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Qwen3 30B A3B

Added Feb 27, 2025Open weights

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Use in the app

Qwen 3 30b A3B is a 30b model with 3 billion active parameters per pass. 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.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.30

Reference prices as published by the model provider. API example

Specifications

Context window
41K
Max output
32.8K
Average output (7 days)
228 tokens
Longer than 24% of 465 models

Side by side

Qwen3 30B A3B compared

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

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Qwen3 30B A3BQwenThis modelMistral SabaMistralRemove
ProviderQwenMistral
Discovery tierOpen weightsOpen weights
AddedFeb 27, 2025Jan 1, 2024
Limits
Context window41KBest32.77K
Max output32.77KBest26.21K
Average output228 tokens63 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.20
Output$0.30Best$0.60
Performance
Request success (24h)99.2%100.0%Best
Benchmark indexes
Intelligence Index6.6Best6.5
Math Index21.7—
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3 30B A3B
  • Mistral Saba

Intelligence Index

  • Qwen3 30B A3B6.6
  • Mistral Saba6.5

Qwen3 30B A3B: Better than 5% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen3 30B A3B: 21.7
    • Mistral Saba: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 30B A3B: 0.0%
    • Mistral Saba: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 30B A3B: 4.6%Top
    • Mistral Saba: 4.3%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 30B A3B: 51.5%Top
    • Mistral Saba: 42.4%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 30B A3B: 71.0%Top
    • Mistral Saba: 61.1%

  • IFBench

    Following precise output instructions

    • Qwen3 30B A3B: 31.9%
    • Mistral Saba: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 30B A3B: 32.2%
    • Mistral Saba: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 30B A3B: 21.7%
    • Mistral Saba: —

  • AIME

    Competition mathematics

    • Qwen3 30B A3B: 26.0%Top
    • Mistral Saba: 13.0%

  • MATH-500

    Competition math problems

    • Qwen3 30B A3B: 86.3%Top
    • Mistral Saba: 67.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 30B A3B: 6.8%
    • Mistral Saba: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 30B A3B: 22.2%
    • Mistral Saba: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 30B A3B: 0.0%
    • Mistral Saba: —

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.

Request success (24h)
99.2%
Reliable
Hourly request success · last 72 hoursLowest hour 88.8%
  • 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/qwen3-30b-a3b",
    "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/qwen3-30b-a3b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

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