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DeepSeek · Open weights

DeepSeek V4 Pro (Thinking)

Added Apr 24, 2026Open weights

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Use in the app

DeepSeek V4 Pro Thinking enables DeepSeek's chain-of-thought mode on the large-scale Mixture-of-Experts model with a 1M-token context window, built for advanced reasoning, coding, long-horizon agent workflows, knowledge, math, and software engineering tasks.

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

Pricing

USD per 1M tokens
Input
$0.70
Output
$1.40
Cache read
$0.07

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
384K
Average output (7 days)
1.6K tokens
Longer than 87% of 465 models

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TryGPT 5.6 LunaMiMo V2.6 FlashGemini 3.6 FlashAll 3

Public evaluations

Benchmarks

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

Intelligence Index

36.0

Tested at max reasoning effort

Better than 79% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    68.8

    Composite of the coding evaluations

    Better than 66% of 168 scored models

Agentic work

  • AutomationBench-AA

    56.7%

    Workflow automation, penalized for guardrail violations

    Better than 71% of 173 scored models

  • AutomationBench-AA tasks completed

    25.9%

    Workflows finished end to end without a guardrail violation

    Better than 76% of 173 scored models

  • AA-Briefcasev1.1

    1,256 Elo

    Agentic knowledge work, rated head to head

    Better than 60% of 166 scored models

  • GDPval-AAv2.1

    1,449 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 68% of 187 scored models

Document reasoning

  • GDP.pdf

    11.4%

    Professional reasoning over PDF documents, all-pass rate

    Better than 53% of 171 scored models

  • AA-LCRv1.1

    80.3%

    Reasoning across long documents

    Better than 81% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    41.0%

    Expert-level questions across academic fields

    Better than 78% of 323 scored models

  • GPQA Diamond

    92.8%

    Graduate-level science questions

    Better than 89% of 291 scored models

Coding

  • Terminal-Benchv4.0

    14.1%

    Practical software engineering in a terminal

    Better than 67% of 171 scored models

  • SciCode

    51.0%

    Scientific computing in Python

    Better than 54% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    80.3%

    Long-context reasoning, earlier grading

    Better than 81% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. DeepSeek V4 Pro (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
54.9 tok/s
Faster than 39% of 250 models
Time to first token
2.7 s
Quicker than 63% of 250 models
Prompt cache hits
59.9%
About $0.32 per 1M input tokens with caching
Request success (24h)
99.9%
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 89.7%
  • 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-pro: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-pro:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.