Gemini 3.1 Pro

Name: Gemini 3.1 Pro
Brand: Google DeepMind
SKU: gemini-3-1-pro
Price: 2.4 USD
Availability: InStock

MultimodalNewAvailable

by Google DeepMindgemini-3-1-pro

Google DeepMind's February 2026 flagship. 1M-token (1,048,576) context, native multimodal (text/image/audio/video), Deep Think reasoning.

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Price · 1M in / out: US$2.40 / US$14.40
Context: 1,048,576 tokens
Max. output: 65,536 tokens
Input → output: Text + Image + Audio + Video → Text
Developer: Google DeepMind
Updated: September 24, 2026

Playground

Try Gemini 3.1 Pro

Chat

US$2.40/1M in

Send a message. The answer arrives in full once the model is done (no streaming).

Max. answer length (tokens)

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This run

at most US$0.0148 · 1.48 credits reserved

Billed by the tokens actually used; the unused part of the reservation is refunded.

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10 free credits (US$0.10) when you sign up with Google

Usable 24 hours after sign-up, up to 5 runs per day and at most 2 credits per run. Other sign-in methods start without credits. Enough for 6 runs of this model.

About Gemini 3.1 Pro

TL;DRAs of September 24, 2026

Gemini 3.1 Pro is a model by Google DeepMind in the Multimodal category. On Railwail, Gemini 3.1 Pro costs US$2.40 per 1M input tokens and US$14.40 per 1M output tokens. The context window holds 1,048,576 tokens, and one response can be up to 65,536 tokens long.

Released February 19, 2026, Gemini 3.1 Pro is Google DeepMind's flagship frontier model. Native 1,048,576-token context window, fully multimodal (text, image, audio, video tokens in a single pass), built-in Deep Think reasoning with adjustable thinking budgets. Top scores on GPQA Diamond, AIME, Humanity's Last Exam, and strong coding on SWE-bench. Function calling, JSON schema, Search grounding and Code Execution are first-class. Best for: long-document and long-video analysis, scientific reasoning, agentic workflows.

Background

About Google DeepMind

Founded 2010 · Mountain View, USA / London, UK

Google DeepMind is the merged AI research organisation formed in April 2023 by combining Google Brain (founded inside Google in 2011) with DeepMind (founded in London in 2010 by Demis Hassabis, Shane Legg and Mustafa Suleyman, acquired by Google in 2014). Demis Hassabis leads the unit as CEO. DeepMind authored seminal papers including 'Attention Is All You Need' (Google Brain, 2017), AlphaGo (2016), AlphaFold (2018-2021, awarded the 2024 Nobel Prize in Chemistry), AlphaZero, Chinchilla scaling laws and the Gemini Technical Report. The Gemini family launched in December 2023 (Ultra, Pro, Nano), followed by Gemini 1.5 Pro with 1M+ context (early 2024), Gemini 2.0 Flash (December 2024), Gemini 2.5 Pro with Deep Think (March/May 2025), Gemini 3 Pro (late 2025), Gemini 3.1 Pro (February 2026) and Gemini 3 Flash (April 2026). Google DeepMind also ships Imagen, Veo, Lyria and NotebookLM and powers AI features across Google Search, Workspace and Android.

Visit Google DeepMind

Architecture

Sparse Mixture-of-Experts Transformer (natively multimodal, Deep Think reasoning)

Gemini 3.1 Pro was released February 19, 2026 as Google DeepMind's flagship frontier model. It is a natively multimodal Sparse Mixture-of-Experts Transformer that ingests text, image, audio and video tokens through a shared embedding space, building directly on the Gemini 3 Pro architecture (late 2025) and Gemini 2.5 Pro (early 2025). The input context window is 1,048,576 tokens, the same size as Gemini 2.5 Pro. Pretraining used Google's TPU v6e infrastructure on a multi-trillion-token corpus mixing web text, code, books, scientific papers, image-text pairs, audio waveforms and video frames, with a knowledge cutoff in late 2025. Post-training combined supervised fine-tuning, RLHF, reinforcement learning against verifiable rewards on math and code, and a refreshed 'Deep Think' reasoning stage that teaches the model to allocate test-time thinking budgets and emit long internal chains-of-thought before its final answer. Tool use, function calling, structured output, Search grounding and the Code Execution tool are first-class. Safety training followed Google's Frontier Safety Framework v2.

Parameters: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
Context: 2,000,000 tokens

Capabilities

1,048,576-token input context window with native long-video and long-audio support
Built-in Deep Think reasoning mode with adjustable thinking budget
Natively multimodal: text, image, audio and video in a single pass
Top scores on GPQA Diamond, AIME 2026 and Humanity's Last Exam
Strong coding performance on SWE-bench Verified and LiveCodeBench
Function calling, JSON schema and parallel tool calls
Search grounding and Code Execution tools built into the API
Cross-lingual reasoning across 100+ languages
Available via Vertex AI, AI Studio and the Gemini app (paid-only since April 2026)
2x batch pricing reduction for large jobs
Best for: long-document and long-video analysis, scientific reasoning, agentic workflows, complex multimodal extraction.

Training & license

Pretrained on a multi-trillion-token mixture of web text, code, books, scientific papers, licensed third-party text, audio waveforms, image-text pairs and video frames. Knowledge cutoff in late 2025. Post-training uses supervised fine-tuning, RLHF, RL against verifiable rewards and Deep Think reasoning training.

License: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Paid-only since April 1, 2026.

Safety testing: Evaluated under Google DeepMind's Frontier Safety Framework v2 covering CBRN, cyber, persuasion and ML R&D risks, with internal red teams and external evaluators.

Known limitations

Deep Think adds significant latency and token cost
Prompts above 200K tokens are billed at 2x input / 1.5x output rates
Long-context recall quality can degrade beyond ~1M tokens for some tasks
Vision occasionally misreads dense tables and handwriting
Region availability varies; not yet generally available in all EU regions

Research papers

Pricing

Prices in US dollars. Usage is charged from prepaid credits.

Input	US$2.40 / 1M tokens
Output	US$14.40 / 1M tokens
Input (prompts over 200,000 tokens)	US$4.80 / 1M tokens
Output (prompts over 200,000 tokens)	US$21.60 / 1M tokens

Billed by the tokens each request actually uses.
1 credit = US$0.01

Cost calculator

Price calculator

Input tokens

/ req.

Output tokens

/ req.

Requests

Total

US$0.96

96 credits

Per request

US$0.0096 · 0.96 credits

Each request is rounded up to 0.01 credits.

API

Call Gemini 3.1 Pro with your Railwail API key. Use this model ID in the request:

gemini-3-1-proAPI documentation Get an API key

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3-1-pro",
    "messages": [
      {
        "role": "user",
        "content": "Explain what a vector database is in two sentences."
      }
    ],
    "max_tokens": 1024
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/api/v1",
)

completion = client.chat.completions.create(
    model="gemini-3-1-pro",
    messages=[
        {
            "role": "user",
            "content": "Explain what a vector database is in two sentences.",
        },
    ],
    max_tokens=1024,
)

print(completion.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/api/v1",
});

const completion = await client.chat.completions.create({
  model: "gemini-3-1-pro",
  messages: [
    {
      role: "user",
      content: "Explain what a vector database is in two sentences."
    }
  ],
  max_tokens: 1024
});

console.log(completion.choices[0].message.content);

// npm install railwail
import railwail from "railwail";

const rw = railwail(process.env.RAILWAIL_API_KEY);

const res = await rw.chat("gemini-3-1-pro", [
  { role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });

console.log(res.choices[0].message.content);

Set your key as RAILWAIL_API_KEYCreate API key

Specifications

Model ID: gemini-3-1-pro
Developer: Google DeepMind
Category: Multimodal
Input: Text, Image, Audio, Video
Output: Text
Context window: 1,048,576 tokens
Max. output: 65,536 tokens
Billing: By usage (tokens or GPU time)
Model size: Undisclosed (sparse MoE, total parameters in the hundreds of billions, active per-token undisclosed)
License: Proprietary commercial license via Google AI Studio, Vertex AI and the Gemini app. Paid-only since April 1, 2026.
Catalog entry updated: September 24, 2026

Input parameters

Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.

ParameterTypeDefaultAllowed values

promptrequired
User message
Type: Text
Default: –
Allowed values: up to 32,000 characters
top_p
Type: Number
Default: 0.95
Allowed values: 0 to 1
stream
Type: Yes/no
Default: false
Allowed values: –
image_url
Optional image URL to analyze
Type: Text
Default: –
Allowed values: –
max_tokens
Type: Integer
Default: 4096
Allowed values: 1 to 32,000
temperature
Type: Number
Default: 1
Allowed values: 0 to 2
system_prompt
Optional system instruction
Type: Text
Default: –
Allowed values: up to 8,000 characters

Use cases

What it is used for

Long-video and long-PDF analysis
Scientific research and literature review
Coding agents with Deep Think reasoning
Search-grounded enterprise chatbots
Multimodal data extraction at scale
Audio transcription and analysis with reasoning
Whole-codebase refactoring and migration

All models in Multimodal

Frequently asked questions

What is Gemini 3.1 Pro?

Gemini 3.1 Pro is a model by Google DeepMind in the Multimodal category. On Railwail you can call it with an API key through the Railwail API.

How much does Gemini 3.1 Pro cost on Railwail?

On Railwail, Gemini 3.1 Pro costs US$2.40 per 1M input tokens and US$14.40 per 1M output tokens. You are charged for what each request actually uses. Usage is paid from prepaid credits; 1 credit equals US$0.01.

What is the context window of Gemini 3.1 Pro?

The context window of Gemini 3.1 Pro holds 1,048,576 tokens. One response can be up to 65,536 tokens long.

How fast is Gemini 3.1 Pro?

There are not enough measured runs of Gemini 3.1 Pro on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is Gemini 3.1 Pro better than BLIP?

That depends on the task. Gemini 3.1 Pro (Google DeepMind) and BLIP (Salesforce) are both models in the Multimodal category. The comparison page shows their prices and specifications side by side.

Compare Gemini 3.1 Pro and BLIP

Can Gemini 3.1 Pro process images?

Yes. Gemini 3.1 Pro accepts images as input in addition to text.

How do I use Gemini 3.1 Pro through the API?

Create a Railwail API key and send your request with the model ID gemini-3-1-pro. Code examples for curl, Python and JavaScript are in the API section of this page.

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Compare Gemini 3.1 Pro vs. Claude Opus 4.7
Claude Sonnet 4.6Anthropic
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Compare Gemini 3.1 Pro vs. Claude Sonnet 4.6
Gemini 3 FlashGoogle DeepMind
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Compare Gemini 3.1 Pro vs. Gemini 3 Flash

Use Gemini 3.1 Pro via the API

One API key for every model on Railwail. Usage is charged from prepaid credits, 1 credit = US$0.01.

Get API key Browse all models