OpenAI, proprietary

# GPT-4o mini

> GPT-4o mini by OpenAI, released July 2024. Ranked #343 of 354 with a Noometry Index of 25.5. API: $0.15 in / $0.60 out per M tokens. 128K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-4o-mini
- Last updated: 2026-10-10
- Title: GPT-4o mini Benchmarks, Price & Rank (October 2026)

GPT-4o mini by OpenAI ranks 343rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 25.5. Its strongest category is agentic & tool use, where it ranks 101st. API pricing starts at $0.15 per million input tokens and $0.60 per million output tokens, with a 128K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #343 of 354
- **Index score:** 25.5
- **Evidence:** Confirmed 60 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** July 18, 2024
- **Weights:** Proprietary
- **Reasoning:** No
- **Context window:** 128K
- **Max output:** 16K
- **Input price:** $0.15 / M
- **Output price:** $0.60 / M
- **Blended price:** $0.26 / M
- **Output speed:** 120 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #63 of 219
- **Knowledge cutoff:** September 2023
- **Input:** text, image, pdf

## Category scores

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

GPT-4o mini category scores

1.  Coding 22.0
2.  Agentic & Tool Use 27.5
3.  Reasoning 8.7
4.  Math 10.4
5.  Knowledge 17.7
6.  Multimodal 25.9
7.  Multilingual 42.0
8.  Instruction Following 61.9
9.  Long Context 39.1
10.  Writing & Preference 39.5
11.  020406080

GPT-4o mini category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 22.0 | #335 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 27.5 | #101 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 8.7 | #347 | 10 |
| [Math](https://noometry.com/best/math) | 10.4 | #314 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 17.7 | #284 | 6 |
| [Multimodal](https://noometry.com/best/multimodal) | 25.9 | #122 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 42.0 | #199 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 61.9 | #239 | 3 |
| [Long Context](https://noometry.com/best/long-context) | 39.1 | #186 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 39.5 | #248 | 7 |

## Strengths and weaknesses

Categories where GPT-4o mini places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-4o mini: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 39.1 | −1.8 | #186 of 296, top 63% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 27.5 | −2.9 | #101 of 154, top 66% |
| [Multilingual](https://noometry.com/best/multilingual) | 42.0 | −5.4 | #199 of 297, top 68% |

### Weakest categories

GPT-4o mini: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 8.7 | −14.9 | #347 of 350, top 100% |
| [Coding](https://noometry.com/best/coding) | 22.0 | −16.8 | #335 of 340, top 99% |
| [Math](https://noometry.com/best/math) | 10.4 | −26.2 | #314 of 327, top 97% |

## Closest competitors

The models ranked just above and below GPT-4o mini. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-4o mini
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [DeepSeek-R1-Distill-Qwen-1.5B](https://noometry.com/models/deepseek-r1-distill-qwen-1-5b) | #339 | 26.1 | — | — | [Compare](https://noometry.com/compare/deepseek-r1-distill-qwen-1-5b-vs-gpt-4o-mini) |
| [Claude 3 Haiku](https://noometry.com/models/claude-3-haiku) | #340 | 25.9 | — | 41 | [Compare](https://noometry.com/compare/claude-3-haiku-vs-gpt-4o-mini) |
| [Gemma 2 9B](https://noometry.com/models/gemma-2-9b) | #341 | 25.9 | — | — | [Compare](https://noometry.com/compare/gemma-2-9b-vs-gpt-4o-mini) |
| [Dolly 2.0-12b](https://noometry.com/models/dolly-2-0-12b) | #342 | 25.5 | — | — | [Compare](https://noometry.com/compare/dolly-2-0-12b-vs-gpt-4o-mini) |
| [Llama 3-8B](https://noometry.com/models/llama-3-8b) | #344 | 25.5 | — | — | [Compare](https://noometry.com/compare/gpt-4o-mini-vs-llama-3-8b) |
| [Claude 2.1](https://noometry.com/models/claude-2-1) | #345 | 25.2 | — | — | [Compare](https://noometry.com/compare/claude-2-1-vs-gpt-4o-mini) |
| [Claude 2](https://noometry.com/models/claude-2) | #346 | 25.0 | — | — | [Compare](https://noometry.com/compare/claude-2-vs-gpt-4o-mini) |
| [DeepSeek LLM 67B](https://noometry.com/models/deepseek-llm-67b) | #347 | 24.9 | — | — | [Compare](https://noometry.com/compare/deepseek-llm-67b-vs-gpt-4o-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-4o mini Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 3.6% | #44 of 44, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 11.8% | #111 of 119, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 46.1% | #13 of 64, top 21% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-18 |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 43.1% | #22 of 39, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1290 | #205 of 294, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 57.4% | #14 of 66, top 22% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-07-18 |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 83.5% | #8 of 45, top 18% | july 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 72.2% | #12 of 38, top 32% | july 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Agentic & Tool Use

GPT-4o mini Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [BALROG](https://noometry.com/benchmarks/balrog) | 17.4% | #27 of 35, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-4o mini Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% | #78 of 83, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 10.7% | #77 of 77, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 28.8% | #94 of 99, top 95% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 0% | #116 of 129, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-15 |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 32.8% | #29 of 39, top 75% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1267 | #208 of 297, top 71% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 12% | #57 of 74, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 54.4% | #124 of 151, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 50% | #23 of 39, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 10.4% | #110 of 125, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 126.56 | #153 of 213, top 72% |  | [Epoch AI](https://epoch.ai/eci) | 2024-07-18 |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 41.3% | #30 of 39, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PIQA](https://noometry.com/benchmarks/piqa) | 88.7% | Best of 27 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-4o mini Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 0.7% | #79 of 81, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 6.9% | #141 of 173, top 82% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-07-30 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 28% | #42 of 57, top 74% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 36.3% | #30 of 39, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1267 | #205 of 285, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 52.6% | #44 of 79, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GSM8K](https://noometry.com/benchmarks/gsm8k) | 91.3% | #4 of 38, top 11% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Knowledge

GPT-4o mini Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 37.7% | #154 of 186, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 8.3% | #76 of 77, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-31 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 60.3% | #43 of 58, top 75% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 37.2% | #50 of 51, top 99% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 36.8% | #47 of 57, top 83% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1235 | #206 of 273, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BoolQ](https://noometry.com/benchmarks/boolq) | 88.7% | #2 of 23, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 81.8% | #13 of 81, top 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multimodal

GPT-4o mini Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1066 | #112 of 122, top 92% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Video-MME](https://noometry.com/benchmarks/video-mme) | 64.8% | #9 of 15, top 60% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 64% | #14 of 25, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 34% | #21 of 24, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

GPT-4o mini Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1266 | #199 of 297, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1265 | #201 of 285, top 71% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1297 | #163 of 223, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1272 | #164 of 231, top 71% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1216 | #150 of 211, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1195 | #163 of 213, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1275 | #193 of 283, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1276 | #171 of 226, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-4o mini Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 56.8% | #32 of 39, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 78.2% | #46 of 57, top 81% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1258 | #204 of 298, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-4o mini Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1289 | #197 of 291, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-4o mini Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1286 | #203 of 297, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1268 | #193 of 295, top 66% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 67.2% | #33 of 39, top 85% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 873 | #100 of 115, top 87% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 79.1% | #35 of 57, top 62% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1285 | #197 of 295, top 67% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 28.6% | #28 of 39, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

GPT-4o 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) | $0.15 | $0.60 | $0.075 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.15 | $0.60 | $0.075 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-4o-mini) | $0.15 | $0.60 | $0.075 | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GPT-4o mini

-   [GPT-4o mini vs Dolly 2.0-12b](https://noometry.com/compare/dolly-2-0-12b-vs-gpt-4o-mini)
-   [GPT-4o mini vs Llama 3-8B](https://noometry.com/compare/gpt-4o-mini-vs-llama-3-8b)
-   [GPT-4o mini vs Gemma 2 9B](https://noometry.com/compare/gemma-2-9b-vs-gpt-4o-mini)
-   [GPT-4o mini vs Claude 2.1](https://noometry.com/compare/claude-2-1-vs-gpt-4o-mini)
-   [GPT-4o mini vs Claude 3 Haiku](https://noometry.com/compare/claude-3-haiku-vs-gpt-4o-mini)
-   [GPT-4o mini vs Claude 2](https://noometry.com/compare/claude-2-vs-gpt-4o-mini)
-   [GPT-4o mini vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-4o-mini)
-   [GPT-4o mini vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-4o-mini)
-   [GPT-4o mini vs Kimi K3](https://noometry.com/compare/gpt-4o-mini-vs-kimi-k3)
-   [GPT-4o mini vs Grok 4.6](https://noometry.com/compare/gpt-4o-mini-vs-grok-4-6)
-   [GPT-4o mini vs Qwen3.8 Max](https://noometry.com/compare/gpt-4o-mini-vs-qwen3-8-max)
-   [GPT-4o mini vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-4o-mini)
-   [GPT-4o mini vs Muse Spark 1.3](https://noometry.com/compare/gpt-4o-mini-vs-muse-spark-1-3)
-   [GPT-4o mini vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-4o-mini)

## 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-4o mini?

GPT-4o mini by OpenAI ranks 343rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 25.5. Its strongest category is agentic & tool use, where it ranks 101st. API pricing starts at $0.15 per million input tokens and $0.60 per million output tokens, with a 128K-token context window.

### How much does GPT-4o mini cost?

GPT-4o mini costs $0.15 per million input tokens and $0.60 per million output tokens on OpenAI's own API, with cached input at $0.075.

### What is GPT-4o mini's context window?

GPT-4o mini accepts up to 128K tokens of input and can write up to 16K tokens in one response.

### Is GPT-4o mini open source?

No. GPT-4o mini is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-4o mini?

GPT-4o mini generated about 120 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-4o mini's strengths and weaknesses?

Relative to other ranked models, GPT-4o mini places best in long context, agentic & tool use, multilingual and lowest in reasoning, coding, math.

### What is GPT-4o mini best at?

Its best category is agentic & tool use, where it ranks 101st on Noometry.

### Cite this page

Noometry. (2026). GPT-4o mini benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-4o-mini

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-4o-mini.md).