Model overview Qwen3.5-35B-A3B-Base is a pre-trained foundation model from Qwen designed for research, fine-tuning, and in-context learning experiments. This base model contains 35 billion total parameters with only 3 billion activated during inference, making it efficient compared to other models in the Qwen3.5 family. The model combines a vision encoder with language capabilities, positioning it as a unified multimodal foundation. If you need a post-trained version ready for direct use, the Qwen3.5-35B-A3B variant offers production-ready performance, while the Qwen3.5-122B-A10B provides significantly more capacity for complex tasks. Qwen3.5-35B-A3B-Base Qwen Qwen3.5-35B-A3B Qwen3.5-122B-A10B Model inputs and outputs The model processes text and visual information through an efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts layers. It generates text responses based on input prompts, supporting extended context windows up to 262,144 tokens natively with extensibility to over 1 million tokens. The architecture includes control tokens like <|im_start|> and <|im_end|> that enable efficient parameter-efficient fine-tuning approaches. <|im_start|> <|im_end|> Inputs Text prompts in 201 languages and dialects for diverse global applicationsVisual information integrated through early fusion multimodal trainingControl tokens for structuring conversations and fine-tuning workflows Text prompts in 201 languages and dialects for diverse global applications Text prompts Visual information integrated through early fusion multimodal training Visual information Control tokens for structuring conversations and fine-tuning workflows Control tokens Outputs Generated text responses across multiple languages with cultural and regional understandingReasoning outputs for complex problem-solving tasksCoded solutions for programming and development challenges Generated text responses across multiple languages with cultural and regional understanding Generated text Reasoning outputs for complex problem-solving tasks Reasoning outputs Coded solutions for programming and development challenges Coded solutions Capabilities The model delivers unified vision-language understanding through early fusion training, achieving performance parity across reasoning, coding, agent tasks, and visual comprehension. Its efficient hybrid architecture processes information through 40 layers with a mix of linear attention heads and gated attention mechanisms, selecting from 256 experts while activating only 9 at inference time. The expanded vocabulary of 248,320 tokens supports comprehensive linguistic coverage, and reinforcement learning training scaled across million-agent environments enables robust real-world adaptation. The model handles extended contexts natively and supports parameter-efficient fine-tuning without requiring embedding layer updates. What can I use it for? Developers and researchers can use this base model for fine-tuning on custom datasets, running in-context learning experiments, and building specialized AI agents. Organizations developing chatbots, content generation systems, or reasoning-heavy applications benefit from the efficient activation pattern that reduces computational overhead. The extended context window supports processing lengthy documents, codebases, and complex conversations. Compare with alternatives like the Qwen3.5-27B for smaller deployments or the larger Qwen3.5-397B-A17B for maximum performance. The technical report provides detailed guidance on implementation strategies. Qwen3.5-27B Qwen3.5-397B-A17B technical report Things to try Experiment with parameter-efficient fine-tuning using LoRA without updating embeddings, leveraging the control tokens built directly into the model. Test the multimodal capabilities by combining visual and textual inputs for tasks like image understanding with reasoning. Explore the extended context window by processing full documents or large codebases in a single inference. Try scaling reinforcement learning workflows across multiple agents to see how the model adapts to complex, progressively difficult task distributions. Investigate cross-lingual capabilities in any of the 201 supported languages to understand how cultural nuances transfer across different locales. This is a simplified guide to an AI model called Qwen3.5-35B-A3B-Base maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called Qwen3.5-35B-A3B-Base maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called Qwen3.5-35B-A3B-Base maintained by Qwen. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.

Qwen3.5-35B-A3B: The Multimodal Base Model That Only “Uses” 3B Params

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