Qwen3-Coder-Next by the Numbers

Why 3B active matters: With only 3B parameters activating per token, Qwen3-Coder-Next generates code at speeds comparable to small models -- while delivering quality that rivals models 20x its active size. This is the MoE advantage applied directly to coding.

Coding Performance Deep-Dive

SWE-Bench Verified measures a model's ability to solve real GitHub issues -- actual bugs and feature requests from production repositories. At 70.6%, Qwen3-Coder-Next resolves over two-thirds of these real-world coding tasks without human intervention.

What makes this model particularly strong for developers:

Running It on Your Mac

Despite the 80B total parameter count, Qwen3-Coder-Next's MoE architecture makes it surprisingly Mac-friendly:

64GB Mac (M4 Max / M5 Max)

  • Quantization: Q4_K_M (~45GB on disk)
  • Speed: ~12 tok/s for code generation
  • Usable context: ~64K tokens
  • Verdict: Fully usable for real development work

128GB Mac (M4/M5 Ultra Mac Studio)

  • Quantization: Q8_0 (~80GB on disk)
  • Speed: ~8 tok/s
  • Usable context: ~128K tokens
  • Verdict: Premium quality, full codebase context

At 12 tok/s on Q4, code generation feels responsive enough for interactive use. You ask for a function, the model starts outputting code within a second, and a typical 50-line function completes in about 25 seconds. Not instant, but fast enough to keep your flow.

Installation Guide

The simplest path is Ollama:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull Qwen3-Coder-Next
ollama pull qwen3-coder-next

# Start coding
ollama run qwen3-coder-next "Write a Python function to parse nested JSON with error handling"

For integration with your editor, pair Ollama with Continue.dev (VS Code / JetBrains) or Cody for a Copilot-like experience backed by Qwen3-Coder-Next running entirely on your machine:

# In Continue.dev config, set the model to:
{
  "model": "qwen3-coder-next",
  "provider": "ollama",
  "apiBase": "http://localhost:11434"
}

You can also run it through LM Studio if you prefer a GUI for model management and chat.

vs DeepSeek R1 for Coding

DeepSeek R1 is the other major contender for local coding AI. Here is how they compare:

Bottom line: For everyday code generation, completion, refactoring, and polyglot development, Qwen3-Coder-Next wins. For complex algorithmic reasoning and mathematical proofs in code, DeepSeek R1's chain-of-thought approach has an edge.

The 480B Flagship: Qwen3-Coder

Alibaba also released Qwen3-Coder-480B, the server-class flagship. At 480B total parameters it is not runnable on any Mac, but it pushes SWE-Bench to 76.2% and represents the state of the art in open-source code generation.

For Mac users, the 80B Qwen3-Coder-Next is the right model. It captures the vast majority of the flagship's coding ability in a package that fits on consumer hardware. The architecture innovations from the 480B model are distilled down into the Next variant.

Check our leaderboard to see how Qwen3-Coder-Next ranks against every other coding model, filtered by Mac compatibility.