OpenAI, proprietary
GPT-5 Mini
GPT-5 Mini by OpenAI ranks 128th of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.8. Its strongest category is instruction following, where it ranks 46th. API pricing starts at $0.25 per million input tokens and $2 per million output tokens, with a 400K-token context window.
Last verified
Specifications
- Noometry rank
- #128 of 354
- Index score
- 41.8
- Evidence
- Confirmed 60 results
- Provider
- OpenAI
- Released
- August 7, 2025
- Weights
- Proprietary
- Reasoning
- Yes
- Context window
- 400K
- Max output
- 128K
- Input price
- $0.25 / M
- Output price
- $2 / M
- Blended price
- $0.69 / M
- Output speed
- 3 tokens/s Kagi
- Value
- #90 of 219
- Knowledge cutoff
- May 2024
- Input
- text, image
Category scores
Each category score combines every public result we have in that category.
- Coding 40.1
- Agentic & Tool Use 31.1
- Reasoning 23.9
- Math 46.7
- Knowledge 45.6
- Multimodal 35.6
- Multilingual 48.9
- Instruction Following 76.2
- Long Context 41.9
- Writing & Preference 55.2
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 40.1 | #146 | 6 |
| Agentic & Tool Use | 31.1 | #70 | 2 |
| Reasoning | 23.9 | #168 | 10 |
| Math | 46.7 | #69 | 7 |
| Knowledge | 45.6 | #86 | 8 |
| Multimodal | 35.6 | #85 | 2 |
| Multilingual | 48.9 | #137 | 1 |
| Instruction Following | 76.2 | #46 | 2 |
| Long Context | 41.9 | #132 | 2 |
| Writing & Preference | 55.2 | #148 | 6 |
Strengths and weaknesses
Categories where GPT-5 Mini 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 | 76.2 | +5.0 | #46 of 305, top 16% |
| Math | 46.7 | +10.1 | #69 of 327, top 22% |
| Knowledge | 45.6 | +8.3 | #86 of 314, top 28% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Multimodal | 35.6 | −2.9 | #85 of 128, top 67% |
| Reasoning | 23.9 | +0.3 | #168 of 350, top 48% |
| Writing & Preference | 55.2 | +1.4 | #148 of 312, top 48% |
Closest competitors
The models ranked just above and below GPT-5 Mini. When scores are this close, price and speed are often the better way to choose.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| GLM-4.7 | #124 | 42.0 | $1 | — | Compare |
| GPT-5.4 nano | #125 | 41.9 | $0.46 | 19 | Compare |
| Amazon Nova Experimental Chat 10 09 | #126 | 41.9 | — | — | Compare |
| Qwen3.5 27B | #127 | 41.9 | $0.82 | — | Compare |
| ERNIE 5.0 0110 | #129 | 41.8 | — | — | Compare |
| Granite 4.2 30b | #130 | 41.8 | — | — | Compare |
| Muse Glimmer | #131 | 41.7 | — | — | Compare |
| o4-mini | #132 | 41.6 | $1.93 | 6 | Compare |
Sponsored placements are available on pages like this one. Advertise on Noometry
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 | 64.7% | #26 of 32, top 82% | medium | Epoch AI | 2026-02-01 |
| SWE-bench Verified (bash only) | 59.8% | #20 of 39, top 52% | medium | SWE-bench | 2025-08-07 |
| SWE-bench Multilingual | 39.7% | #13 of 13, top 100% | SWE-bench | 2026-02-13 | |
| SciCode | 39.2% | #83 of 121, top 69% | Epoch AI | ||
| SciCode | 39% | high | Epoch AI | ||
| WeirdML | 52.7% | #42 of 119, top 36% | high | Epoch AI | |
| LMArena Coding | 1406 | #133 of 294, top 46% | high | LMArena | 2026-10-08 |
| ALE-Bench | 799.77 | #57 of 105, top 55% | high | Epoch AI | |
| AlgoTune | 1.38 | #15 of 18, top 84% | high | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Terminal-Bench | 34.8% | #29 of 41, top 71% | Epoch AI | ||
| Terminal-Bench | 31.9% | medium | Epoch AI | ||
| Berkeley Function Calling Leaderboard | 55.5% | #14 of 49, top 29% | fc | Berkeley Function Calling Leaderboard | |
| Vending-Bench 2 | -31.18 | #60 of 60, top 100% | Epoch AI |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| ARC-AGI-2 | 4.4% | #60 of 83, top 73% | high | Epoch AI | |
| ARC-AGI-2 | 0.8% | low | Epoch AI | ||
| ARC-AGI-2 | 4% | medium | Epoch AI | ||
| ARC-AGI-2 | 1.7% | minimal | Epoch AI | ||
| Kagi LLM Benchmark | 70.3% | #24 of 99, top 25% | Kagi LLM Benchmark | ||
| ARC-AGI-1 | 54.3% | #54 of 83, top 66% | high | Epoch AI | |
| ARC-AGI-1 | 26.3% | low | Epoch AI | ||
| ARC-AGI-1 | 37.3% | medium | Epoch AI | ||
| ARC-AGI-1 | 5.3% | minimal | Epoch AI | ||
| CritPt | 0% | #114 of 134, top 86% | Epoch AI | ||
| CritPt | 0% | high | Epoch AI | ||
| Chess Puzzles | 30% | #37 of 129, top 29% | high | Epoch AI | 2026-08-07 |
| Chess Puzzles | 12% | low | Epoch AI | 2026-08-07 | |
| Chess Puzzles | 7% | minimal | Epoch AI | 2026-08-07 | |
| EnigmaEval | 8.2% | #16 of 38, top 43% | Epoch AI | ||
| LMArena Hard Prompts | 1380 | #138 of 297, top 47% | high | LMArena | 2026-10-08 |
| Mystery Game Puzzles | 5% | high | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 4% | medium | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 10% | #60 of 74, top 82% | minimal | Epoch AI | 2026-08-27 |
| DTBench | 80.5% | #71 of 151, top 48% | high | Epoch AI | |
| LMCA | 34.2% | #65 of 125, top 52% | high | Epoch AI | |
| Epoch Capabilities Index | 145.52 | #81 of 213, top 39% | Epoch AI | 2025-08-07 | |
| ForecastBench | 61 | #19 of 72, top 27% | Epoch AI |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| FrontierMath (Tiers 1-3) | 46.7% | #48 of 81, top 60% | high | Epoch AI | 2026-06-12 |
| FrontierMath (Tiers 1-3) | 18.2% | low | Epoch AI | 2026-08-27 | |
| FrontierMath (Tiers 1-3) | 6% | minimal | Epoch AI | 2026-08-27 | |
| FrontierMath Tier 4 | 12.2% | #51 of 63, top 81% | high | Epoch AI | 2026-06-12 |
| OTIS Mock AIME 2024-2025 | 86.7% | #61 of 173, top 36% | high | Epoch AI | 2025-10-30 |
| OTIS Mock AIME 2024-2025 | 78.3% | medium | Epoch AI | 2025-08-07 | |
| OTIS Mock AIME 2024-2025 | 55.6% | minimal | Epoch AI | 2026-08-07 | |
| ProofBench | 9% | #64 of 77, top 84% | high | Epoch AI | |
| Omni-MATH | 72.2% | Best of 57 | HELM Capabilities | ||
| LMArena Math | 1378 | #144 of 285, top 51% | high | LMArena | 2026-10-08 |
| MATH Level 5 | 97.8% | #2 of 79, top 3% | high | Epoch AI | 2025-10-30 |
| MATH Level 5 | 96.8% | medium | Epoch AI | 2025-08-20 | |
| FrontierMath (Feb 2025 set) | 27.2% | #22 of 68, top 33% | high | Epoch AI | 2025-11-13 |
| FrontierMath (Feb 2025 set) | 20.3% | medium | Epoch AI | 2025-11-13 | |
| FrontierMath Tier 4 (v1) | 6.3% | #24 of 55, top 44% | high | Epoch AI | 2025-10-30 |
| FrontierMath Tier 4 (v1) | 4.2% | medium | Epoch AI | 2025-08-07 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 75% | #94 of 186, top 51% | high | Epoch AI | 2025-10-30 |
| GPQA Diamond | 71.7% | medium | Epoch AI | 2025-08-07 | |
| GPQA Diamond | 71.7% | minimal | Epoch AI | 2026-08-07 | |
| Humanity's Last Exam | 19.4% | #19 of 41, top 47% | Epoch AI | ||
| SimpleQA Verified | 21.6% | #63 of 77, top 82% | high | Epoch AI | 2026-08-10 |
| MMLU-Pro | 83.5% | #11 of 58, top 19% | HELM Capabilities | ||
| Confabulations (lower is better) | 13.3% | #11 of 51, top 22% | medium reasoning | Lech Mazur benchmarks | |
| Vectara Hallucination Rate (lower is better) | 12.9% | #79 of 96, top 83% | Vectara Hallucination Leaderboard | ||
| GPQA (HELM) | 75.6% | #3 of 57, top 6% | HELM Capabilities | ||
| LMArena Expert | 1379 | #137 of 273, top 51% | high | LMArena | 2026-10-08 |
Multimodal
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Vision | 1202 | #77 of 122, top 64% | high | LMArena | 2026-10-09 |
| VPCT | 39% | high | Epoch AI | ||
| VPCT | 40.2% | #11 of 24, top 46% | medium | Epoch AI |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1363 | #137 of 297, top 47% | high | LMArena | 2026-10-08 |
| LMArena Chinese | 1385 | #146 of 285, top 52% | high | LMArena | 2026-10-08 |
| LMArena French | 1386 | #128 of 223, top 58% | high | LMArena | 2026-10-08 |
| LMArena German | 1366 | #120 of 231, top 52% | high | LMArena | 2026-10-08 |
| LMArena Japanese | 1341 | #105 of 211, top 50% | high | LMArena | 2026-10-08 |
| LMArena Korean | 1308 | #125 of 213, top 59% | high | LMArena | 2026-10-08 |
| LMArena Russian | 1362 | #140 of 283, top 50% | high | LMArena | 2026-10-08 |
| LMArena Spanish | 1355 | #140 of 226, top 62% | high | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| IFEval | 92.7% | #6 of 57, top 11% | HELM Capabilities | ||
| LMArena Instruction Following | 1357 | #139 of 298, top 47% | high | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Fiction.LiveBench | 69.4% | #17 of 47, top 37% | medium | Epoch AI | |
| LMArena Longer Query | 1355 | #149 of 291, top 52% | high | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1373 | #142 of 297, top 48% | high | LMArena | 2026-10-08 |
| LMArena Creative Writing | 1325 | #150 of 295, top 51% | high | LMArena | 2026-10-08 |
| Short-Story Creative Writing | 83.1% | #8 of 39, top 21% | medium | Epoch AI | |
| EQ-Bench Creative Writing | 1313 | #77 of 115, top 67% | EQ-Bench | ||
| WildBench | 85.5% | #8 of 57, top 15% | HELM Capabilities | ||
| LMArena Multi-Turn | 1363 | #147 of 295, top 50% | high | LMArena | 2026-10-08 |
API pricing by provider
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
|---|---|---|---|---|
| azure | $0.25 | $2 | $0.03 | 2026-10-10 |
| openai | $0.25 | $2 | $0.025 | 2026-10-10 |
| openrouter | $0.25 | $2 | $0.025 | 2026-10-10 |
Compare GPT-5 Mini
- GPT-5 Mini vs GPT-4.1 mini
- GPT-5 Mini vs Qwen3.5 27B
- GPT-5 Mini vs ERNIE 5.0 0110
- GPT-5 Mini vs Amazon Nova Experimental Chat 10 09
- GPT-5 Mini vs Granite 4.2 30b
- GPT-5 Mini vs GPT-5.4 nano
- GPT-5 Mini vs Muse Glimmer
- GPT-5 Mini vs Claude Fable 5.1
- GPT-5 Mini vs Gemini 3.8 Flash
- GPT-5 Mini vs Kimi K3
- GPT-5 Mini vs Grok 4.6
- GPT-5 Mini vs Qwen3.8 Max
- GPT-5 Mini vs GLM-5.3
- GPT-5 Mini vs Muse Spark 1.3
Other OpenAI models
- GPT-6 Astra70.8
- GPT-6.1 Sol65.6
- GPT-5.6 Sol65.0
- GPT-5.5 Pro64.3
- GPT-5.563.4
- GPT-6 Sol61.8
- GPT-5.459.4
- GPT-5.6 Terra59.2
Frequently asked questions
How good is GPT-5 Mini?
GPT-5 Mini by OpenAI ranks 128th of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.8. Its strongest category is instruction following, where it ranks 46th. API pricing starts at $0.25 per million input tokens and $2 per million output tokens, with a 400K-token context window.
How much does GPT-5 Mini cost?
GPT-5 Mini costs $0.25 per million input tokens and $2 per million output tokens on OpenAI's own API, with cached input at $0.025.
What is GPT-5 Mini's context window?
GPT-5 Mini accepts up to 400K tokens of input and can write up to 128K tokens in one response.
Is GPT-5 Mini open source?
No. GPT-5 Mini is proprietary and available only through OpenAI's API and partner platforms.
How fast is GPT-5 Mini?
GPT-5 Mini 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 GPT-5 Mini's strengths and weaknesses?
Relative to other ranked models, GPT-5 Mini places best in instruction following, math, knowledge and lowest in multimodal, reasoning, writing & preference.
What is GPT-5 Mini best at?
Its best category is instruction following, where it ranks 46th on Noometry.