Gemini 3.1 Pro Preview: pricing, context and performance

Gemini 3.1 Pro Preview at a glance

Gemini 3.1 Pro Preview is a language model from Google DeepMind, released on February 19, 2026. That makes it 8 months old as of October 22, 2026. Its weights are not published, so it only runs remotely, through the Google DeepMind API or partner hosts (38 listed on models.dev). It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under mid-range models (34 models in this category on the site).

What do independent benchmarks say?

According to results published by Epoch AI (checked on October 22, 2026), Gemini 3.1 Pro Preview has a capability index of 79.3 out of 100, computed on 6 benchmarks from our basket.

It is not among the top 30 of the weekly ranking.

• GPQA Diamond (PhD-level science questions): 94.4% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 95.6% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 59.6% — the best tracked model, GPT-6 Astra, reaches 93.7%
• SWE-bench Verified (fixing real software bugs): 75.6% — the best tracked model, Claude Opus 4.7, reaches 83.5%

Pricing and budget

Gemini 3.1 Pro Preview costs $2 per million input tokens and $12 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 $0.064, or $64 for 1,000 such tasks.

That is 2.7 times the median output price of mid-range models ($4.38).

Tokens read back from cache cost $0.20 per million, which helps an agent that keeps resending the same context.

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

What it can do

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 January 2025: 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.

What is it good for?

Given its category, Gemini 3.1 Pro Preview is mainly aimed at a balance between quality and cost, for everyday uses that do not justify a frontier model.

Its SWE-bench Verified result (75.6%, 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.

Alternatives

In the same category, the models closest to Gemini 3.1 Pro Preview are:

• Claude Sonnet 5 (Anthropic, index 79.3)
• Grok 4.5 (xAI, index 79.1)
• Mistral Large 4 (Mistral AI, index 79.7)
• Grok 4.7 (xAI, index 79.9)