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 weightsPricing
Auto routing · per 1M tokens- Input
- $0.12
- Output
- $0.80
- Cache read
- $0.070
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
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
9.2
Coding Index
36.2
Agentic Index
0.9
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
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