Gemini 3.7 Flash, the Google DeepMind model in numbers

Gemini 3.7 Flash at a glance

Google DeepMind released Gemini 3.7 Flash on August 13, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. Its weights are not published, so it only runs remotely, through the Google DeepMind API or partner hosts (31 listed on models.dev). That makes it 2 months old as of October 22, 2026.

We file it under frontier models (24 models in this category on the site).

How much does Gemini 3.7 Flash cost?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.023, or $22.5 for 1,000 such tasks.

That is 3.9 times cheaper than the median output price of frontier models ($14.5).

Tokens read back from cache cost $0.075 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), Gemini 3.7 Flash has a capability index of 85.9 out of 100, computed on 5 benchmarks from our basket.

That puts it at #17 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 94.8% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 97.2% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 71.6% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 43.6% — the best tracked model, Claude Opus 5.5, reaches 54.6%

Context and capabilities

Its context window reaches 1,048,576 tokens, roughly 1,573 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 65,536 tokens.

It accepts as input: text, image, video, audio and PDF. Its knowledge stops at March 2026: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

Who is it for?

Given its category, Gemini 3.7 Flash is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its FrontierCode result (43.6%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to Gemini 3.7 Flash are:

• Grok 4.6 (xAI, index 86.1)
• GPT-5.6 Terra (OpenAI, index 85.7)
• Qwen3.7 Max (Alibaba Cloud (Qwen), index 86.6)
• Gemini 3 Pro Preview (Google DeepMind, index 87)