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Qwen3 Coder Next

qwen/qwen3-coder-next
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Qwen3 Coder Next

qwen/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.

Added Dec 8, 2025

Model weights

Context Window

262.1K

Max Output

65.5K

Avg output tokens (7d)

408 tokens

34%

Input Price (Auto)

$0.12/1M

Output Price (Auto)

$0.80/1M

Cache Read (Auto)

$0.070/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

10.1

Better than 43% of models compared

Coding Index

36.2

Better than 42% of models compared

Agentic Index

3.6

Better than 28% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.1%

Better than 18% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

414 Elo

Better than 22% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

664 Elo

Better than 31% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.6%

Better than 21% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

47.0%

Better than 45% of models compared

Reasoning

HLE

Humanity's Last Exam

10.1%

Better than 54% 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 17% of models compared

SciCode

Python programming for scientific computing

36.2%

Better than 17% 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 45% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

8.2%

Last updated Sep 13, 2026

Artificial Analysis

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