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OGPT-5.1-Codex-MiniAPI

GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex

400KContext window · tokens
$0.25Input price · per M tokens
$2Output 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 GPT-5.1-Codex-Mini, measured by Artificial Analysis. Higher is better. Measurement mode: high.

Intelligence index31.3
Math index91.7
GPQA81.3%
MMLU-Pro82%
Humanity's Last Exam18.5%
Long Context Reasoning65%
LiveCodeBench83.6%
SciCode42.6%
AIME 202591.7%
IFBench67.9%
τ²-Bench62.9%
Terminal-Bench Hard33.3%
💰 Blended price$0.688 / 1M tokens
📈 Value45.5 intelligence points per $
vs. models measured heretop 46%
Scores higher than 54% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $1 per 1M tokens (median of 19) — this one costs $0.69.
Cheaper and better on this index: GLM 5.3 Flash · Gemini 3.7 Flash (batch) · GPT-5.6 Luna and 28 more
Benchmark data by Artificial Analysis

About this model

GPT-5.1-Codex-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 GPT-5.1-Codex-Mini?

GPT-5.1-Codex-Mini is an AI language model from OpenAI. It is a proprietary model, available through an API. It scores 31.3 on the Artificial Analysis intelligence index.

Is GPT-5.1-Codex-Mini free?

GPT-5.1-Codex-Mini is not free: it costs $0.25 per million input tokens and $2 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is GPT-5.1-Codex-Mini good at?

Independent benchmarks from Artificial Analysis give it GPQA 81.3%, MMLU-Pro 82%, Humanity's Last Exam 18.5%, Long Context Reasoning 65%, LiveCodeBench 83.6%, SciCode 42.6%, AIME 2025 91.7%, IFBench 67.9%, τ²-Bench 62.9%, Terminal-Bench Hard 33.3%. It is particularly used for mathematical reasoning.

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