Qwen3-Coder-Next-GGUF

Today, we're announcing **Qwen3-Coder-Next**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: - **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. - **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. - **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. **Qwen3-Coder-Next** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - 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

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