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Qwen3 Coder Next

qwen/qwen3-coder-next
Provider logo

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

Input Price (Auto)

$0.15/1M

Output Price (Auto)

$0.80/1M

Cache Read (Auto)

$0.075/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

21.3

Better than 57% of models compared

Coding Index

36.2

Better than 46% of models compared

Agentic Index

8.9

Better than 31% of models compared

Reasoning

GPQA Diamond

Graduate-level scientific reasoning

73.7%

Better than 57% of models compared

HLE

Humanity's Last Exam

10.1%

Better than 57% of models compared

IFBench

Instruction-following benchmark

35.2%

Better than 27% of models compared

T²-Bench Telecom

Conversational AI agents in dual-control scenarios

79.5%

Better than 69% of models compared

AA-LCR

Long context reasoning evaluation

42.3%

Better than 48% of models compared

GDPval-AA

Economically valuable tasks

10.8%

CritPt

Research-level physics reasoning

0.0%

Coding

SciCode

Python programming for scientific computing

32.3%

Better than 46% of models compared

Terminal-Bench Hard

Agentic coding and terminal use

18.2%

Better than 57% 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%

Last updated Aug 16, 2026

Artificial Analysis

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