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Qwen · Open weights

Qwen 3 32b

Added Jan 1, 2024Open weights

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Use in the app

Qwen 3 32b is a 32b model. Supports switching between thinking and non thinking: trigger thinking with /think and /no_think anywhere in a prompt or system message to toggle chain-of-thought reasoning.

  • Tools
  • PDF input
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.08
Output
$0.24

Reference prices as published by the model provider. API example

Specifications

Context window
41K
Max output
32.8K
Average output (7 days)
646 tokens
Longer than 58% of 468 models

Side by side

Qwen 3 32b compared

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

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Qwen 3 32bQwenThis modelQwen 3.8 27B KimikoQwenRemove
ProviderQwenQwen
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Oct 5, 2026
Limits
Context window41K262.14KBest
Max output32.77K32.77K
Average output646 tokens210 tokens
Pricing, USD per 1M tokens
Input$0.08Best$0.10
Output$0.24Best$0.60
Cache read—$0.05
Performance
Throughput26.6 tok/s—
Time to first token2.8 s—
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index7.3—
Math Index19.7—
Capabilities
ReasoningNoYes
ToolsYesYes
VisionNoYes
PDF inputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen 3 32b
  • Qwen 3.8 27B Kimiko

Intelligence Index

  • Qwen 3 32b7.3
  • Qwen 3.8 27B Kimiko—

Qwen 3 32b: Better than 10% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen 3 32b: 19.7
    • Qwen 3.8 27B Kimiko: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen 3 32b: 0.0%
    • Qwen 3.8 27B Kimiko: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen 3 32b: 4.1%
    • Qwen 3.8 27B Kimiko: —

  • GPQA Diamond

    Graduate-level science questions

    • Qwen 3 32b: 53.5%
    • Qwen 3.8 27B Kimiko: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen 3 32b: 72.7%
    • Qwen 3.8 27B Kimiko: —

  • IFBench

    Following precise output instructions

    • Qwen 3 32b: 31.5%
    • Qwen 3.8 27B Kimiko: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen 3 32b: 28.8%
    • Qwen 3.8 27B Kimiko: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen 3 32b: 19.7%
    • Qwen 3.8 27B Kimiko: —

  • AIME

    Competition mathematics

    • Qwen 3 32b: 30.3%
    • Qwen 3.8 27B Kimiko: —

  • MATH-500

    Competition math problems

    • Qwen 3 32b: 86.9%
    • Qwen 3.8 27B Kimiko: —

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen 3 32b: 0.0%
    • Qwen 3.8 27B Kimiko: —

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
26.6 tok/s
Faster than 17% of 263 models
Time to first token
2.8 s
Quicker than 62% of 263 models
Prompt cache hits
0.0%
Request success (24h)
100.0%
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 92.8%
  • 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": "qwen/qwen3-32b",
    "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="qwen/qwen3-32b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

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