Qwen3 Coder Next

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.

  • Tool Calling
  • Structured Output

Added Dec 8, 2025

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.12
Output
$0.80
Cache read
$0.070
Compare provider prices

Specifications

Context window
262.1K
Max output
65.5K
Parameters
80B / 3B
Total / active
Avg output (7d)
329 tokens
Longer than 30% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

9.2

Better than 36% of models compared

Coding Index

36.2

Better than 42% of models compared

Agentic Index

0.9

Better than 11% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.1%

Better than 15% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

401 Elo

Better than 20% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

517 Elo

Better than 27% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.6%

Better than 15% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

47.0%

Better than 42% of models compared

Reasoning

HLE

Humanity's Last Exam

10.1%

Better than 50% of models compared

IFBench

Instruction-following benchmark

35.2%

Better than 27% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

36.2%

Better than 16% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

16.2%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

93.7%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

73.7%

Better than 54% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

18.2%

Better than 57% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

79.5%

Better than 69% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

47.0%

Better than 42% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.1%

Last updated Oct 3, 2026

Artificial Analysis

Providers

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