Qwen3.6 35B A3B: Agentic Coding with Hybrid Gated DeltaNet

Qwen3.6 35B A3B is the first open-weight model in the Qwen3.6 series, built on direct community feedback and focused on stability and real-world utility. It combines a hybrid Gated DeltaNet and Gated Attention architecture with sparse Mixture-of-Experts routing and a vision encoder for unified multimodal reasoning.

Key Features

• Agentic Coding - Handles frontend workflows and repository-level reasoning with improved fluency and precision over earlier Qwen generations
• Thinking Preservation - New option to retain reasoning context from historical messages, streamlining iterative development and reducing redundant token generation
• Hybrid Architecture - Alternating Gated DeltaNet and Gated Attention blocks combined with sparse MoE, balancing long-context efficiency against attention precision
• Sparse Mixture-of-Experts - 256 total experts with 8 routed and 1 shared expert active per token, delivering 35B total capacity with only 3B active parameters
• Multi-Token Prediction - Trained with multi-step MTP, enabling speculative decoding for lower-latency inference
• Native 262K Context - Handles 262,144 tokens natively, extensible up to 1,010,000 tokens via YaRN RoPE scaling
• Multimodal Inputs - Unified vision-language model supporting text, image, and video inputs
• Tool Calling - Native tool-calling support with the qwen3_coder parser for agent workflows

Benchmark Performance

Coding and Software Engineering:

• SWE-bench Verified: 73.4
• SWE-bench Multilingual: 67.2
• SWE-bench Pro: 49.5
• Terminal-Bench 2.0: 51.5
• LiveCodeBench v6: 80.4
• NL2Repo: 29.4
• QwenClawBench: 52.6

General Agent and Tool Use:

• TAU3-Bench: 67.2
• DeepPlanning: 25.9
• MCPMark: 37.0
• MCP-Atlas: 62.8
• WideSearch: 60.1

Knowledge:

• MMLU-Pro: 85.2
• MMLU-Redux: 93.3
• SuperGPQA: 64.7
• C-Eval: 90.0

STEM and Reasoning:

• GPQA: 86.0
• HLE: 21.4
• HMMT Feb 25: 90.7
• HMMT Nov 25: 89.1
• HMMT Feb 26: 83.6
• IMOAnswerBench: 78.9
• AIME26: 92.6

Use Cases

• Agentic coding tasks across frontend, backend, and repository-level workflows
• Multi-turn agent scenarios where preserved reasoning context improves decision consistency
• Tool-calling and MCP-based automation
• Competition-level mathematics and STEM reasoning
• Long-context document analysis up to 262K tokens natively
• Visual question answering and image-grounded reasoning
• Video understanding with configurable frame sampling

Architecture

Qwen3.6 35B A3B uses a 40-layer hybrid architecture organized as ten cycles of three Gated DeltaNet blocks followed by one Gated Attention block, each paired with a sparse Mixture-of-Experts feed-forward layer.

Gated DeltaNet provides linear-attention efficiency with a fixed-size recurrent state, keeping long-context compute and memory cost tractable. The interleaved Gated Attention blocks use 16 query heads and 2 key-value heads with a 256-dimensional head and a 64-dimensional rotary position embedding, preserving precise token-level attention where it is most valuable.

The Mixture-of-Experts layer routes each token through 8 of 256 available experts plus 1 shared expert, with a 512-dimensional expert intermediate size. The model is trained with Multi-Token Prediction across multiple steps, enabling speculative decoding at inference time.

A 2048-dimensional language backbone pairs with a vision encoder to form a unified multimodal model, supporting a 248,320-token padded vocabulary and handling text, image, and video inputs through a shared representation.

Deploy Qwen3.6 35B A3B on Vast.ai with vLLM, SGLang, or llama.cpp for efficient agentic coding, long-context reasoning, and multimodal inference on flexible GPU infrastructure.