ai/qwen3.6 repository overview

⁠Read our How to Run Qwen3.6 Guide!⁠

See Unsloth Dynamic 2.0 GGUFs⁠ for our quantization benchmarks.

⁠ ⁠ ⁠

• NEW: Developer Role Support so Qwen3.6 can work in Codex, OpenCode and more!
• Qwen3.6 can now be run and fine-tuned in Unsloth Studio⁠. Read our guide⁠.
• Tool calling improvements: Makes parsing nested objects to make tool calling succeed more.
• Example of Qwen3.6 (4-bit GGUF) running in Unsloth Studio with tool-calling:

⁠Qwen3.6-35B-A3B

Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.

⁠Qwen3.6 Highlights

This release delivers substantial upgrades, particularly in

• Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
• Thinking Preservation: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

For more details, please refer to our blog post Qwen3.6-35B-A3B⁠.

⁠Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 35B in total and 3B activated
  • Hidden Dimension: 2048
  • Token Embedding: 248320 (Padded)
  • Number of Layers: 40
  • Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 32 for V and 16 for QK
    • Head Dimension: 128
  • Gated Attention:
    • Number of Attention Heads: 16 for Q and 2 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
  • Mixture Of Experts
    • Number of Experts: 256
    • Number of Activated Experts: 8 Routed + 1 Shared
    • Expert Intermediate Dimension: 512
  • LM Output: 248320 (Padded)
  • MTP: trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

⁠Benchmark Results

⁠Language