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GGemini 3.5 FlashAPI

Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed.

1MContext window · tokens
$1.5Input price · per M tokens
$9Output 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 3.5 Flash, measured by Artificial Analysis. Higher is better. Measurement mode: high.

Intelligence index52
Coding index70.1
GPQA92.2%
Humanity's Last Exam42.7%
Long Context Reasoning81%
SciCode53.1%
IFBench76.3%
τ²-Bench95.3%
τ-Bench Banking32.2%
Terminal-Bench78.7%
Terminal-Bench Hard40.9%
💰 Blended price$3.375 / 1M tokens
📈 Value15.4 intelligence points per $
vs. models measured heretop 15%
Scores higher than 85% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $1.5 per 1M tokens (median of 14) — this one costs $3.38.
Cheaper and better on this index: Grok 4.6 · GPT-5.6 Sol (batch) · GLM 5.3 and 16 more
Benchmark data by Artificial Analysis

About this model

Gemini 3.5 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 3.5 Flash?

Gemini 3.5 Flash is an AI language model from Google. It is a proprietary model, available through an API. It scores 52 on the Artificial Analysis intelligence index.

Is Gemini 3.5 Flash free?

Gemini 3.5 Flash is not free: it costs $1.5 per million input tokens and $9 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is Gemini 3.5 Flash good at?

Independent benchmarks from Artificial Analysis give it GPQA 92.2%, Humanity's Last Exam 42.7%, Long Context Reasoning 81%, SciCode 53.1%, IFBench 76.3%, τ²-Bench 95.3%, τ-Bench Banking 32.2%, Terminal-Bench 78.7%, Terminal-Bench Hard 40.9%. It is particularly used for code generation.

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