Qwen3-Coder-Next: 3B Model Tops Coding Agents
Qwen3-Coder-Next uses hybrid MoE architecture and scaled agentic training on verifiable tasks to hit 70%+ on SWE-Bench Verified, matching 10-20x larger models at lower inference cost.
Agentic Training Unlocks Long-Horizon Coding
Qwen3-Coder-Next builds on Qwen3-Next-80B-A3B-Base's hybrid attention and Mixture-of-Experts (MoE) for efficient inference with just 3B active parameters. Instead of scaling parameters, it scales agentic signals via verifiable coding tasks in executable environments, incorporating environment feedback through reinforcement learning. The training pipeline includes continued pretraining on code- and agent-centric data, supervised fine-tuning (SFT) on high-quality agent trajectories, domain-specialized training (software engineering, QA, web/UX), and expert distillation into a single deployable model. This emphasizes long-horizon reasoning, tool usage, and recovery from execution failures—key for production coding agents handling multi-turn interactions.
To replicate: pair tasks with environments for direct feedback, prioritize trajectories showing tool calls and error recovery, and distill multi-expert setups for single-model efficiency. Result: models learn autonomous coding without constant human intervention.
Pareto-Optimal Efficiency on Agent Benchmarks
On SWE-Bench Verified, Qwen3-Coder-Next scores over 70% using SWE-Agent scaffolding, staying competitive on multilingual SWE-Bench and tougher SWE-Bench Pro. It outperforms or matches larger open-source models on TerminalBench 2.0 and Aider despite smaller size. Scaling agent turns boosts SWE-Bench Pro results, proving strength in extended reasoning—more turns yield higher solve rates.
Efficiency edge: 3B active parameters deliver SWE-Bench Pro performance of models with 10×–20× more active params, shifting the Pareto frontier for cost-effective agent deployment. Deploy locally for fast inference without cloud dependency, ideal for tools like OpenClaw, Cline, or browser agents.
| Benchmark | Qwen3-Coder-Next Score | Comparison |
|---|---|---|
| SWE-Bench Verified | >70% | Tops small models |
| SWE-Bench Pro | Competitive, scales with turns | Equals 10-20x larger |
Deployable Demos Prove Real-World Fit
Integrate into apps like Qwen Code, Claude Code, or coder.qwen.ai for tasks: build chat interfaces (Web Dev), desktop cleanup (CLI), multicolor animations (Cline), Gomoku games, Amazon product searches (Browser Agent), or Qwen3-Coder-Next web pages (OpenClaw). These showcase tool use, environment interaction, and rapid prototyping.
Future: enhance reasoning/decision-making, expand task support, iterate via user feedback. Access via GitHub, Hugging Face, ModelScope for immediate testing.