OpenAI, proprietary

# o3

> o3 by OpenAI, released April 2025. Ranked #61 of 354 with a Noometry Index of 47.5. API: $2 in / $8 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/o3
- Last updated: 2026-10-10
- Title: o3 Benchmarks, Price & Rank (October 2026) | Noometry

o3 by OpenAI ranks 61st of 354 ranked models on the Noometry Index as of October 2026, with a score of 47.5. Its strongest category is long context, where it ranks 6th. API pricing starts at $2 per million input tokens and $8 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #61 of 354
- **Index score:** 47.5
- **Evidence:** Confirmed 63 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** April 16, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 100K
- **Input price:** $2 / M
- **Output price:** $8 / M
- **Blended price:** $3.50 / M
- **Output speed:** 3 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #168 of 219
- **Knowledge cutoff:** May 2024
- **Input:** text, image, pdf

## Category scores

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

o3 category scores

1.  Coding 46.8
2.  Agentic & Tool Use 34.5
3.  Reasoning 32.0
4.  Math 50.2
5.  Knowledge 54.6
6.  Multimodal 41.4
7.  Multilingual 51.7
8.  Instruction Following 72.8
9.  Long Context 53.3
10.  Writing & Preference 63.5
11.  020406080

o3 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 46.8 | #64 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.5 | #44 | 4 |
| [Reasoning](https://noometry.com/best/reasoning) | 32.0 | #78 | 11 |
| [Math](https://noometry.com/best/math) | 50.2 | #58 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 54.6 | #52 | 7 |
| [Multimodal](https://noometry.com/best/multimodal) | 41.4 | #36 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 51.7 | #105 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 72.8 | #127 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 53.3 | #6 | 3 |
| [Writing & Preference](https://noometry.com/best/writing) | 63.5 | #64 | 6 |

## Strengths and weaknesses

Categories where o3 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

o3: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 53.3 | +12.3 | #6 of 296, top 3% |
| [Knowledge](https://noometry.com/best/knowledge) | 54.6 | +17.3 | #52 of 314, top 17% |
| [Math](https://noometry.com/best/math) | 50.2 | +13.6 | #58 of 327, top 18% |

### Weakest categories

o3: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 72.8 | +1.5 | #127 of 305, top 42% |
| [Multilingual](https://noometry.com/best/multilingual) | 51.7 | +4.3 | #105 of 297, top 36% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.5 | +4.1 | #44 of 154, top 29% |

## Closest competitors

The models ranked just above and below o3. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to o3
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | #57 | 48.1 | $0.90 | 66 | [Compare](https://noometry.com/compare/kimi-k2-5-vs-o3) |
| [Step 5 Preview](https://noometry.com/models/step-5-preview) | #58 | 47.9 | $1.43 | — | [Compare](https://noometry.com/compare/o3-vs-step-5-preview) |
| [GLM-5.1](https://noometry.com/models/glm-5-1) | #59 | 47.8 | $2.15 | — | [Compare](https://noometry.com/compare/glm-5-1-vs-o3) |
| [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | #60 | 47.7 | $1.71 | — | [Compare](https://noometry.com/compare/kimi-k2-6-vs-o3) |
| [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus) | #62 | 47.5 | $1.13 | — | [Compare](https://noometry.com/compare/o3-vs-qwen3-6-plus) |
| [Inkling-Small](https://noometry.com/models/inkling-small) | #63 | 46.5 | $0.64 | — | [Compare](https://noometry.com/compare/inkling-small-vs-o3) |
| [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | #64 | 46.4 | $41.25 | 5 | [Compare](https://noometry.com/compare/gpt-5-pro-vs-o3) |
| [Grok 4.20 Multi-Agent](https://noometry.com/models/grok-4-20-multi-agent) | #65 | 46.2 | $1.56 | — | [Compare](https://noometry.com/compare/grok-4-20-multi-agent-vs-o3) |

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

o3 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 62.3% | #27 of 32, top 85% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 58.4% | #21 of 39, top 54% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 76.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 81.3% | #4 of 44, top 10% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 76.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 8.8% | #18 of 31, top 59% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 52.4% | #44 of 119, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1408 | #132 of 294, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [CadEval](https://noometry.com/benchmarks/cadeval) | 74% | Best of 14 | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 933.55 | #46 of 105, top 44% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

o3 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 63% | #7 of 49, top 15% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [GDPval](https://noometry.com/benchmarks/gdpval) | 30.8% | #7 of 11, top 64% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 45.2% | #16 of 24, top 67% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld](https://noometry.com/benchmarks/osworld) | 23% | #7 of 8, top 88% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1144 | #25 of 32, top 79% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 63.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 65.4% | #14 of 32, top 44% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

o3 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 6.5% | #51 of 83, top 62% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 53.1% | #38 of 77, top 50% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 67.6% | #27 of 99, top 28% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 60.8% | #50 of 83, top 61% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 41.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 53.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.4% | #77 of 134, top 58% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 34% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 27% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 38% | #26 of 129, top 21% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 11.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 13.1% | #10 of 38, top 27% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1402 | #124 of 297, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 29% | #29 of 74, top 40% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 23% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 84.8% | #54 of 151, top 36% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 39.7% | #46 of 125, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 146.86 | #67 of 213, top 32% |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-16 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 62.5 | Best of 72 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

o3 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 33.3% | #60 of 81, top 75% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 19.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 29.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 83.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 60% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 84.4% | #71 of 173, top 42% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 71.4% | #4 of 57, top 8% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1426 | #93 of 285, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 97.8% | #4 of 79, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 18.7% | #32 of 68, top 48% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 9.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-17 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 16.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-16 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% | #42 of 55, top 77% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-07-01 |

### Knowledge

o3 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 81.8% | #78 of 186, top 42% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 79.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 80.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 20.3% | #18 of 41, top 44% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 19.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 49.4% | #26 of 77, top 34% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 85.9% | #6 of 58, top 11% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 14.4% | #16 of 51, top 32% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 75.3% | #4 of 57, top 8% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1402 | #120 of 273, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

o3 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1214 | #73 of 122, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 60% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 74% | #10 of 25, top 40% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 52% | #7 of 24, top 30% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

o3 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1401 | #105 of 297, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1437 | #114 of 285, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1430 | #92 of 223, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1420 | #77 of 231, top 34% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1403 | #59 of 211, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1370 | #87 of 213, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1406 | #98 of 283, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1395 | #116 of 226, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

o3 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 86.9% | #16 of 57, top 29% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1368 | #131 of 298, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

o3 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 88.9% | #6 of 47, top 13% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 17.8% | #12 of 19, top 64% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1372 | #139 of 291, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

o3 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1410 | #110 of 297, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1359 | #122 of 295, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 83.9% | #5 of 39, top 13% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1676 | #35 of 115, top 31% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 86.1% | #4 of 57, top 8% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1405 | #117 of 295, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

o3 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/o3) | $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 o3

-   [o3 vs o1](https://noometry.com/compare/o1-vs-o3)
-   [o3 vs Kimi K2.6](https://noometry.com/compare/kimi-k2-6-vs-o3)
-   [o3 vs Qwen3.6 Plus](https://noometry.com/compare/o3-vs-qwen3-6-plus)
-   [o3 vs GLM-5.1](https://noometry.com/compare/glm-5-1-vs-o3)
-   [o3 vs Inkling-Small](https://noometry.com/compare/inkling-small-vs-o3)
-   [o3 vs Step 5 Preview](https://noometry.com/compare/o3-vs-step-5-preview)
-   [o3 vs GPT-5 Pro](https://noometry.com/compare/gpt-5-pro-vs-o3)
-   [o3 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-o3)
-   [o3 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-o3)
-   [o3 vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-o3)
-   [o3 vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-o3)
-   [o3 vs Qwen3.8 Max](https://noometry.com/compare/o3-vs-qwen3-8-max)
-   [o3 vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-o3)
-   [o3 vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-o3)

## 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 o3?

o3 by OpenAI ranks 61st of 354 ranked models on the Noometry Index as of October 2026, with a score of 47.5. Its strongest category is long context, where it ranks 6th. API pricing starts at $2 per million input tokens and $8 per million output tokens, with a 200K-token context window.

### How much does o3 cost?

o3 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 o3's context window?

o3 accepts up to 200K tokens of input and can write up to 100K tokens in one response.

### Is o3 open source?

No. o3 is proprietary and available only through OpenAI's API and partner platforms.

### How fast is o3?

o3 generated about 3 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are o3's strengths and weaknesses?

Relative to other ranked models, o3 places best in long context, knowledge, math and lowest in instruction following, multilingual, agentic & tool use.

### What is o3 best at?

Its best category is long context, where it ranks 6th on Noometry.

### Cite this page

Noometry. (2026). o3 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/o3

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/o3.md).