ai/qwen3.5

https://github.com/unslothai/unsloth/https://unsloth.ai/docs/models/qwen3.5#how-to-enable-or-disable-reasoning-and-thinking.

Mar 5 'Final' Update:

• All GGUFs now use our new imatrix data. See some improvements in chat, coding, long context, and tool-calling use-cases.
• GGUFs now updated with an improved quantization algorithm.
• Rest of variants like Q8_0, Q4_K_M, BF16 are now uploaded.
• See our new benchmarks for https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks.

For Qwen3.5-35B-A3B, we primarily reduced the maximum KLD:

Feb 27 Update: GGUFs Refreshed + Tool-calling fixes + Benchmarks

• Qwen3.5 is now updated with improved tool-calling & coding performance! See improvements via Claude Code, Codex.
• We also benchmarked GGUFs & removed MXFP4 layers from 3 quants. https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks

Please follow the correct instructions / settings in our https://unsloth.ai/docs/models/qwen3.5.

Fine-tuning and RL Qwen3.5

• You can also fine-tune and perform https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide on all Qwen3.5 models with Unsloth via our https://unsloth.ai/docs/get-started/unsloth-notebooks#standard-sft-notebooks.
• Read our https://unsloth.ai/docs/models/qwen3.5/fine-tune for tips, VRAM requirements, code and more https://unsloth.ai/docs/models/qwen3.5/fine-tune.

Qwen3.5-35B-A3B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

[!Tip] For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by https://modelstudio.alibabacloud.com/.

In particular, Qwen3.5-Flash is the hosted version corresponding to Qwen3.5-35B-A3B with more production features, e.g., 1M context length by default and official built-in tools. For more information, please refer to the https://www.alibabacloud.com/help/en/model-studio/text-generation.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

• Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

• Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

• Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

For more details, please refer to our blog post https://qwen.ai/blog?id=qwen3.5.

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