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Google · Open weights

Gemma 4 31B

Added Apr 2, 2026Open weights

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Use in the app

Google's Gemma 4 31B instruction-tuned model for heavier reasoning, coding, agentic workflows, and long-context multimodal understanding. This route keeps tokenizer thinking disabled for faster direct answers. Requests containing video cost $0.14 per million input tokens and $0.40 per million output tokens.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.14
Output
$0.40
Cache read
$0.05

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
131.1K
Average output (7 days)
338 tokens
Longer than 36% of 468 models

Side by side

Gemma 4 31B compared

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

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Gemma 4 31BGoogleThis modelGemma 4 26B A4B ThinkingGoogleRemove
ProviderGoogleGoogle
Discovery tierOpen weightsOpen weights
AddedApr 2, 2026Apr 2, 2026
Limits
Context window262.14K262.14K
Max output131.07K131.07K
Average output338 tokens1.1K tokens
Pricing, USD per 1M tokens
Input$0.14$0.09Best
Output$0.40$0.30Best
Cache read$0.05$0.05
Performance
Throughput39.3 tok/sBest31.8 tok/s
Time to first token3.2 sBest4.4 s
Request success (24h)99.7%100.0%Best
Benchmark indexes
Intelligence Index14.716.7Best
Coding Index43.4Best39.3
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
Video inputYesYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Gemma 4 31B
  • Gemma 4 26B A4B Thinking

Intelligence Index

  • Gemma 4 31B14.7
  • Gemma 4 26B A4B Thinking16.7

Gemma 4 31B: Better than 35% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemma 4 31B: 43.4Top
    • Gemma 4 26B A4B Thinking: 39.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemma 4 31B: 4.9%Top
    • Gemma 4 26B A4B Thinking: 1.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemma 4 31B: 0.5%Top
    • Gemma 4 26B A4B Thinking: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemma 4 31B: 368 EloTop
    • Gemma 4 26B A4B Thinking: 315 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemma 4 31B: 619 EloTop
    • Gemma 4 26B A4B Thinking: 565 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemma 4 31B: 6.0%
    • Gemma 4 26B A4B Thinking: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemma 4 31B: 69.7%Top
    • Gemma 4 26B A4B Thinking: 65.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemma 4 31B: 23.6%Top
    • Gemma 4 26B A4B Thinking: 19.3%

  • GPQA Diamond

    Graduate-level science questions

    • Gemma 4 31B: 85.7%Top
    • Gemma 4 26B A4B Thinking: 79.2%

  • IFBench

    Following precise output instructions

    • Gemma 4 31B: 75.6%Top
    • Gemma 4 26B A4B Thinking: 72.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemma 4 31B: 0.0%
    • Gemma 4 26B A4B Thinking: —

  • SciCode

    Scientific computing in Python

    • Gemma 4 31B: 45.5%Top
    • Gemma 4 26B A4B Thinking: 40.0%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemma 4 31B: 36.4%Top
    • Gemma 4 26B A4B Thinking: 13.6%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemma 4 31B: 59.9%Top
    • Gemma 4 26B A4B Thinking: 43.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemma 4 31B: 69.7%Top
    • Gemma 4 26B A4B Thinking: 65.7%

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
39.3 tok/s
Faster than 27% of 253 models
Time to first token
3.2 s
Quicker than 55% of 253 models
Prompt cache hits
15.1%
About $0.13 per 1M input tokens with caching
Request success (24h)
99.7%
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 96.7%
  • 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/gemma-4-31b-it",
    "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/gemma-4-31b-it",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.