Qwen3-Coder-Next: Efficient Agentic Coding Model
Qwen3-Coder-Next, built on hybrid MoE architecture, matches Claude Sonnet on agentic coding and browser tasks at lower cost, with 256K context extendable to 1M tokens.
Hybrid Architecture Delivers Top Agentic Performance
Qwen3-Coder-Next uses Qwen3-Next-80B-A3B-Base's hybrid attention and MoE for strong coding and agentic abilities via scalable training on executable tasks, environment interaction, and RL. Available sizes include Qwen3-Coder-Next (instruct/base), Qwen3-Coder-480B-A35B-Instruct, and Qwen3-Coder-30B-A3B-Instruct, all with 256K native context (extendable to 1M via Yarn). FP8 and GGUF variants reduce inference costs while rivaling Claude Sonnet on agentic coding, browser-use, and foundational tasks. Supports platforms like Qwen Code, CLINE, Claude Code with custom function calling via SGLang/vLLM tool parser; updated tokenizer ensures Qwen3 consistency, dropping
Chat and Code Generation Quickstarts
Load via transformers: use from_pretrained on tokenizer/model, apply_chat_template with add_generation_prompt for ChatML format (<|im_start|>assistant\n), generate with max_new_tokens, then batch_decode. Instruct models handle chatting directly. For fill-in-the-middle (FIM), prefix <FIM_PREFIX>, suffix <FIM_SUFFIX>, middle <FIM_MIDDLE> per arXiv:2207.14255—supported across all Qwen3-Coder versions for code insertion in context gaps.
Demos Showcase Autonomous Agents
Qwen3-Coder-Next builds full websites (e.g., Qwen history page deployed via Alibaba Cloud Nginx), tidies desktops via environment interaction, implements reverse-tower-defense game Zombies vs. Plants (5x9 grid, 120s timer, zombie types at 50-150 brain cost, plants with HP/damage stats, collision/particle effects), creates sound ASCII art drawers (mouse/touch, pattern switcher, harmonious notes), vibe-checks sites by clicking/reporting bugs, and renders 800-1200 particle systems (cursor forces, FPS counter, requestAnimationFrame). Videos prove end-to-end execution from natural language prompts.