OpenAI, proprietary

# o1

> o1 by OpenAI, released September 2024. Ranked #143 of 354 with a Noometry Index of 40.9. API: $15 in / $60 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/o1
- Last updated: 2026-10-10
- Title: o1 Benchmarks, Price & Rank (October 2026) | Noometry

o1 by OpenAI ranks 143rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 40.9. Its strongest category is long context, where it ranks 9th. API pricing starts at $15 per million input tokens and $60 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #143 of 354
- **Index score:** 40.9
- **Evidence:** Confirmed 52 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** September 12, 2024
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 100K
- **Input price:** $15 / M
- **Output price:** $60 / M
- **Blended price:** $26.25 / M
- **Output speed:** Not measured
- **Value:** #210 of 219
- **Knowledge cutoff:** September 2023
- **Input:** text, image, pdf

## Category scores

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

o1 category scores

1.  Coding 46.1
2.  Agentic & Tool Use 24.6
3.  Reasoning 27.9
4.  Math 36.1
5.  Knowledge 41.5
6.  Multimodal 34.2
7.  Multilingual 48.6
8.  Instruction Following 74.8
9.  Long Context 50.3
10.  Writing & Preference 55.6
11.  020406080

o1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 46.1 | #70 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 24.6 | #117 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 27.9 | #111 | 9 |
| [Math](https://noometry.com/best/math) | 36.1 | #175 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 41.5 | #110 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 34.2 | #93 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 48.6 | #142 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.8 | #86 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 50.3 | #9 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 55.6 | #144 | 5 |

## Strengths and weaknesses

Categories where o1 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

o1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 50.3 | +9.4 | #9 of 296, top 4% |
| [Coding](https://noometry.com/best/coding) | 46.1 | +7.4 | #70 of 340, top 21% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.8 | +3.5 | #86 of 305, top 29% |

### Weakest categories

o1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 24.6 | −5.8 | #117 of 154, top 76% |
| [Multimodal](https://noometry.com/best/multimodal) | 34.2 | −4.3 | #93 of 128, top 73% |
| [Math](https://noometry.com/best/math) | 36.1 | −0.5 | #175 of 327, top 54% |

## Closest competitors

The models ranked just above and below o1. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to o1
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Hunyuan Turbos 20250226](https://noometry.com/models/hunyuan-turbos) | #139 | 41.3 | — | — | [Compare](https://noometry.com/compare/hunyuan-turbos-vs-o1) |
| [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | #140 | 41.2 | $1 | 201 | [Compare](https://noometry.com/compare/kimi-k2-vs-o1) |
| [Grok-3 mini](https://noometry.com/models/grok-3-mini) | #141 | 41.2 | — | 10 | [Compare](https://noometry.com/compare/grok-3-mini-vs-o1) |
| [Claude Opus 4.1](https://noometry.com/models/claude-opus-4-1) | #142 | 41.0 | $30 | — | [Compare](https://noometry.com/compare/claude-opus-4-1-vs-o1) |
| [Gemini 3.1 Flash Lite](https://noometry.com/models/gemini-3-1-flash-lite) | #144 | 40.8 | $0.56 | 10 | [Compare](https://noometry.com/compare/gemini-3-1-flash-lite-vs-o1) |
| [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | #145 | 40.8 | $6 | 31 | [Compare](https://noometry.com/compare/claude-sonnet-4-vs-o1) |
| [Qwen2.5-Max](https://noometry.com/models/qwen2-5-max) | #146 | 40.7 | — | — | [Compare](https://noometry.com/compare/o1-vs-qwen2-5-max) |
| [Nemotron 3 Nano 30B A3B](https://noometry.com/models/nemotron-3-nano-30b-a3b) | #147 | 40.6 | $0.0875 | — | [Compare](https://noometry.com/compare/nemotron-3-nano-30b-a3b-vs-o1) |

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

o1 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 61.7% | #11 of 44, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 47.6% | #54 of 119, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 46.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 69.7% | #8 of 39, top 21% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1367 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1367 | #162 of 294, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [CadEval](https://noometry.com/benchmarks/cadeval) | 56% | #4 of 14, top 29% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 89% | Best of 45 | sept 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 80.2% | Best of 38 | sept 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Agentic & Tool Use

o1 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Cybench](https://noometry.com/benchmarks/cybench) | 10% | #16 of 21, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 45.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 51.1% | #23 of 32, top 72% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

o1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 41.7% | #50 of 77, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 40.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 36.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 18% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 27.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 30.7% | #65 of 83, top 79% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 15% | #68 of 129, top 53% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 12% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 5.7% | #21 of 38, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 91.6% | #2 of 39, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1371 | #147 of 297, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1354 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 74.7% | #88 of 151, top 59% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 65.5% | #9 of 39, top 24% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 22.3% | #90 of 125, top 72% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 134.79 |  |  | [Epoch AI](https://epoch.ai/eci) | 2024-09-12 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 141.91 | #100 of 213, top 47% |  | [Epoch AI](https://epoch.ai/eci) | 2024-12-17 |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 75.7% | #5 of 39, top 13% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

o1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 14.7% | #74 of 81, top 92% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 8.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 10.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 31.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-03-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 73.3% | #86 of 173, top 50% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 53.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 73.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-27 |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 80.3% | #4 of 39, top 11% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1373 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1388 | #140 of 285, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 81.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 94.7% | #12 of 79, top 16% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-13 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 94.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 9.3% | #38 of 68, top 56% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-03-07 |

### Knowledge

o1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 50.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 76.8% | #87 of 186, top 47% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-13 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 74.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-20 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 75.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 8% | #30 of 41, top 74% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 41.1% | #40 of 77, top 52% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-31 |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 13% |  |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 11.7% | #5 of 51, top 10% | medium reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1338 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1361 | #147 of 273, top 54% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

o1 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1168 | #92 of 122, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 80% | #5 of 25, top 20% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 37% | #18 of 24, top 75% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SpatialViz-Bench](https://noometry.com/benchmarks/spatialviz-bench) | 41.4% | #2 of 8, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

o1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1358 | #142 of 297, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1315 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1326 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1394 | #140 of 285, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1344 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1344 | #146 of 223, top 66% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1337 | #139 of 231, top 61% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1309 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1294 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1346 | #100 of 211, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1396 | #59 of 213, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1290 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1356 | #143 of 283, top 51% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1316 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1302 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1345 | #150 of 226, top 67% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

o1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 81.5% | #7 of 39, top 18% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1367 | #132 of 298, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1342 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

o1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 83.3% | #10 of 47, top 22% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1344 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1378 | #135 of 291, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

o1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1366 | #146 of 297, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1353 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1318 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1348 | #130 of 295, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 70.2% | #31 of 39, top 80% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1355 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1369 | #141 of 295, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 65.4% | #3 of 39, top 8% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

o1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $15 | $60 | $7.50 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $15 | $60 | $7.50 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/o1) | $15 | $60 | $7.50 | 2026-10-10 |

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

## Compare o1

-   [o1 vs Claude Opus 4.1](https://noometry.com/compare/claude-opus-4-1-vs-o1)
-   [o1 vs Gemini 3.1 Flash Lite](https://noometry.com/compare/gemini-3-1-flash-lite-vs-o1)
-   [o1 vs Grok-3 mini](https://noometry.com/compare/grok-3-mini-vs-o1)
-   [o1 vs Claude Sonnet 4](https://noometry.com/compare/claude-sonnet-4-vs-o1)
-   [o1 vs Kimi K2 (Jul 2025)](https://noometry.com/compare/kimi-k2-vs-o1)
-   [o1 vs Qwen2.5-Max](https://noometry.com/compare/o1-vs-qwen2-5-max)
-   [o1 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-o1)
-   [o1 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-o1)
-   [o1 vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-o1)
-   [o1 vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-o1)
-   [o1 vs Qwen3.8 Max](https://noometry.com/compare/o1-vs-qwen3-8-max)
-   [o1 vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-o1)
-   [o1 vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-o1)
-   [o1 vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-o1)

## 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 o1?

o1 by OpenAI ranks 143rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 40.9. Its strongest category is long context, where it ranks 9th. API pricing starts at $15 per million input tokens and $60 per million output tokens, with a 200K-token context window.

### How much does o1 cost?

o1 costs $15 per million input tokens and $60 per million output tokens on OpenAI's own API, with cached input at $7.50.

### What is o1's context window?

o1 accepts up to 200K tokens of input and can write up to 100K tokens in one response.

### Is o1 open source?

No. o1 is proprietary and available only through OpenAI's API and partner platforms.

### What are o1's strengths and weaknesses?

Relative to other ranked models, o1 places best in long context, coding, instruction following and lowest in agentic & tool use, multimodal, math.

### What is o1 best at?

Its best category is long context, where it ranks 9th on Noometry.

### Cite this page

Noometry. (2026). o1 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/o1

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/o1.md).