DeepSeek · Open weights
DeepSeek V4.1 Flash Uncensored Thinking
A lower-refusal finetune of DeepSeek V4.1 Flash with text input, optional reasoning, and tool calling. Supports a context window of up to 1M tokens.
- Reasoning
- Tools
- Input
- Text
- Output
- Text
- Reasoning effort
- none · low · medium · high
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.33
- Output
- $1.32
- Cache read
- $0.0083
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Average output (7 days)
- 797 tokens
- Longer than 65% of 465 models
Side by side
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Public evaluations
Benchmarks
There are no public benchmark results for DeepSeek V4.1 Flash Uncensored Thinking yet.
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
- 437.7 tok/s
- Faster than 97% of 250 models
- Time to first token
- 4.8 s
- Quicker than 38% of 250 models
- Prompt cache hits
- 96.4%
- About $0.02 per 1M input tokens with caching
- Request success (24h)
- 92.6%
- 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": "deepseek/deepseek-v4.1-flash-uncensored:thinking",
"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="deepseek/deepseek-v4.1-flash-uncensored:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)DeepSeek
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