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Qwen · Open weights

Qwen3 Coder Next

Added Dec 8, 2025Open weights

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Use in the app

Qwen3 Coder Next is an open-weight coding model built on Qwen3-Next-80B-A3B-Base (hybrid attention + MoE). It is agentically trained at scale on executable tasks and environment interaction, delivering strong coding and tool-use performance at lower inference cost. Native 256K context.

  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.25
Output
$0.80
Cache read
$0.0798

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
182 tokens
Longer than 20% of 465 models

Side by side

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TryMistral Medium 3.1Gemini 2.5 Flash Lite Preview (09/2025)Llama 4 MaverickAll 3

Public evaluations

Benchmarks

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

Intelligence Index

9.2

Better than 17% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    36.2

    Composite of the coding evaluations

    Better than 29% of 168 scored models

Agentic work

  • AutomationBench-AA

    1.1%

    Workflow automation, penalized for guardrail violations

    Better than 10% of 173 scored models

  • AutomationBench-AA tasks completed

    0.0%

    Workflows finished end to end without a guardrail violation

    Better than 0% of 173 scored models

  • AA-Briefcasev1.1

    404 Elo

    Agentic knowledge work, rated head to head

    Better than 14% of 166 scored models

  • GDPval-AAv2.1

    513 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 13% of 187 scored models

Document reasoning

  • GDP.pdf

    2.6%

    Professional reasoning over PDF documents, all-pass rate

    Better than 10% of 171 scored models

  • AA-LCRv1.1

    47.0%

    Reasoning across long documents

    Better than 26% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    10.1%

    Expert-level questions across academic fields

    Better than 32% of 323 scored models

  • GPQA Diamond

    73.7%

    Graduate-level science questions

    Better than 32% of 291 scored models

  • IFBench

    35.2%

    Following precise output instructions

    Better than 15% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    36.2%

    Scientific computing in Python

    Better than 13% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    18.2%

    Earlier terminal task suite

    Better than 36% of 212 scored models

  • τ²-Bench Telecom

    79.5%

    Tool use in a customer-support simulation

    Better than 60% of 214 scored models

  • AA-LCR (unversioned)

    47.0%

    Long-context reasoning, earlier grading

    Better than 26% 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
123.1 tok/s
Faster than 71% of 251 models
Time to first token
1.7 s
Quicker than 83% of 251 models
Prompt cache hits
96.8%
About $0.0853 per 1M input tokens with caching
Request success (24h)
92.0%
Unstable

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 75.0%
  • 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-coder-next",
    "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-coder-next",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.