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GGemini 3.1 Flash LiteAPI

Gemini 3.1 Flash Lite is Google’s GA high-efficiency multimodal model optimized for low-latency, high-volume workloads.

1MContext window · tokens
$0.25Input price · per M tokens
$1.5Output 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 3.1 Flash Lite, measured by Artificial Analysis. Higher is better.

Intelligence index25.6
Coding index34.7
GPQA82.2%
Humanity's Last Exam17.2%
Long Context Reasoning71.3%
SciCode41.9%
IFBench77.2%
τ²-Bench31.3%
τ-Bench Banking9.7%
Terminal-Bench31.1%
Terminal-Bench Hard24.2%
💰 Blended price$0.563 / 1M tokens
📈 Value45.5 intelligence points per $
vs. models measured heretop 56%
Scores higher than 44% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.96 per 1M tokens (median of 19) — this one costs $0.56.
Cheaper and better on this index: GLM 5.3 Flash · Gemini 3.7 Flash (batch) · GPT-5.6 Luna and 33 more
Benchmark data by Artificial Analysis

About this model

Gemini 3.1 Flash Lite 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 3.1 Flash Lite?

Gemini 3.1 Flash Lite is an AI language model from Google. It is a proprietary model, available through an API. It scores 25.6 on the Artificial Analysis intelligence index.

Is Gemini 3.1 Flash Lite free?

Gemini 3.1 Flash Lite is not free: it costs $0.25 per million input tokens and $1.5 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is Gemini 3.1 Flash Lite good at?

Independent benchmarks from Artificial Analysis give it GPQA 82.2%, Humanity's Last Exam 17.2%, Long Context Reasoning 71.3%, SciCode 41.9%, IFBench 77.2%, τ²-Bench 31.3%, τ-Bench Banking 9.7%, Terminal-Bench 31.1%, Terminal-Bench Hard 24.2%. It is particularly used for code generation.

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