Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.
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 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
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