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Qwen3 Next 80B A3B (Thinking)

Added Jan 1, 2024Open weights

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Use in the app

Qwen3 Next 80B A3B (Thinking)

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.65

Reference prices as published by the model provider. API example

Specifications

Context window
256K
Max output
32.8K

Side by side

Qwen3 Next 80B A3B (Thinking) compared

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

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Qwen3 Next 80B A3B (Thinking)QwenThis modelGLM 4.5 AirZ.aiRemove
ProviderQwenZ.ai
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 15, 2025
Limits
Context window256KBest128K
Max output32.77K98.3KBest
Average output—59 tokens
Pricing, USD per 1M tokens
Input$0.15$0.13Best
Output$0.65Best$0.85
Cache read—$0.025
Performance
Throughput—90.9 tok/s
Time to first token—1.5 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index11.2Best11.1
Coding Index17.4—
Math Index84.3Best80.7
Capabilities
ToolsNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3 Next 80B A3B (Thinking)
  • GLM 4.5 Air

Intelligence Index

  • Qwen3 Next 80B A3B (Thinking)11.2
  • GLM 4.5 Air11.1

Qwen3 Next 80B A3B (Thinking): Better than 24% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3 Next 80B A3B (Thinking): 17.4
    • GLM 4.5 Air: —

  • Math Index

    Composite of the math evaluations

    • Qwen3 Next 80B A3B (Thinking): 84.3Top
    • GLM 4.5 Air: 80.7

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3 Next 80B A3B (Thinking): 128 Elo
    • GLM 4.5 Air: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 Next 80B A3B (Thinking): 63.7%Top
    • GLM 4.5 Air: 46.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 Next 80B A3B (Thinking): 12.6%Top
    • GLM 4.5 Air: 7.0%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 Next 80B A3B (Thinking): 75.9%Top
    • GLM 4.5 Air: 73.3%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 Next 80B A3B (Thinking): 82.4%Top
    • GLM 4.5 Air: 81.5%

  • IFBench

    Following precise output instructions

    • Qwen3 Next 80B A3B (Thinking): 60.7%Top
    • GLM 4.5 Air: 37.6%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 Next 80B A3B (Thinking): 78.4%Top
    • GLM 4.5 Air: 68.4%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 Next 80B A3B (Thinking): 84.3%Top
    • GLM 4.5 Air: 80.7%

  • AIME

    Competition mathematics

    • Qwen3 Next 80B A3B (Thinking): —
    • GLM 4.5 Air: 67.3%

  • MATH-500

    Competition math problems

    • Qwen3 Next 80B A3B (Thinking): —
    • GLM 4.5 Air: 96.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 Next 80B A3B (Thinking): 9.8%
    • GLM 4.5 Air: 20.5%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 Next 80B A3B (Thinking): 41.5%
    • GLM 4.5 Air: 46.5%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 Next 80B A3B (Thinking): 63.7%Top
    • GLM 4.5 Air: 46.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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-next-80b-a3b-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="qwen/qwen3-next-80b-a3b-thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.