DeepSeek · Open weights
DeepSeek V4.1 Flash TEE
DeepSeek V4.1 Flash's October 17, 2026 checkpoint (1017) supports text and image input, reasoning, tool calling, and structured output with a 1M-token context window. This route runs through Tinfoil attested inference inside a Trusted Execution Environment. Tinfoil lists this model as experimental, with availability subject to change.
- Reasoning
- Tools
- Vision
- Structured output
- Parallel tools
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- none · low · medium · high · max
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.65
- Output
- $1.45
- Cache read
- $0.13
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 384K
- Average output (7 days)
- 571 tokens
- Longer than 53% of 465 models
Side by side
Compare models
Put DeepSeek V4.1 Flash TEE next to up to three other models. The comparison stays in the link, so you can share it.
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
24.7
Better than 60% of 326 scored models
Agentic work
AutomationBench-AA
47.8%Workflow automation, penalized for guardrail violations
Better than 61% of 173 scored models
AutomationBench-AA tasks completed
14.2%Workflows finished end to end without a guardrail violation
Better than 58% of 173 scored models
AA-Briefcasev1.1
1,118 EloAgentic knowledge work, rated head to head
Better than 54% of 166 scored models
GDPval-AAv2.1
1,340 EloEconomically valuable occupational tasks, rated head to head
Better than 58% of 187 scored models
Document reasoning
GDP.pdf
3.8%Professional reasoning over PDF documents, all-pass rate
Better than 14% of 171 scored models
AA-LCRv1.1
55.3%Reasoning across long documents
Better than 33% of 309 scored models
Reasoning and knowledge
Humanity’s Last Exam
10.8%Expert-level questions across academic fields
Better than 33% of 323 scored models
Coding
Terminal-Benchv4.0
5.6%Practical software engineering in a terminal
Better than 54% of 171 scored models
SciCode
35.5%Scientific computing in Python
Better than 11% of 174 scored models
Legacy evaluations
AA-LCR (unversioned)
55.3%Long-context reasoning, earlier grading
Better than 33% 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
- 240.6 tok/s
- Faster than 89% of 250 models
- Time to first token
- 3.2 s
- Quicker than 53% of 250 models
- Prompt cache hits
- 66.3%
- About $0.31 per 1M input tokens with caching
- Request success (24h)
- 99.5%
- 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.
- 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/deepseek-v4.1-flash",
"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="TEE/deepseek-v4.1-flash",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)DeepSeek
Related models
Other DeepSeek models, starting with the same family.