What is Gemini 3.1 Pro Preview built for?

Google describes it as refining the Gemini 3 Pro series with better thinking, improved token efficiency and a more grounded, factually consistent experience, optimised for software engineering and agentic workflows.

Should I run a preview model in production?

Only with a pinned prompt version and a tested fallback. odnoga lets you switch the production label back to the previous model without an application change.

Is the 1M context worth the cost?

Sometimes. Compare a long-context prompt against a retrieval-based one on the same frozen test cases — the laboratory reports tokens and cost next to pass rate, so the trade-off is measured rather than argued.

Gemini 3.1 Pro Preview

Gemini 3.1 Pro Preview by Google AI: $2 input and $12 output per 1M tokens, 1.0M context. Call it through the odnoga LLM gateway.

ReasoningVisionToolsJSON modeStreaming

Gemini 3.1 Pro Preview is served by Google AI and called through odnoga with the same OpenAI-compatible request shape as every other model in the catalog. It is a reasoning model, so it spends extra output tokens working through a problem before answering — budget for higher output cost on hard tasks. It accepts images alongside text. It supports tool and function calling. It can be forced to return structured JSON. Its 1.0M-token context window sets how much input you can send in one request. Cached input is billed at $0.2 per 1M tokens, so repeated prefixes cost less.

Specification and price

Vendor	Google AI
Model ID	gemini-3.1-pro-preview
Context window	1.0M tokens
Max output	66K tokens
Capabilities	Reasoning, Vision, Tools, JSON mode, Streaming

Your plan

Per 1M tokens	Vendor cost	Your price on Free+7%
Input	$2	$2.15
Output	$12	$12.90
Cached input	$0.2	$0.215

Vendor cost is the list price per million tokens as recorded in the odnoga catalog; your price applies your plan margin with the same formula that bills every request — see pricing. Pricing

Call it through odnoga

const res = await fetch("https://api.odnoga.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ODNOGA_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gemini-3.1-pro-preview",
    messages: [{ role: "user", content: "Hello" }],
  }),
});

Same request shape for every vendor in the catalog — swap the model id and odnoga handles keys, routing, limits and cost accounting.

How to use Gemini 3.1 Pro Preview

Reviewed 2026-09-14

Best for

Complex problems across mixed sources: Google describes it as its most advanced reasoning Gemini model, handling text, audio, images, video, PDFs and whole code repositories.
Software engineering behaviour and agentic workflows that need precise tool use and reliable multi-step execution.
Analysis over very large inputs with a 1M-token context window.
Tasks where factual consistency and token efficiency matter more than raw speed.

Not the right pick when

High-volume, latency-sensitive traffic — a Flash tier is the intended answer.
Cost-sensitive batch work: Pro-tier pricing shows up quickly at scale.
Production paths that cannot tolerate a preview model id changing underneath them.

Practical tips through odnoga

01Use it for the hard tail of your traffic and a Flash tier for the rest — routing by prompt is the whole point of a gateway.
02Give it the sources rather than a summary of them; its advantage is comprehension across large, mixed inputs.
03Because it is a preview id, pin it in a managed prompt version and keep the previous model available for instant switch-back.
04Track cost per tenant: Pro-tier reasoning on long inputs is the most expensive combination in any catalog.

What a month costs

1,000 calls a month, 10,000 input tokens and 2,000 output tokens each, at your Free price (vendor cost +7%):

Input (10M tokens)	$21.51
Output (2M tokens)	$25.81
Your cost per month on Free	$47.31

Vendor list cost $44.00 + odnoga margin $3.31 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.