Google · Open weights
Gemma 4 31B Thinking TEE
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
Gemma 4 31B Thinking TEE compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemma 4 31B Thinking TEEGoogleThis model | DeepSeek V3.1 Terminus (Thinking)DeepSeekRemove | |
|---|---|---|
| Provider | DeepSeek | |
| Discovery tier | Open weights | Open weights |
| Added | May 2, 2026 | Jan 1, 2024 |
| Limits | ||
| Context window | 262.14KBest | 128K |
| Max output | 131.07KBest | 65.54K |
| Average output | 1.1K tokens | 362 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.40 | $0.25Best |
| Output | $1.00 | $0.70Best |
| Cache read | $0.40 | — |
| Performance | ||
| Throughput | — | 21.6 tok/s |
| Time to first token | — | 6.4 s |
| Request success (24h) | 80.3% | 99.5%Best |
| Benchmark indexes | ||
| Intelligence Index | 14.7 | 14.8Best |
| Coding Index | 43.4 | 43.5Best |
| Math Index | — | 89.7 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | Yes | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemma 4 31B Thinking TEE
- DeepSeek V3.1 Terminus (Thinking)
Intelligence Index
- Gemma 4 31B Thinking TEE14.7
- DeepSeek V3.1 Terminus (Thinking)14.8
Gemma 4 31B Thinking TEE: Better than 35% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemma 4 31B Thinking TEE: 43.4
- DeepSeek V3.1 Terminus (Thinking): 43.5Top
Math Index
Composite of the math evaluations
- Gemma 4 31B Thinking TEE: —
- DeepSeek V3.1 Terminus (Thinking): 89.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemma 4 31B Thinking TEE: 4.9%Top
- DeepSeek V3.1 Terminus (Thinking): 2.7%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemma 4 31B Thinking TEE: 0.5%Top
- DeepSeek V3.1 Terminus (Thinking): 0.2%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemma 4 31B Thinking TEE: 368 Elo
- DeepSeek V3.1 Terminus (Thinking): 458 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemma 4 31B Thinking TEE: 619 Elo
- DeepSeek V3.1 Terminus (Thinking): 709 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemma 4 31B Thinking TEE: 6.0%Top
- DeepSeek V3.1 Terminus (Thinking): 5.4%
AA-LCRv1.1
Reasoning across long documents
- Gemma 4 31B Thinking TEE: 69.7%Top
- DeepSeek V3.1 Terminus (Thinking): 69.3%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemma 4 31B Thinking TEE: 23.6%Top
- DeepSeek V3.1 Terminus (Thinking): 16.4%
GPQA Diamond
Graduate-level science questions
- Gemma 4 31B Thinking TEE: 85.7%Top
- DeepSeek V3.1 Terminus (Thinking): 79.2%
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemma 4 31B Thinking TEE: —
- DeepSeek V3.1 Terminus (Thinking): 85.1%
IFBench
Following precise output instructions
- Gemma 4 31B Thinking TEE: 75.6%Top
- DeepSeek V3.1 Terminus (Thinking): 57.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemma 4 31B Thinking TEE: 0.0%Top
- DeepSeek V3.1 Terminus (Thinking): 0.0%Top
SciCode
Scientific computing in Python
- Gemma 4 31B Thinking TEE: 45.5%Top
- DeepSeek V3.1 Terminus (Thinking): 38.0%
LiveCodeBench
Recent competitive programming problems
- Gemma 4 31B Thinking TEE: —
- DeepSeek V3.1 Terminus (Thinking): 79.8%
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemma 4 31B Thinking TEE: —
- DeepSeek V3.1 Terminus (Thinking): 89.7%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemma 4 31B Thinking TEE: 36.4%Top
- DeepSeek V3.1 Terminus (Thinking): 30.3%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemma 4 31B Thinking TEE: 59.9%Top
- DeepSeek V3.1 Terminus (Thinking): 37.1%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemma 4 31B Thinking TEE: 69.7%Top
- DeepSeek V3.1 Terminus (Thinking): 69.3%
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.
- 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.
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)Related models
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