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Qwen3.6‑35B
Developer & Data Science ToolsModel Training & DeploymentLLM models
Run a 35-billion-parameter MoE model with only 3 billion parameters actually activated, while achieving coding, vision, and reasoning performance on par with much larger models. Optimized for agentic coding, multimodal perception, and very long contexts (up to over a million tokens)
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90,971Directory views
Category
Developer & Data Science ToolsModel Training & Deployment
Listed
June 2026Developer & Data Science Tools, Model Training & Deployment
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discord.ggPros and cons
Pros
- High-efficiency inference using only 3 billion active parameters per request
- Massive context window supporting over one million tokens for long-form analysis
- Specialized optimization for agentic coding and autonomous developer tasks
- Integrated multimodal perception capable of processing both vision and text inputs
Cons
- MoE architectures can occasionally produce inconsistent results across highly specialized domains
- Local deployment still requires hardware capable of housing 35B total parameters despite 3B activation
- Performance in agentic workflows may vary based on the complexity of the specific coding environment
- Free access tiers often come with undisclosed rate limits or usage caps that require verification
FAQ
How does the 35B MoE architecture differ from standard dense models?
The Mixture-of-Experts (MoE) architecture allows the model to access a 35-billion-parameter knowledge base while only activating 3 billion parameters during inference. This design is intended to provide the reasoning and coding depth of a much larger model while maintaining the speed and computational efficiency typically associated with smaller, more agile AI systems.
What are the practical benefits of the 1M+ token context window?
A context window exceeding one million tokens enables the model to process massive datasets, such as entire code repositories or lengthy technical manuals, in a single prompt. This is a critical evaluation factor for developers building agents that need to maintain long-term coherence and reference distant information without losing context.