An In-Depth Look at Alibaba’s Qwen 3.5

By Ashiq

 

The Rise of Native Multimodal Agents: An In-Depth Look at Alibaba’s Qwen 3.5

The global landscape of artificial intelligence has reached a new milestone with the release of Qwen 3.5, the latest flagship model series from Alibaba Group. Unveiled on the eve of the 2026 Chinese New Year, Qwen 3.5 represents a strategic shift from traditional large language models toward native multimodal agents. By integrating advanced reasoning, coding proficiency, and autonomous agentic capabilities into a single, highly efficient architecture, Alibaba aims to challenge the dominance of Western frontier models such as OpenAI’s GPT-5.2 and Anthropic’s Claude 4.5 Opus.


Architectural Innovation: Power Meets Efficiency

At the core of Qwen 3.5 lies a sophisticated hybrid architecture that balances massive scale with remarkable inference efficiency. The first model in the series, Qwen3.5-397B-A17B, utilizes a combination of Gated Delta Networks (linear attention) and a Sparse Mixture-of-Experts (MoE) framework. This design allows the model to maintain a vast knowledge base while significantly reducing the computational cost of each interaction.


  • Parameter Scaling: The model comprises a total of 397 billion parameters, yet only 17 billion are activated during any single forward pass. This sparse activation strategy ensures that the model remains responsive and cost-effective for large-scale deployments.

  • Inference Throughput: Compared to its predecessor, Qwen3-Max, the new architecture delivers a 19.0x increase in decoding throughput at a 256k context length. This leap in efficiency makes it one of the fastest high-parameter models currently available.

  • Native Multimodality: Unlike models that rely on separate encoders for different data types, Qwen 3.5 is built on an early-fusion foundation. This allows it to process text, images, and video tokens simultaneously, leading to a more holistic understanding of complex, real-world environments.

Redefining AI Agents: Autonomy and Tool Use

The most significant advancement in Qwen 3.5 is its focus on agentic capabilities. Alibaba has positioned this model as a "native agent," capable of executing multi-step tasks with minimal human supervision. This development aligns with the broader industry trend toward AI systems that do not just generate text but also take actions.


  • Agentic Reasoning: Qwen 3.5 demonstrates superior performance in benchmarks like BFCL-V4 and VITA-Bench, which evaluate a model's ability to use tools, plan complex workflows, and handle dynamic environments.

  • OpenClaw Compatibility: The model is fully compatible with open-source AI agent frameworks such as OpenClaw. This interoperability allows developers to integrate Qwen 3.5 into existing agentic ecosystems, leveraging its "visual agentic capabilities" to interact with graphical user interfaces (GUIs) on web, iOS, and Android platforms.

  • Hosted Capabilities: The hosted version, Qwen3.5-Plus, features a 1 million token context window by default and includes built-in adaptive tool use, enabling it to manage massive datasets and long-running autonomous tasks without context degradation.

Benchmark Performance and Global Reach

Alibaba’s self-reported evaluations indicate that Qwen 3.5 is highly competitive with the world’s leading AI models. It excels in STEM subjects, coding, and multilingual understanding, reflecting the extensive reinforcement learning (RL) scaling used during its post-training phase.


  • Multilingual Support: The model’s language coverage has expanded from 119 to 201 languages and dialects. A new 250k-token vocabulary further boosts encoding and decoding efficiency by up to 60% for many non-English languages.

  • Reasoning and STEM: In benchmarks such as MMLU-Pro and SuperGPQA, Qwen 3.5 achieves scores that are on par with or exceed those of GPT-5.2 and Gemini-3 Pro, particularly in complex reasoning and scientific knowledge tasks.

  • Coding Proficiency: The model shows strong results in SWE-bench Verified and Terminal-Bench 2, highlighting its ability to act as a reliable coding assistant capable of debugging and maintaining large codebases.

Infrastructure and Training

The development of Qwen 3.5 was supported by a heterogeneous infrastructure that decouples parallelism strategies across vision and language components. This approach avoids the inefficiencies of uniform training methods and allows for near 100% throughput on mixed-modality data. Furthermore, a native FP8 pipeline was implemented to reduce activation memory by 50%, ensuring stable scaling to tens of trillions of tokens.

Conclusion

Qwen 3.5 marks a pivotal moment for Alibaba and the broader open-source AI community. By prioritizing native multimodality and agentic autonomy, Alibaba has created a versatile platform that is as capable of reasoning through a complex legal document as it is of navigating a mobile application to complete a purchase. As the "DeepSeek Moment" becomes the new normal for high-performance, efficient AI, Qwen 3.5 stands as a formidable contender in the race to build truly autonomous digital assistants.

References

  1. Qwen3.5: Towards Native Multimodal Agents - Official Alibaba Qwen Blog.

  2. Alibaba unveils Qwen3.5 as China’s chatbot race shifts to AI agents - CNBC News Report.

  3. Qwen/Qwen3.5-397B-A17B Model Card - Hugging Face Repository.

  4. Alibaba debuts Qwen 3.5, adding "visual agentic capabilities" - Techmeme / Industry Coverage.

  5. QwenLM/Qwen3.5 GitHub Repository - Technical documentation and implementation details.

  6. DeepSeek V3 vs Qwen3 Max Benchmarks - Spectrum AI Labs Comparison.

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