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GGemini 2.5 Pro (batch)API

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks.

1MContext window · tokens
$0.63Input price · per M tokens
$5Output price · per M tokens
GoogleProvider

Prices update automatically — checked daily against provider list prices.

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for Gemini 2.5 Pro (batch), measured by Artificial Analysis. Higher is better.

Intelligence index16.1
Coding index33.3
Math index87.7
GPQA84.4%
MMLU-Pro86.2%
Humanity's Last Exam22.5%
Long Context Reasoning69%
LiveCodeBench80.1%
SciCode46.3%
MATH-50096.7%
AIME88.7%
AIME 202587.7%
IFBench48.7%
τ²-Bench54.1%
τ-Bench Banking9.7%
Terminal-Bench28.5%
Terminal-Bench Hard26.5%
💰 Blended price$3.438 / 1M tokens
📈 Value4.7 intelligence points per $
vs. models measured heretop 58%
Scores higher than 42% of the 292 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.96 per 1M tokens (median of 24) — this one costs $1.72.
Cheaper and better on this index: MiMo-V2.6-Pro · DeepSeek V4.1 Flash 1017 · GLM 5.3 and 82 more
Benchmark data by Artificial Analysis

About this model

Gemini 2.5 Pro (batch) is a commercial AI model by Google. 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 Gemini 2.5 Pro (batch)?

Gemini 2.5 Pro (batch) is an AI language model from Google. It is a proprietary model, available through an API. It scores 16.1 on the Artificial Analysis intelligence index.

Is Gemini 2.5 Pro (batch) free?

Gemini 2.5 Pro (batch) is not free: it costs $0.63 per million input tokens and $5 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is Gemini 2.5 Pro (batch) good at?

Independent benchmarks from Artificial Analysis give it GPQA 84.4%, MMLU-Pro 86.2%, Humanity's Last Exam 22.5%, Long Context Reasoning 69%, LiveCodeBench 80.1%, SciCode 46.3%, MATH-500 96.7%, AIME 88.7%, AIME 2025 87.7%, IFBench 48.7%, τ²-Bench 54.1%, τ-Bench Banking 9.7%, Terminal-Bench 28.5%, Terminal-Bench Hard 26.5%. It is particularly used for code generation.

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