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DeepSeek · Open weights

DeepSeek V3.2 Thinking

Added Dec 1, 2025Open weights

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DeepSeek V3.2 (thinking/reasoner mode) — official successor to V3.2-Exp. Reasoning-first model built for agents with GPT-5 level performance. Balanced inference vs. output length. First DeepSeek model with thinking-in-tool-use capability. FP8.

  • Reasoning
  • Tools
  • Structured output
  • PDF input
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.21
Output
$0.31
Cache read
$0.0216

Reference prices as published by the model provider. API example

Specifications

Context window
163K
Max output
65.5K
Average output (7 days)
1.4K tokens
Longer than 85% of 465 models

Side by side

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TryQwen3.5 397B A17B TEEMiMo V2.5 ThinkingOpenAI o3-pro (2025-06-10)All 3

Public evaluations

Benchmarks

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

Intelligence Index

21.5

Better than 52% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    44.2

    Composite of the coding evaluations

    Better than 37% of 168 scored models

  • Math Index

    92.0

    Composite of the math evaluations

    Better than 92% of 118 scored models

Agentic work

  • GDPval-AAv2.1

    691 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 21% of 187 scored models

Document reasoning

  • AA-LCRv1.1

    73.3%

    Reasoning across long documents

    Better than 55% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    24.6%

    Expert-level questions across academic fields

    Better than 56% of 323 scored models

  • GPQA Diamond

    84.0%

    Graduate-level science questions

    Better than 56% of 291 scored models

  • MMLU-Pro

    86.2%

    Multi-subject knowledge and reasoning

    Better than 92% of 132 scored models

  • IFBench

    60.7%

    Following precise output instructions

    Better than 59% of 215 scored models

Coding

  • LiveCodeBench

    86.2%

    Recent competitive programming problems

    Better than 96% of 129 scored models

Math

  • AIME 2025

    92.0%

    Competition mathematics, 2025 problems

    Better than 92% of 118 scored models

Legacy evaluations

  • Terminal-Bench Hard

    35.6%

    Earlier terminal task suite

    Better than 68% of 212 scored models

  • τ²-Bench Telecom

    90.6%

    Tool use in a customer-support simulation

    Better than 76% of 214 scored models

  • AA-LCR (unversioned)

    73.3%

    Long-context reasoning, earlier grading

    Better than 55% 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
17.0 tok/s
Faster than 8% of 250 models
Time to first token
4.3 s
Quicker than 41% of 250 models
Prompt cache hits
69.4%
About $0.079 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 98.8%
  • 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-v3.2: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="deepseek/deepseek-v3.2:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.