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Oo4 MiniAPI

OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities.

200KContext window · tokens
$1.1Input price · per M tokens
$4.4Output price · per M tokens
OpenAIProvider

Prices update automatically — checked daily against provider list prices.

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for o4 Mini, measured by Artificial Analysis. Higher is better. Measurement mode: high.

Intelligence index26.1
Math index90.7
GPQA78.4%
MMLU-Pro83.2%
Humanity's Last Exam16.5%
Long Context Reasoning60%
LiveCodeBench85.9%
SciCode46.5%
MATH-50098.9%
AIME94%
AIME 202590.7%
IFBench68.7%
τ²-Bench55.6%
Terminal-Bench Hard15.2%
💰 Blended price$1.925 / 1M tokens
📈 Value13.6 intelligence points per $
vs. models measured heretop 53%
Scores higher than 47% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.56 per 1M tokens (median of 15) — this one costs $1.93.
Cheaper and better on this index: GLM 5.3 Flash · Gemini 3.7 Flash · Gemini 3.7 Flash (batch) and 70 more
Benchmark data by Artificial Analysis

About this model

o4 Mini is a commercial AI model by OpenAI. The specifications below are tracked automatically: pricing is refreshed daily from public list prices, so the numbers on this page reflect the current cost of using the model through its API.

Frequently asked questions

What is o4 Mini?

o4 Mini is an AI language model from OpenAI. It is a proprietary model, available through an API. It scores 26.1 on the Artificial Analysis intelligence index.

Is o4 Mini free?

o4 Mini is not free: it costs $1.1 per million input tokens and $4.4 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is o4 Mini good at?

Independent benchmarks from Artificial Analysis give it GPQA 78.4%, MMLU-Pro 83.2%, Humanity's Last Exam 16.5%, Long Context Reasoning 60%, LiveCodeBench 85.9%, SciCode 46.5%, MATH-500 98.9%, AIME 94%, AIME 2025 90.7%, IFBench 68.7%, τ²-Bench 55.6%, Terminal-Bench Hard 15.2%. It is particularly used for mathematical reasoning.

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