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Gemini 3.1 Pro (Preview)

Added Feb 19, 2026

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Use in the app

Gemini 3.1 Pro preview is built for tasks where simple answers are not enough. Stronger core reasoning for complex coding, math, and long-context workflows, with multimodal support and a reported 77.1% verified score on ARC-AGI-2. NOTE: Inputs > 200k tokens are charged at 2x input and 1.5x output rates.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Audio input
  • Video input
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
low · medium · high
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$2.00
Output
$12.00
Cache read
$0.20
Cache write
$0.38

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
699 tokens
Longer than 62% of 465 models

Side by side

Gemini 3.1 Pro (Preview) compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Gemini 3.1 Pro (Preview)GoogleThis modelClaude Sonnet 4.6 ThinkingAnthropicRemove
ProviderGoogleAnthropic
Discovery tierFrontierFrontier
AddedFeb 19, 2026Feb 17, 2026
Limits
Context window1.05MBest1M
Max output65.54K128KBest
Average output699 tokens598 tokens
Pricing, USD per 1M tokens
Input$2.00Best$3.00
Output$12.00Best$15.00
Cache read$0.20Best$0.30
Cache write$0.38—
Performance
Throughput68.0 tok/s208.2 tok/sBest
Time to first token4.7 s4.3 sBest
Request success (24h)99.8%100.0%Best
Benchmark indexes
Intelligence Index29.730.1Best
Coding Index68.8Best63.0
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputNoYes
Audio inputYesNo
Video inputYesNo
Reasoning effortlow, medium, highlow, medium, high, xhigh, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Gemini 3.1 Pro (Preview)
  • Claude Sonnet 4.6 Thinking

Intelligence Index

  • Gemini 3.1 Pro (Preview)29.7
  • Claude Sonnet 4.6 Thinking30.1

Gemini 3.1 Pro (Preview): Better than 71% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 3.1 Pro (Preview): 68.8Top
    • Claude Sonnet 4.6 Thinking: 63.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.1 Pro (Preview): 35.4%Top
    • Claude Sonnet 4.6 Thinking: 20.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.1 Pro (Preview): 9.4%Top
    • Claude Sonnet 4.6 Thinking: 3.7%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Gemini 3.1 Pro (Preview): 0.0%
    • Claude Sonnet 4.6 Thinking: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.1 Pro (Preview): 456 Elo
    • Claude Sonnet 4.6 Thinking: 1,062 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.1 Pro (Preview): 791 Elo
    • Claude Sonnet 4.6 Thinking: 1,233 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.1 Pro (Preview): 17.8%Top
    • Claude Sonnet 4.6 Thinking: 15.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.1 Pro (Preview): 82.0%Top
    • Claude Sonnet 4.6 Thinking: 80.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.1 Pro (Preview): 11.1%Top
    • Claude Sonnet 4.6 Thinking: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.1 Pro (Preview): 47.0%Top
    • Claude Sonnet 4.6 Thinking: 33.6%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.1 Pro (Preview): 94.1%Top
    • Claude Sonnet 4.6 Thinking: 87.5%

  • IFBench

    Following precise output instructions

    • Gemini 3.1 Pro (Preview): 77.1%Top
    • Claude Sonnet 4.6 Thinking: 56.6%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.1 Pro (Preview): 4.0%Top
    • Claude Sonnet 4.6 Thinking: 3.0%

  • SciCode

    Scientific computing in Python

    • Gemini 3.1 Pro (Preview): 58.7%Top
    • Claude Sonnet 4.6 Thinking: 50.1%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 3.1 Pro (Preview): 53.8%Top
    • Claude Sonnet 4.6 Thinking: 53.0%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 3.1 Pro (Preview): 95.6%Top
    • Claude Sonnet 4.6 Thinking: 75.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.1 Pro (Preview): 82.0%Top
    • Claude Sonnet 4.6 Thinking: 80.0%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
68.0 tok/s
Faster than 45% of 250 models
Time to first token
4.7 s
Quicker than 36% of 250 models
Prompt cache hits
28.0%
About $1.50 per 1M input tokens with caching
Request success (24h)
99.8%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 97.2%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-3.1-pro-preview",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="google/gemini-3.1-pro-preview",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.