% | #46 of 58, top 80% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12.1% |  |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 10.9% | #66 of 96, top 69% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 44.2% | #37 of 57, top 65% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1470 | #49 of 273, top 18% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5.1 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1250 | #55 of 122, top 46% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [VPCT](https://noometry.com/benchmarks/vpct) | 58.7% | #5 of 24, top 21% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 53.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1403 | #37 of 38, top 98% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

GPT-5.1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1431 | #55 of 297, top 19% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1495 | #49 of 285, top 18% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1450 | #69 of 223, top 31% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1438 | #60 of 231, top 26% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1453 | #23 of 211, top 11% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1401 | #52 of 213, top 25% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1435 | #60 of 283, top 22% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1433 | #79 of 226, top 35% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5.1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 93.3% | Best of 39 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 93.5% | #3 of 57, top 6% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1443 | #47 of 298, top 16% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5.1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 21.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 23.7% | #2 of 19, top 11% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 17.3% | #3 of 13, top 24% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1447 | #55 of 291, top 19% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5.1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1443 | #56 of 297, top 19% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1427 | #50 of 295, top 17% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 86.3% | #2 of 57, top 4% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1450 | #54 of 295, top 19% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 80.2% | Best of 39 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

GPT-5.1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $1.25 | $10 | $0.13 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $1.25 | $10 | $0.13 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5.1) | $1.25 | $10 | $0.13 | 2026-10-10 |

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

## Compare GPT-5.1

-   [GPT-5.1 vs GPT-5](https://noometry.com/compare/gpt-5-vs-gpt-5-1)
-   [GPT-5.1 vs Claude Haiku 5.5](https://noometry.com/compare/claude-haiku-5-5-vs-gpt-5-1)
-   [GPT-5.1 vs Grok 4.20 (Non-Reasoning)](https://noometry.com/compare/gpt-5-1-vs-grok-4-20)
-   [GPT-5.1 vs Muse Spark 1.1](https://noometry.com/compare/gpt-5-1-vs-muse-spark-1-1)
-   [GPT-5.1 vs MiMo-V2.6-Flash](https://noometry.com/compare/gpt-5-1-vs-mimo-v2-6-flash)
-   [GPT-5.1 vs Claude Sonnet 4.6](https://noometry.com/compare/claude-sonnet-4-6-vs-gpt-5-1)
-   [GPT-5.1 vs Grok 4](https://noometry.com/compare/gpt-5-1-vs-grok-4)
-   [GPT-5.1 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-1)
-   [GPT-5.1 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-1)
-   [GPT-5.1 vs Kimi K3](https://noometry.com/compare/gpt-5-1-vs-kimi-k3)
-   [GPT-5.1 vs Grok 4.6](https://noometry.com/compare/gpt-5-1-vs-grok-4-6)
-   [GPT-5.1 vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-1-vs-qwen3-8-max)
-   [GPT-5.1 vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-1)
-   [GPT-5.1 vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-1-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-5.1?

GPT-5.1 by OpenAI ranks 53rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 49.0. Its strongest category is instruction following, where it ranks 1st. API pricing starts at $1.25 per million input tokens and $10 per million output tokens, with a 400K-token context window.

### How much does GPT-5.1 cost?

GPT-5.1 costs $1.25 per million input tokens and $10 per million output tokens on OpenAI's own API, with cached input at $0.13.

### What is GPT-5.1's context window?

GPT-5.1 accepts up to 400K tokens of input and can write up to 128K tokens in one response.

### Is GPT-5.1 open source?

No. GPT-5.1 is proprietary and available only through OpenAI's API and partner platforms.

### What are GPT-5.1's strengths and weaknesses?

Relative to other ranked models, GPT-5.1 places best in instruction following, long context, multimodal and lowest in agentic & tool use, knowledge, coding.

### What is GPT-5.1 best at?

Its best category is instruction following, where it ranks 1st on Noometry.

### Cite this page

Noometry. (2026). GPT-5.1 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-5-1

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-5-1.md).