OpenAI, proprietary
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.
Last verified
Specifications
- Noometry rank
- #241 of 354
- Index score
- 33.5
- Evidence
- Confirmed 49 results
- Provider
- 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
- 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.
- Coding 33.6
- Agentic & Tool Use 25.8
- Reasoning 16.3
- Math 29.4
- Knowledge 35.9
- Multimodal 31.3
- Multilingual 45.3
- Instruction Following 75.0
- Long Context 31.3
- Writing & Preference 39.1
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 33.6 | #254 | 3 |
| Agentic & Tool Use | 25.8 | #106 | 2 |
| Reasoning | 16.3 | #306 | 8 |
| Math | 29.4 | #241 | 7 |
| Knowledge | 35.9 | #178 | 6 |
| Multimodal | 31.3 | #108 | 2 |
| Multilingual | 45.3 | #172 | 1 |
| Instruction Following | 75.0 | #79 | 2 |
| Long Context | 31.3 | #281 | 2 |
| Writing & Preference | 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
| Category | Score | vs median | Rank |
|---|---|---|---|
| Instruction Following | 75.0 | +3.7 | #79 of 305, top 26% |
| Knowledge | 35.9 | −1.4 | #178 of 314, top 57% |
| Multilingual | 45.3 | −2.1 | #172 of 297, top 58% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Long Context | 31.3 | −9.7 | #281 of 296, top 95% |
| Reasoning | 16.3 | −7.3 | #306 of 350, top 88% |
| 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.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| Codellama 70b Instruct | #237 | 33.7 | — | — | Compare |
| Qwen3 8B | #238 | 33.7 | $0.31 | — | Compare |
| Grok-2 (Dec 2024) | #239 | 33.7 | — | — | Compare |
| GPT-4.1 mini | #240 | 33.6 | $0.70 | 86 | Compare |
| Mercury 2.5 | #242 | 33.5 | $0.0675 | — | Compare |
| Mistral Small | #243 | 33.4 | $0.26 | 120 | Compare |
| Nova 2.0 Pro Preview | #244 | 33.4 | — | — | Compare |
| Qwen2.5-Coder-32B | #245 | 33.4 | $0.74 | — | Compare |
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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
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| SWE-bench Verified (bash only) | 34.8% | #31 of 39, top 80% | medium | SWE-bench | 2025-08-07 |
| WeirdML | 38.1% | #84 of 119, top 71% | high | Epoch AI | |
| WeirdML | 25.9% | low | Epoch AI | ||
| LMArena Coding | 1351 | #172 of 294, top 59% | high | LMArena | 2026-10-08 |
| ALE-Bench | 718.67 | #68 of 105, top 65% | high | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Terminal-Bench | 21.8% | #37 of 41, top 91% | Epoch AI | ||
| Terminal-Bench | 11.5% | medium | Epoch AI | ||
| Berkeley Function Calling Leaderboard | 51.5% | #18 of 49, top 37% | fc | Berkeley Function Calling Leaderboard |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| ARC-AGI-2 | 2.6% | #64 of 83, top 78% | high | Epoch AI | |
| ARC-AGI-2 | 0% | low | Epoch AI | ||
| ARC-AGI-2 | 0.9% | medium | Epoch AI | ||
| ARC-AGI-2 | 0% | minimal | Epoch AI | ||
| Kagi LLM Benchmark | 62.2% | #39 of 99, top 40% | Kagi LLM Benchmark | ||
| ARC-AGI-1 | 16.7% | high | Epoch AI | ||
| ARC-AGI-1 | 4% | low | Epoch AI | ||
| ARC-AGI-1 | 20.7% | #69 of 83, top 84% | medium | Epoch AI | |
| ARC-AGI-1 | 1.5% | minimal | Epoch AI | ||
| Chess Puzzles | 27% | #39 of 129, top 31% | high | Epoch AI | 2026-08-07 |
| Chess Puzzles | 15% | low | Epoch AI | 2026-07-13 | |
| Chess Puzzles | 1% | minimal | Epoch AI | 2026-07-13 | |
| LMArena Hard Prompts | 1328 | #175 of 297, top 59% | high | LMArena | 2026-10-08 |
| Mystery Game Puzzles | 8% | high | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 9% | #62 of 74, top 84% | low | Epoch AI | 2026-08-27 |
| Mystery Game Puzzles | 9% | medium | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 5% | minimal | Epoch AI | 2026-08-27 | |
| DTBench | 62.7% | #109 of 151, top 73% | high | Epoch AI | |
| LMCA | 7.9% | #117 of 125, top 94% | high | Epoch AI | |
| Epoch Capabilities Index | 139.38 | #112 of 213, top 53% | Epoch AI | 2025-08-07 | |
| ForecastBench | 59.1 | #42 of 72, top 59% | Epoch AI |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | #69 of 81, top 86% | high | Epoch AI | 2026-06-12 |
| FrontierMath (Tiers 1-3) | 6% | low | Epoch AI | 2026-08-27 | |
| FrontierMath (Tiers 1-3) | 1.8% | minimal | Epoch AI | 2026-08-27 | |
| FrontierMath Tier 4 | 2.4% | #60 of 63, top 96% | high | Epoch AI | 2026-06-12 |
| OTIS Mock AIME 2024-2025 | 81.1% | #79 of 173, top 46% | high | Epoch AI | 2025-10-31 |
| OTIS Mock AIME 2024-2025 | 46.7% | low | Epoch AI | 2026-07-13 | |
| OTIS Mock AIME 2024-2025 | 74.2% | medium | Epoch AI | 2025-08-07 | |
| OTIS Mock AIME 2024-2025 | 35.6% | minimal | Epoch AI | 2026-07-13 | |
| ProofBench | 12% | #61 of 77, top 80% | high | Epoch AI | |
| Omni-MATH | 54.6% | #15 of 57, top 27% | HELM Capabilities | ||
| LMArena Math | 1317 | #171 of 285, top 60% | high | LMArena | 2026-10-08 |
| MATH Level 5 | 94.9% | high | Epoch AI | 2025-08-20 | |
| MATH Level 5 | 95.2% | #11 of 79, top 14% | medium | Epoch AI | 2025-08-20 |
| FrontierMath (Feb 2025 set) | 8.3% | #40 of 68, top 59% | high | Epoch AI | 2025-10-30 |
| FrontierMath (Feb 2025 set) | 7.2% | medium | Epoch AI | 2025-08-07 | |
| FrontierMath Tier 4 (v1) | 0% | high | Epoch AI | 2025-10-30 | |
| FrontierMath Tier 4 (v1) | 2.1% | #40 of 55, top 73% | medium | Epoch AI | 2025-08-07 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 69.4% | #99 of 186, top 54% | high | Epoch AI | 2025-10-30 |
| GPQA Diamond | 57.6% | low | Epoch AI | 2026-07-13 | |
| GPQA Diamond | 67.4% | medium | Epoch AI | 2025-08-07 | |
| GPQA Diamond | 48.5% | minimal | Epoch AI | 2026-07-13 | |
| SimpleQA Verified | 11.7% | #74 of 77, top 97% | high | Epoch AI | 2026-08-10 |
| MMLU-Pro | 77.8% | #23 of 58, top 40% | HELM Capabilities | ||
| Vectara Hallucination Rate (lower is better) | 10.5% | #58 of 96, top 61% | Vectara Hallucination Leaderboard | ||
| GPQA (HELM) | 67.9% | #13 of 57, top 23% | HELM Capabilities | ||
| LMArena Expert | 1321 | #168 of 273, top 62% | high | LMArena | 2026-10-08 |
Multimodal
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Vision | 1159 | #96 of 122, top 79% | high | LMArena | 2026-10-09 |
| VPCT | 37.2% | #17 of 24, top 71% | high | Epoch AI | |
| VPCT | 35.4% | medium | Epoch AI |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1313 | #172 of 297, top 58% | high | LMArena | 2026-10-08 |
| LMArena Chinese | 1356 | #165 of 285, top 58% | high | LMArena | 2026-10-08 |
| LMArena German | 1327 | #143 of 231, top 62% | high | LMArena | 2026-10-08 |
| LMArena Japanese | 1226 | #148 of 211, top 71% | high | LMArena | 2026-10-08 |
| LMArena Korean | 1269 | #140 of 213, top 66% | high | LMArena | 2026-10-08 |
| LMArena Russian | 1296 | #179 of 283, top 64% | high | LMArena | 2026-10-08 |
| LMArena Spanish | 1360 | #135 of 226, top 60% | high | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| IFEval | 93.2% | #4 of 57, top 8% | HELM Capabilities | ||
| LMArena Instruction Following | 1306 | #172 of 298, top 58% | high | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Fiction.LiveBench | 44.4% | #40 of 47, top 86% | medium | Epoch AI | |
| LMArena Longer Query | 1312 | #179 of 291, top 62% | high | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1320 | #178 of 297, top 60% | high | LMArena | 2026-10-08 |
| LMArena Creative Writing | 1249 | #205 of 295, top 70% | high | LMArena | 2026-10-08 |
| EQ-Bench Creative Writing | 705 | #111 of 115, top 97% | EQ-Bench | ||
| WildBench | 80.6% | #27 of 57, top 48% | HELM Capabilities | ||
| LMArena Multi-Turn | 1311 | #182 of 295, top 62% | high | LMArena | 2026-10-08 |
API pricing by provider
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
|---|---|---|---|---|
| azure | $0.05 | $0.40 | $0.01 | 2026-10-10 |
| openai | $0.05 | $0.40 | $0.005 | 2026-10-10 |
| openrouter | $0.05 | $0.40 | $0.005 | 2026-10-10 |
Compare GPT-5 Nano
- GPT-5 Nano vs GPT-4.1 nano
- GPT-5 Nano vs GPT-4.1 mini
- GPT-5 Nano vs Mercury 2.5
- GPT-5 Nano vs Grok-2 (Dec 2024)
- GPT-5 Nano vs Mistral Small
- GPT-5 Nano vs Qwen3 8B
- GPT-5 Nano vs Nova 2.0 Pro Preview
- GPT-5 Nano vs Claude Fable 5.1
- GPT-5 Nano vs Gemini 3.8 Flash
- GPT-5 Nano vs Kimi K3
- GPT-5 Nano vs Grok 4.6
- GPT-5 Nano vs Qwen3.8 Max
- GPT-5 Nano vs GLM-5.3
- GPT-5 Nano vs Muse Spark 1.3
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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.