OpenAI, proprietary

# GPT-5 Nano

> GPT-5 Nano by OpenAI, released August 2025. Ranked #241 of 354 with a Noometry Index of 33.5. API: $0.05 in / $0.40 out per M tokens. 400K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-nano
- Last updated: 2026-10-10
- Title: GPT-5 Nano Benchmarks, Price & Rank (October 2026)

GPT-5 Nano by OpenAI ranks 241st of 354 ranked models on the Noometry Index as of October 2026, with a score of 33.5. Its strongest category is instruction following, where it ranks 79th. API pricing starts at $0.05 per million input tokens and $0.40 per million output tokens, with a 400K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #241 of 354
- **Index score:** 33.5
- **Evidence:** Confirmed 49 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** August 7, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 400K
- **Max output:** 128K
- **Input price:** $0.05 / M
- **Output price:** $0.40 / M
- **Blended price:** $0.14 / M
- **Output speed:** 4 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #31 of 219
- **Knowledge cutoff:** May 2024
- **Input:** text, image

## Category scores

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

GPT-5 Nano category scores

1.  Coding 33.6
2.  Agentic & Tool Use 25.8
3.  Reasoning 16.3
4.  Math 29.4
5.  Knowledge 35.9
6.  Multimodal 31.3
7.  Multilingual 45.3
8.  Instruction Following 75.0
9.  Long Context 31.3
10.  Writing & Preference 39.1
11.  020406080

GPT-5 Nano category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 33.6 | #254 | 3 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.8 | #106 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 16.3 | #306 | 8 |
| [Math](https://noometry.com/best/math) | 29.4 | #241 | 7 |
| [Knowledge](https://noometry.com/best/knowledge) | 35.9 | #178 | 6 |
| [Multimodal](https://noometry.com/best/multimodal) | 31.3 | #108 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 45.3 | #172 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.0 | #79 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 31.3 | #281 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 39.1 | #249 | 5 |

## Strengths and weaknesses

Categories where GPT-5 Nano places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-5 Nano: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.0 | +3.7 | #79 of 305, top 26% |
| [Knowledge](https://noometry.com/best/knowledge) | 35.9 | −1.4 | #178 of 314, top 57% |
| [Multilingual](https://noometry.com/best/multilingual) | 45.3 | −2.1 | #172 of 297, top 58% |

### Weakest categories

GPT-5 Nano: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 31.3 | −9.7 | #281 of 296, top 95% |
| [Reasoning](https://noometry.com/best/reasoning) | 16.3 | −7.3 | #306 of 350, top 88% |
| [Multimodal](https://noometry.com/best/multimodal) | 31.3 | −7.2 | #108 of 128, top 85% |

## Closest competitors

The models ranked just above and below GPT-5 Nano. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-5 Nano
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Codellama 70b Instruct](https://noometry.com/models/codellama-70b-instruct) | #237 | 33.7 | — | — | [Compare](https://noometry.com/compare/codellama-70b-instruct-vs-gpt-5-nano) |
| [Qwen3 8B](https://noometry.com/models/qwen3-8b) | #238 | 33.7 | $0.31 | — | [Compare](https://noometry.com/compare/gpt-5-nano-vs-qwen3-8b) |
| [Grok-2 (Dec 2024)](https://noometry.com/models/grok-2) | #239 | 33.7 | — | — | [Compare](https://noometry.com/compare/gpt-5-nano-vs-grok-2) |
| [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | #240 | 33.6 | $0.70 | 86 | [Compare](https://noometry.com/compare/gpt-4-1-mini-vs-gpt-5-nano) |
| [Mercury 2.5](https://noometry.com/models/mercury-2-5) | #242 | 33.5 | $0.0675 | — | [Compare](https://noometry.com/compare/gpt-5-nano-vs-mercury-2-5) |
| [Mistral Small](https://noometry.com/models/mistral-small) | #243 | 33.4 | $0.26 | 120 | [Compare](https://noometry.com/compare/gpt-5-nano-vs-mistral-small) |
| [Nova 2.0 Pro Preview](https://noometry.com/models/nova-2-0-pro-preview) | #244 | 33.4 | — | — | [Compare](https://noometry.com/compare/gpt-5-nano-vs-nova-2-0-pro-preview) |
| [Qwen2.5-Coder-32B](https://noometry.com/models/qwen2-5-coder-32b) | #245 | 33.4 | $0.74 | — | [Compare](https://noometry.com/compare/gpt-5-nano-vs-qwen2-5-coder-32b) |

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 Nano Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 34.8% | #31 of 39, top 80% | medium | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 38.1% | #84 of 119, top 71% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 25.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1351 | #172 of 294, top 59% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 718.67 | #68 of 105, top 65% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5 Nano Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 21.8% | #37 of 41, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 11.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 51.5% | #18 of 49, top 37% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

GPT-5 Nano Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 2.6% | #64 of 83, top 78% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 62.2% | #39 of 99, top 40% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 16.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 20.7% | #69 of 83, top 84% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 1.5% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 27% | #39 of 129, top 31% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 15% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 1% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1328 | #175 of 297, top 59% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 9% | #62 of 74, top 84% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 5% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 62.7% | #109 of 151, top 73% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 7.9% | #117 of 125, top 94% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 139.38 | #112 of 213, top 53% |  | [Epoch AI](https://epoch.ai/eci) | 2025-08-07 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59.1 | #42 of 72, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-5 Nano Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 20% | #69 of 81, top 86% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-12 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 1.8% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 2.4% | #60 of 63, top 96% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-12 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 81.1% | #79 of 173, top 46% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-31 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 46.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 74.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 35.6% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 12% | #61 of 77, top 80% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 54.6% | #15 of 57, top 27% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1317 | #171 of 285, top 60% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 94.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-20 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 95.2% | #11 of 79, top 14% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-20 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 8.3% | #40 of 68, top 59% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 7.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 0% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% | #40 of 55, top 73% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |

### Knowledge

GPT-5 Nano Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 69.4% | #99 of 186, top 54% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 57.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 67.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 48.5% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 11.7% | #74 of 77, top 97% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 77.8% | #23 of 58, top 40% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 10.5% | #58 of 96, top 61% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 67.9% | #13 of 57, top 23% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1321 | #168 of 273, top 62% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5 Nano Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1159 | #96 of 122, top 79% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [VPCT](https://noometry.com/benchmarks/vpct) | 37.2% | #17 of 24, top 71% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 35.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

GPT-5 Nano Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1313 | #172 of 297, top 58% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1356 | #165 of 285, top 58% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1327 | #143 of 231, top 62% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1226 | #148 of 211, top 71% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1269 | #140 of 213, top 66% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1296 | #179 of 283, top 64% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1360 | #135 of 226, top 60% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5 Nano Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 93.2% | #4 of 57, top 8% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1306 | #172 of 298, top 58% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5 Nano Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 44.4% | #40 of 47, top 86% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1312 | #179 of 291, top 62% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5 Nano Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1320 | #178 of 297, top 60% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1249 | #205 of 295, top 70% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 705 | #111 of 115, top 97% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 80.6% | #27 of 57, top 48% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1311 | #182 of 295, top 62% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-5 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) | $0.05 | $0.40 | $0.01 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.05 | $0.40 | $0.005 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5-nano) | $0.05 | $0.40 | $0.005 | 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 Nano

-   [GPT-5 Nano vs GPT-4.1 nano](https://noometry.com/compare/gpt-4-1-nano-vs-gpt-5-nano)
-   [GPT-5 Nano vs GPT-4.1 mini](https://noometry.com/compare/gpt-4-1-mini-vs-gpt-5-nano)
-   [GPT-5 Nano vs Mercury 2.5](https://noometry.com/compare/gpt-5-nano-vs-mercury-2-5)
-   [GPT-5 Nano vs Grok-2 (Dec 2024)](https://noometry.com/compare/gpt-5-nano-vs-grok-2)
-   [GPT-5 Nano vs Mistral Small](https://noometry.com/compare/gpt-5-nano-vs-mistral-small)
-   [GPT-5 Nano vs Qwen3 8B](https://noometry.com/compare/gpt-5-nano-vs-qwen3-8b)
-   [GPT-5 Nano vs Nova 2.0 Pro Preview](https://noometry.com/compare/gpt-5-nano-vs-nova-2-0-pro-preview)
-   [GPT-5 Nano vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-nano)
-   [GPT-5 Nano vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-nano)
-   [GPT-5 Nano vs Kimi K3](https://noometry.com/compare/gpt-5-nano-vs-kimi-k3)
-   [GPT-5 Nano vs Grok 4.6](https://noometry.com/compare/gpt-5-nano-vs-grok-4-6)
-   [GPT-5 Nano vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-nano-vs-qwen3-8-max)
-   [GPT-5 Nano vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-nano)
-   [GPT-5 Nano vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-nano-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 Nano?

GPT-5 Nano by OpenAI ranks 241st of 354 ranked models on the Noometry Index as of October 2026, with a score of 33.5. Its strongest category is instruction following, where it ranks 79th. API pricing starts at $0.05 per million input tokens and $0.40 per million output tokens, with a 400K-token context window.

### How much does GPT-5 Nano cost?

GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens on OpenAI's own API, with cached input at $0.005.

### What is GPT-5 Nano's context window?

GPT-5 Nano accepts up to 400K tokens of input and can write up to 128K tokens in one response.

### Is GPT-5 Nano open source?

No. GPT-5 Nano is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-5 Nano?

GPT-5 Nano generated about 4 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-5 Nano's strengths and weaknesses?

Relative to other ranked models, GPT-5 Nano places best in instruction following, knowledge, multilingual and lowest in long context, reasoning, multimodal.

### What is GPT-5 Nano best at?

Its best category is instruction following, where it ranks 79th on Noometry.

### Cite this page

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

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