Alibaba
Qwen3-Coder-Next
前沿
937.9K下载量1.4K点赞Jan 2026发布日期256K tokens上下文Apache 2.0许可证93 卓越质量
Qwen3-Coder-Next (80B parameters) requires approximately 52.1 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 3B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 60 GB of VRAM.
快速开始
— 复制粘贴即可本地运行Copy-paste commands to run Qwen3-Coder-Next on your machine.
Run
ollama run qwen3-coder-nextQuick specs
Parameters80B (3B active)
Architecturemoe (MoE)
Context256K tokens
Modalitytext
Min RAM31.2 GB
Rec. RAM48.8 GB (Q4_K_M)
LicenseApache 2.0
FamilyQwen Coder
✓ Code✓ Reasoning
About this model
- •Super Efficient with Significant Performance: With only 3B activated parameters (80B total parameters), it achieves performance comparable to...
- •Advanced Agentic Capabilities: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from...
- •Versatile Integration with Real-World IDE: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless...
相关模型
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最佳硬件
Qwen3-Coder-Next 的最佳选择
运行此模型
量化选项
各量化级别的 VRAM 估算
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 31.2 GB | Low | — |
Q3_K_S | 3 | 39.2 GB | Low | — |
NVFP4 | 4 | 44.8 GB | Medium | — |
Q4_K_M | 4 | 48.8 GB | Medium | — |
Q5_K_M | 5 | 57.6 GB | High | — |
Q6_K | 6 | 65.6 GB | High | — |
Q8_0 | 8 | 85.6 GB | Very High | — |
F16 | 16 | 164.0 GB | Maximum | — |
Quality benchmarks
Qwen3-Coder-Next benchmark scores
Coding
SWE-bench Verified70.6%
HumanEval+—
Aider Polyglot—
LiveCodeBench74.5%
Reasoning
MMLU-Pro78.4%
GPQA Diamond—
MATH-500—
ARC Challenge—
Source: official · 2026-01-30
硬件兼容性
全部硬件的适配估算
Computing compatibility...
内存详细分析
Reference: RTX 2060 6GB
Weights48.8 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom0.6 GB
常见问题
FAQ — Qwen3-Coder-Next
另请参阅