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.
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.
Inputs
- Text prompts in 201 languages and dialects for diverse global applications
- Visual information integrated through early fusion multimodal training
- Control tokens for structuring conversations and fine-tuning workflows
Outputs
- Generated text responses across multiple languages with cultural and regional understanding
- Reasoning outputs for complex problem-solving tasks
- Coded solutions for programming and development challenges
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.
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.
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