GGemini 3.7 FlashAPI

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning.

1MContext window · tokens

$0.75Input price · per M tokens

$3.75Output price · per M tokens

GoogleProvider

Prices update automatically — checked daily against provider list prices.

$1.5 per 1M tokens, assuming 3 input for 1 output the convention used by Artificial Analysis
cheaper than 58% of the 227 commercial models tracked here
26 of 36 Google models tracked here, from cheapest

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for Gemini 3.7 Flash, measured by Artificial Analysis. Higher is better. Measurement mode: High.

Intelligence index39.1

Coding index76.1

GPQA94.5%

Humanity's Last Exam47.9%

Long Context Reasoning81.7%

SciCode57.2%

τ-Bench Banking32.8%

Terminal-Bench85.8%

💰 Blended price$1.5 / 1M tokens

📈 Value26.1 intelligence points per $

vs. models measured heretop 16%

Scores higher than 84% of the 284 models measured by Artificial Analysis and tracked here.
Models at this level cost $2 per 1M tokens (median of 22) — this one costs $1.5.
Cheaper and better on this index: MiMo-V2.6-Pro · GLM 5.3 · GLM 5.3 (batch) and 8 more

Benchmark data by Artificial Analysis

About this model

Gemini 3.7 Flash 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 3.7 Flash?

Gemini 3.7 Flash is an AI language model from Google. It is a proprietary model, available through an API. It scores 39.1 on the Artificial Analysis intelligence index.

Is Gemini 3.7 Flash free?

Gemini 3.7 Flash is not free: it costs $0.75 per million input tokens and $3.75 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is Gemini 3.7 Flash good at?

Independent benchmarks from Artificial Analysis give it GPQA 94.5%, Humanity's Last Exam 47.9%, Long Context Reasoning 81.7%, SciCode 57.2%, τ-Bench Banking 32.8%, Terminal-Bench 85.8%. It is particularly used for code generation.