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Google · Open weights

Gemma 4 31B Thinking TEE

Added May 2, 2026Open weights

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Use in the app

Google's Gemma 4 31B instruction-tuned model in a Trusted Execution Environment (TEE) with thinking explicitly enabled and provider attestation support.

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

Pricing

USD per 1M tokens
Input
$0.40
Output
$1.00
Cache read
$0.40

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
131.1K
Average output (7 days)
1.1K tokens
Longer than 75% of 468 models

Side by side

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TryDeepSeek V3.1 Terminus (Thinking)GLM 4.7 Flash ThinkingQwen3.6 35B A3B TEEAll 3

Public evaluations

Benchmarks

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

Intelligence Index

14.7

Better than 35% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    43.4

    Composite of the coding evaluations

    Better than 33% of 168 scored models

Agentic work

  • AutomationBench-AA

    4.9%

    Workflow automation, penalized for guardrail violations

    Better than 27% of 173 scored models

  • AutomationBench-AA tasks completed

    0.5%

    Workflows finished end to end without a guardrail violation

    Better than 24% of 173 scored models

  • AA-Briefcasev1.1

    368 Elo

    Agentic knowledge work, rated head to head

    Better than 11% of 166 scored models

  • GDPval-AAv2.1

    619 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 17% of 187 scored models

Document reasoning

  • GDP.pdf

    6.0%

    Professional reasoning over PDF documents, all-pass rate

    Better than 25% of 171 scored models

  • AA-LCRv1.1

    69.7%

    Reasoning across long documents

    Better than 49% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    23.6%

    Expert-level questions across academic fields

    Better than 52% of 323 scored models

  • GPQA Diamond

    85.7%

    Graduate-level science questions

    Better than 61% of 291 scored models

  • IFBench

    75.6%

    Following precise output instructions

    Better than 84% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    45.5%

    Scientific computing in Python

    Better than 32% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    36.4%

    Earlier terminal task suite

    Better than 70% of 212 scored models

  • τ²-Bench Telecom

    59.9%

    Tool use in a customer-support simulation

    Better than 45% of 214 scored models

  • AA-LCR (unversioned)

    69.7%

    Long-context reasoning, earlier grading

    Better than 49% 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.

Prompt cache hits
30.1%
About $0.40 per 1M input tokens with caching
Request success (24h)
80.3%
Unstable

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 33.3%
  • 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": "TEE/gemma4-31b:thinking",
    "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="TEE/gemma4-31b:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.