Qwen3.5 is a multimodal foundation model family designed for reasoning, coding, agentic use, and visual understanding. It was introduced by the Qwen Team in the release post Qwen3.5: Towards Native Multimodal Agents (2026).
Official model weights, repository resources, and deployment guidance are available through the official GitHub repository, as well as Hugging Face and ModelScope model pages.
Qwen3.5 is a native multimodal foundation model family that combines text and vision capabilities within a unified architecture. It is designed to improve performance across a broad set of tasks, including reasoning, coding, agent workflows, and visual understanding, while also emphasizing efficient inference and scalable deployment.
A central idea in Qwen3.5 is its unified vision-language foundation, where early fusion training over massive multimodal data aims to provide strong cross-modal generalization. The model family also introduces an efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts, alongside large-scale reinforcement learning intended to improve adaptability in complex real-world environments.
Key traits of Qwen3.5:
- Native multimodal design: Integrates text and vision within one foundation model family.
- Efficient hybrid architecture: Combines Gated Delta Networks with sparse Mixture-of-Experts for high-throughput inference.
- Scalable RL training: Uses reinforcement learning across large agent-like training environments to improve generalization.
- Broad model family: Includes released sizes such as 397B-A17B, 122B-A10B, 35B-A3B, 27B, 9B, 4B, 2B, and 0.8B.
- Wide language coverage: Expands support to 201 languages and dialects for broader global usability.
- Qwen3.5 is presented as a multimodal agent-oriented model family that supports reasoning, coding, tool use, and visual understanding in a single framework.
- The architecture combines early vision-language fusion with an efficient sparse MoE-style backbone, aiming to balance capability and inference efficiency.
- The released ecosystem includes official support for Qwen Chat, Qwen API, Qwen Code, and Qwen Agent, showing that the models are intended not only for chat but also for agentic workflows and application integration.
- For local or production deployment, Qwen3.5 is supported by multiple inference frameworks, including Transformers, llama.cpp, MLX, SGLang, and vLLM.
- The open-weight models are released under the Apache 2.0 license.
Qwen3.5 is intended for:
- General-purpose multimodal AI assistants combining text and image understanding.
- Reasoning and coding workflows, including development support and code understanding.
- Agentic applications involving planning, memory, and tool usage through frameworks such as Qwen Agent.
- Flexible deployment scenarios, from local inference to large-scale API serving.
Limitations:
- Qwen3.5 is a family of models rather than a single configuration, so practical performance and hardware requirements vary significantly by model size.
- Some official usage documentation is still marked as coming soon, so developers may need to rely on model cards and deployment examples in the meantime.
- Efficient deployment of larger variants may require specialized inference frameworks such as vLLM or SGLang, along with substantial compute resources.
- Because the series evolves rapidly, benchmark leadership and preferred model variants may change as more releases and optimizations appear.
¶ BibTeX entry and citation info
@misc{qwen3.5,
title = {{Qwen3.5}: Towards Native Multimodal Agents},
author = {{Qwen Team}},
month = {February},
year = {2026},
url = {https://qwen.ai/blog?id=qwen3.5}
}