Model overview Model overview Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-GGUF is a compact reasoning model built on the Qwen3.5-9B architecture that has been fine-tuned to inherit the structured thinking patterns of Claude-4.6 Opus. Created by Jackrong, this model combines the efficiency of a 9-billion parameter base with reasoning capabilities distilled from a much larger teacher model. The training process injected high-quality reasoning trajectories across science, mathematics, and instruction-following tasks, making it lighter than the 27B variant while maintaining strong analytical capabilities. For users seeking a smaller alternative, the 2B version provides an even more compact option. Model inputs and outputs Model inputs and outputs This model processes text inputs and generates responses that include structured internal reasoning followed by final answers. It operates within a 16,384 token context window, allowing it to handle complex multi-step problems while maintaining visibility into its thinking process. The model formats its reasoning in dedicated `` tags, separating internal logic from the final response output. Inputs Inputs - User queries or prompts of any complexity level, from straightforward questions to intricate multi-step problems - Contextual information up to 16,384 tokens that the model can reference during reasoning - Structured instructions that benefit from step-by-step analytical breakdown Outputs Outputs - Internal reasoning sequences wrapped in `` tags showing the model's analytical approach - Final answers that follow the reasoning blocks, delivering precise and nuanced solutions - Structured problem decomposition breaking complex tasks into clearly defined subcomponents Capabilities Capabilities The model demonstrates modular and structured thinking inherited from Opus-level reasoning patterns. It parses prompts with confidence, establishing outlined plans in its thinking blocks rather than falling into exploratory trial-and-error patterns. The model handles extended reasoning traces without redundant cognitive loops, adopting an efficient methodology: identifying core objectives, breaking tasks into subcomponents, evaluating constraints and edge cases, formulating step-by-step solution plans, and executing reasoning sequentially with consistency verification. Training on datasets including [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) has strengthened its ability to tackle diverse reasoning challenges with improved reasoning diversity. What can I use it for? What can I use it for? This model serves offline analytical tasks, coding challenges, mathematical problem-solving, and logic-dependent prompting where transparency in reasoning is valuable. It works for software development when you need the model to show its planning approach before implementation, scientific analysis requiring step-by-step methodology verification, mathematical proofs needing rigorous intermediate steps, and complex decision-making scenarios where understanding the reasoning chain matters. The compact size makes it suitable for edge deployment and resource-constrained environments while maintaining reasoning quality, making it practical for applications requiring both performance and interpretability. Things to try Things to try Test the model on problems that benefit from visible reasoning scaffolds rather than direct answers. Mathematical puzzles with multiple solution paths highlight how the structured thinking avoids redundant loops compared to standard models. Complex coding tasks that require planning before execution show where the `` blocks provide genuine value. Try comparing its reasoning efficiency on simple versus complex queries to observe how it scales analytical depth appropriately. Use it for research tasks where you need to follow the model's logical chain to verify correctness, or for educational purposes where understanding the thinking process matters as much as the final answer. This is a simplified guide to an AI model called Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-GGUF maintained by Jackrong. 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 Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-GGUF maintained by Jackrong. 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 Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-GGUF maintained by Jackrong. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.

A Compact Reasoning Model That Shows Its Work Before Answering

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