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DeepSeek · Open weights

DeepSeek V4.1 Flash 1017 Thinking

Added Oct 17, 2026Open weights

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Use in the app

The October 17, 2026 snapshot of DeepSeek V4.1 Flash supports text and image input, tool calling, and optional reasoning with low, high, and max effort. It has a shared 1,048,576-token context window and supports up to 393,216 output tokens within the remaining context. Released under the MIT license.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Reasoning effort
none · low · high · max
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.60
Cache read
$0.003

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
393.2K
Average output (7 days)
1.2K tokens
Longer than 78% of 465 models

Side by side

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TryGLM 5.3 TEEGrok 4.6MiMo V2.6 ProAll 3

Public evaluations

Benchmarks

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

Intelligence Index

45.1

Tested at max reasoning effort

Better than 91% of 326 scored models

3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    66.6%

    Workflow automation, penalized for guardrail violations

    Better than 95% of 173 scored models

  • AutomationBench-AA tasks completed

    36.7%

    Workflows finished end to end without a guardrail violation

    Better than 95% of 173 scored models

  • AA-Briefcasev1.1

    1,570 Elo

    Agentic knowledge work, rated head to head

    Better than 90% of 166 scored models

  • GDPval-AAv2.1

    1,746 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 96% of 187 scored models

Document reasoning

  • GDP.pdf

    17.2%

    Professional reasoning over PDF documents, all-pass rate

    Better than 67% of 171 scored models

  • AA-LCRv1.1

    82.7%

    Reasoning across long documents

    Better than 88% of 309 scored models

  • MLCR-AA

    26.7%

    Long-context reasoning over medical records

    Better than 88% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    43.3%

    Expert-level questions across academic fields

    Better than 86% of 323 scored models

Coding

  • Terminal-Benchv4.0

    33.8%

    Practical software engineering in a terminal

    Better than 82% of 171 scored models

  • SciCode

    55.9%

    Scientific computing in Python

    Better than 79% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    82.7%

    Long-context reasoning, earlier grading

    Better than 88% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. DeepSeek V4.1 Flash 1017 Thinking was tested at max reasoning effort; compare scores measured at the same effort.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
261.1 tok/s
Faster than 90% of 250 models
Time to first token
2.4 s
Quicker than 68% of 250 models
Prompt cache hits
91.2%
About $0.0115 per 1M input tokens with caching
Request success (24h)
99.8%
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 99.4%
  • 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": "deepseek/deepseek-v4.1-flash-1017:thinking",
    "reasoning_effort": "none",
    "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-1017:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.