Qwen3-Coder-Next-Base

Qwen3-Coder-Next-Base#

Highlights#

Today, we're announcing Qwen3-Coder-Next-Base, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:

• Advanced architecture: It integrates the Hybrid Attention with highly sparse MoE, enabling high throughput and strong ultra-long-context modeling.
• Robust data foundation: Trained on highly diverse, broad-coverage corpora, with native 256K context and support for 370+ languages, it leaves ample headroom for post-training.
• Agentic coding capability: With a carefully designed training recipe, it has strong capabilities in tool calling, scaffold/template adaptation, and error detection/recovery, making it a strong backbone for reliable coding agents.

Model Overview#

Qwen3-Coder-Next-Base has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining
• Number of Parameters: 80B in total and 3B activated
• Number of Parameters (Non-Embedding): 79B
• Hidden Dimension: 2048
• Number of Layers: 48
  • Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE))
• Gated Attention:
  • Number of Attention Heads: 16 for Q and 2 for KV
  • Head Dimension: 256
  • Rotary Position Embedding Dimension: 64
• Gated DeltaNet:
  • Number of Linear Attention Heads: 32 for V and 16 for QK
  • Head Dimension: 128
• Mixture of Experts:
  • Number of Experts: 512
  • Number of Activated Experts: 10
  • Number of Shared Experts: 1
  • Expert Intermediate Dimension: 512
• Context Length: 262,144 natively

NOTE: This model supports only non-thinking mode and does not generate <think></think> blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Best Practices#

To achieve optimal performance, we recommend the following sampling parameters: temperature=1.0, top_p=0.95, top_k=40.

Citation#

If you find our work helpful, feel free to give us a cite.

@techreport{qwen_qwen3_coder_next_tech_report,
  title        = {Qwen3-Coder-Next Technical Report},
  author       = {{Qwen Team}},
  url          = {https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf},
  note         = {Accessed: 2026-02-03}
}