OpenAI, proprietary

# GPT-5 Mini

> GPT-5 Mini by OpenAI, released August 2025. Ranked #128 of 354 with a Noometry Index of 41.8. API: $0.25 in / $2 out per M tokens. 400K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-mini
- Last updated: 2026-10-10
- Title: GPT-5 Mini Benchmarks, Price & Rank (October 2026)

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 October 10, 2026

## Specifications

- **Noometry rank:** #128 of 354
- **Index score:** 41.8
- **Evidence:** Confirmed 60 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** August 7, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 400K
- **Max output:** 128K
- **Input price:** $0.25 / M
- **Output price:** $2 / M
- **Blended price:** $0.69 / M
- **Output speed:** 3 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **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.

GPT-5 Mini category scores

1.  Coding 40.1
2.  Agentic & Tool Use 31.1
3.  Reasoning 23.9
4.  Math 46.7
5.  Knowledge 45.6
6.  Multimodal 35.6
7.  Multilingual 48.9
8.  Instruction Following 76.2
9.  Long Context 41.9
10.  Writing & Preference 55.2
11.  020406080

GPT-5 Mini category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 40.1 | #146 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 31.1 | #70 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 23.9 | #168 | 10 |
| [Math](https://noometry.com/best/math) | 46.7 | #69 | 7 |
| [Knowledge](https://noometry.com/best/knowledge) | 45.6 | #86 | 8 |
| [Multimodal](https://noometry.com/best/multimodal) | 35.6 | #85 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 48.9 | #137 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.2 | #46 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 41.9 | #132 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 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

GPT-5 Mini: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.2 | +5.0 | #46 of 305, top 16% |
| [Math](https://noometry.com/best/math) | 46.7 | +10.1 | #69 of 327, top 22% |
| [Knowledge](https://noometry.com/best/knowledge) | 45.6 | +8.3 | #86 of 314, top 28% |

### Weakest categories

GPT-5 Mini: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 35.6 | −2.9 | #85 of 128, top 67% |
| [Reasoning](https://noometry.com/best/reasoning) | 23.9 | +0.3 | #168 of 350, top 48% |
| [Writing & Preference](https://noometry.com/best/writing) | 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.

Models ranked closest to GPT-5 Mini
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [GLM-4.7](https://noometry.com/models/glm-4-7) | #124 | 42.0 | $1 | — | [Compare](https://noometry.com/compare/glm-4-7-vs-gpt-5-mini) |
| [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | #125 | 41.9 | $0.46 | 19 | [Compare](https://noometry.com/compare/gpt-5-4-nano-vs-gpt-5-mini) |
| [Amazon Nova Experimental Chat 10 09](https://noometry.com/models/amazon-nova-experimental-chat-10-09) | #126 | 41.9 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-gpt-5-mini) |
| [Qwen3.5 27B](https://noometry.com/models/qwen3-5-27b) | #127 | 41.9 | $0.82 | — | [Compare](https://noometry.com/compare/gpt-5-mini-vs-qwen3-5-27b) |
| [ERNIE 5.0 0110](https://noometry.com/models/ernie-5-0) | #129 | 41.8 | — | — | [Compare](https://noometry.com/compare/ernie-5-0-vs-gpt-5-mini) |
| [Granite 4.2 30b](https://noometry.com/models/granite-4-2-30b) | #130 | 41.8 | — | — | [Compare](https://noometry.com/compare/gpt-5-mini-vs-granite-4-2-30b) |
| [Muse Glimmer](https://noometry.com/models/muse-glimmer) | #131 | 41.7 | — | — | [Compare](https://noometry.com/compare/gpt-5-mini-vs-muse-glimmer) |
| [o4-mini](https://noometry.com/models/o4-mini) | #132 | 41.6 | $1.93 | 6 | [Compare](https://noometry.com/compare/gpt-5-mini-vs-o4-mini) |

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 Mini Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 64.7% | #26 of 32, top 82% | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-01 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 59.8% | #20 of 39, top 52% | medium | [SWE-bench](https://www.swebench.com/) | 2025-08-07 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 39.7% | #13 of 13, top 100% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 39.2% | #83 of 121, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 39% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 52.7% | #42 of 119, top 36% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1406 | #133 of 294, top 46% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 799.77 | #57 of 105, top 55% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.38 | #15 of 18, top 84% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5 Mini Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 34.8% | #29 of 41, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 31.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 55.5% | #14 of 49, top 29% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | \-31.18 | #60 of 60, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-5 Mini Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4.4% | #60 of 83, top 73% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.7% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 70.3% | #24 of 99, top 25% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 54.3% | #54 of 83, top 66% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 26.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 37.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 5.3% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #114 of 134, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 30% | #37 of 129, top 29% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 12% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 7% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 8.2% | #16 of 38, top 43% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1380 | #138 of 297, top 47% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 10% | #60 of 74, top 82% | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 80.5% | #71 of 151, top 48% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 34.2% | #65 of 125, top 52% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 145.52 | #81 of 213, top 39% |  | [Epoch AI](https://epoch.ai/eci) | 2025-08-07 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61 | #19 of 72, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-5 Mini Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 46.7% | #48 of 81, top 60% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-12 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 18.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 6% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 12.2% | #51 of 63, top 81% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-12 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 86.7% | #61 of 173, top 36% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 78.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 55.6% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 9% | #64 of 77, top 84% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 72.2% | Best of 57 |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1378 | #144 of 285, top 51% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 97.8% | #2 of 79, top 3% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 96.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-20 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 27.2% | #22 of 68, top 33% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 20.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 6.3% | #24 of 55, top 44% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 4.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |

### Knowledge

GPT-5 Mini Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 75% | #94 of 186, top 51% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-30 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.7% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 19.4% | #19 of 41, top 47% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 21.6% | #63 of 77, top 82% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 83.5% | #11 of 58, top 19% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 13.3% | #11 of 51, top 22% | medium reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12.9% | #79 of 96, top 83% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 75.6% | #3 of 57, top 6% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1379 | #137 of 273, top 51% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5 Mini Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1202 | #77 of 122, top 64% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [VPCT](https://noometry.com/benchmarks/vpct) | 39% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 40.2% | #11 of 24, top 46% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

GPT-5 Mini Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1363 | #137 of 297, top 47% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1385 | #146 of 285, top 52% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1386 | #128 of 223, top 58% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1366 | #120 of 231, top 52% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1341 | #105 of 211, top 50% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1308 | #125 of 213, top 59% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1362 | #140 of 283, top 50% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1355 | #140 of 226, top 62% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5 Mini Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 92.7% | #6 of 57, top 11% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1357 | #139 of 298, top 47% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5 Mini Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 69.4% | #17 of 47, top 37% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1355 | #149 of 291, top 52% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5 Mini Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1373 | #142 of 297, top 48% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1325 | #150 of 295, top 51% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 83.1% | #8 of 39, top 21% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1313 | #77 of 115, top 67% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 85.5% | #8 of 57, top 15% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1363 | #147 of 295, top 50% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-5 Mini API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.25 | $2 | $0.03 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.25 | $2 | $0.025 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5-mini) | $0.25 | $2 | $0.025 | 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 Mini

-   [GPT-5 Mini vs GPT-4.1 mini](https://noometry.com/compare/gpt-4-1-mini-vs-gpt-5-mini)
-   [GPT-5 Mini vs Qwen3.5 27B](https://noometry.com/compare/gpt-5-mini-vs-qwen3-5-27b)
-   [GPT-5 Mini vs ERNIE 5.0 0110](https://noometry.com/compare/ernie-5-0-vs-gpt-5-mini)
-   [GPT-5 Mini vs Amazon Nova Experimental Chat 10 09](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-gpt-5-mini)
-   [GPT-5 Mini vs Granite 4.2 30b](https://noometry.com/compare/gpt-5-mini-vs-granite-4-2-30b)
-   [GPT-5 Mini vs GPT-5.4 nano](https://noometry.com/compare/gpt-5-4-nano-vs-gpt-5-mini)
-   [GPT-5 Mini vs Muse Glimmer](https://noometry.com/compare/gpt-5-mini-vs-muse-glimmer)
-   [GPT-5 Mini vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-mini)
-   [GPT-5 Mini vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-mini)
-   [GPT-5 Mini vs Kimi K3](https://noometry.com/compare/gpt-5-mini-vs-kimi-k3)
-   [GPT-5 Mini vs Grok 4.6](https://noometry.com/compare/gpt-5-mini-vs-grok-4-6)
-   [GPT-5 Mini vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-mini-vs-qwen3-8-max)
-   [GPT-5 Mini vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-mini)
-   [GPT-5 Mini vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-mini-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 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.

### Cite this page

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

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