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DeepSeek · Open weights

DeepSeek V3.2

Added Dec 1, 2025Open weights

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

  • 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)
408 tokens
Longer than 40% of 465 models

Side by side

DeepSeek V3.2 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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DeepSeek V3.2DeepSeekThis modelDeepSeek Chat 0324DeepSeekRemove
ProviderDeepSeekDeepSeek
Discovery tierOpen weightsOpen weights
AddedDec 1, 2025Jan 1, 2024
Limits
Context window163KBest128K
Max output65.54KBest8.19K
Average output408 tokens280 tokens
Pricing, USD per 1M tokens
Input$0.21$0.20Best
Output$0.31Best$0.77
Cache read$0.0216Best$0.14
Performance
Throughput46.8 tok/sBest26.5 tok/s
Time to first token3.2 s2.7 sBest
Request success (24h)99.6%99.9%Best
Benchmark indexes
Intelligence Index16.0Best9.7
Coding Index—21.2
Math Index59.0Best41.0
Capabilities
ToolsYesYes
Structured outputYesYes
PDF inputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • DeepSeek V3.2
  • DeepSeek Chat 0324

Intelligence Index

  • DeepSeek V3.216.0
  • DeepSeek Chat 03249.7

DeepSeek V3.2: Better than 40% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 21.2

  • Math Index

    Composite of the math evaluations

    • DeepSeek V3.2: 59.0Top
    • DeepSeek Chat 0324: 41.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 0.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 68 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 69 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 1.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek V3.2: 45.7%Top
    • DeepSeek Chat 0324: 40.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek V3.2: 11.2%Top
    • DeepSeek Chat 0324: 4.7%

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek V3.2: 75.1%Top
    • DeepSeek Chat 0324: 65.5%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • DeepSeek V3.2: 83.7%Top
    • DeepSeek Chat 0324: 81.9%

  • IFBench

    Following precise output instructions

    • DeepSeek V3.2: 49.0%Top
    • DeepSeek Chat 0324: 41.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 0.0%

  • SciCode

    Scientific computing in Python

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 39.0%

  • LiveCodeBench

    Recent competitive programming problems

    • DeepSeek V3.2: 59.3%Top
    • DeepSeek Chat 0324: 40.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • DeepSeek V3.2: 59.0%Top
    • DeepSeek Chat 0324: 41.0%

  • AIME

    Competition mathematics

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 52.0%

  • MATH-500

    Competition math problems

    • DeepSeek V3.2: —
    • DeepSeek Chat 0324: 94.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek V3.2: 32.6%Top
    • DeepSeek Chat 0324: 15.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek V3.2: 78.9%Top
    • DeepSeek Chat 0324: 47.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek V3.2: 45.7%Top
    • DeepSeek Chat 0324: 40.7%

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
46.8 tok/s
Faster than 34% of 250 models
Time to first token
3.2 s
Quicker than 53% of 250 models
Prompt cache hits
59.2%
About $0.098 per 1M input tokens with caching
Request success (24h)
99.6%
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 95.6%
  • 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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

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