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Google · Frontier

Gemini 3.1 Pro (Preview Low)

Added Feb 21, 2026

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Use in the app

Gemini 3.1 Pro preview low-reasoning variant.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Audio input
Input
Text, Images, Audio
Output
Text
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)
1.6K tokens
Longer than 87% of 465 models

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TryGPT 6 LunaClaude Sonnet 4.6 ThinkingMiniMax M3All 3

Public evaluations

Benchmarks

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

Intelligence Index

29.7

Better than 71% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    68.8

    Composite of the coding evaluations

    Better than 66% of 168 scored models

Agentic work

  • AutomationBench-AA

    35.4%

    Workflow automation, penalized for guardrail violations

    Better than 52% of 173 scored models

  • AutomationBench-AA tasks completed

    9.4%

    Workflows finished end to end without a guardrail violation

    Better than 50% of 173 scored models

  • Harvey LAB-AAv1.1

    0.0%

    Legal tasks passed in full, with no material hallucination

    Better than 0% of 29 scored models

  • AA-Briefcasev1.1

    456 Elo

    Agentic knowledge work, rated head to head

    Better than 15% of 166 scored models

  • GDPval-AAv2.1

    791 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 25% of 187 scored models

Document reasoning

  • GDP.pdf

    17.8%

    Professional reasoning over PDF documents, all-pass rate

    Better than 70% of 171 scored models

  • AA-LCRv1.1

    82.0%

    Reasoning across long documents

    Better than 85% of 309 scored models

  • MLCR-AA

    11.1%

    Long-context reasoning over medical records

    Better than 38% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    47.0%

    Expert-level questions across academic fields

    Better than 90% of 323 scored models

  • GPQA Diamond

    94.1%

    Graduate-level science questions

    Better than 96% of 291 scored models

  • IFBench

    77.1%

    Following precise output instructions

    Better than 93% of 215 scored models

Coding

  • Terminal-Benchv4.0

    4.0%

    Practical software engineering in a terminal

    Better than 51% of 171 scored models

  • SciCode

    58.7%

    Scientific computing in Python

    Better than 85% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    53.8%

    Earlier terminal task suite

    Better than 93% of 212 scored models

  • τ²-Bench Telecom

    95.6%

    Tool use in a customer-support simulation

    Better than 87% of 214 scored models

  • AA-LCR (unversioned)

    82.0%

    Long-context reasoning, earlier grading

    Better than 85% of 309 scored models

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
101.1 tok/s
Faster than 59% of 250 models
Time to first token
4.9 s
Quicker than 34% of 250 models
Prompt cache hits
0.0%
About $2.00 per 1M input tokens with caching
Request success (24h)
95.0%
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 57.1%
  • 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-low",
    "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-low",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.