Model overview
`Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled` is a reasoning-focused language model built on the Qwen3.5 architecture and fine-tuned using Chain-of-Thought distillation from Claude-4.6 Opus. The model demonstrates structured problem-solving through explicit internal reasoning steps before generating final answers. This 35-billion parameter model represents a larger variant compared to smaller alternatives like the 27B version, offering enhanced capacity for complex analytical tasks. The training process focused on eliminating repetitive reasoning patterns common in base Qwen models, resulting in more efficient and transparent thinking processes.
Model inputs and outputs
The model operates on text inputs and produces structured text outputs containing visible reasoning steps. A key distinguishing feature is the use of explicit `` tags that encapsulate the model's internal reasoning before the final response. This design choice makes the model's decision-making process transparent to users, allowing them to follow the logical progression from problem analysis to solution delivery.
Inputs
- Text prompts of any complexity level, from simple questions to multi-step analytical problems
- Extended context up to 8,192 tokens, supporting longer documents and complex reasoning traces
- Structured instructions that benefit from step-by-step planning and breakdown
Outputs
- Structured responses containing visible reasoning within `` tags
- Final answers following the internal reasoning block
- Step-by-step explanations with clear intermediate logic for verification
Capabilities
This model excels at modular and structured thinking, demonstrating confidence in parsing problems and establishing outlined plans rather than exploratory trial-and-error approaches. It breaks down complex user problems into clearly defined components, applies systematic analytical methodologies, and delivers nuanced solutions. The model handles coding tasks, mathematical problem-solving, logic-dependent reasoning, and analytical work where transparency in decision-making matters. It leverages an 8,192 token context window that accommodates multi-step reasoning traces without memory constraints.
What can I use it for?
This model works well for offline analytical tasks where users need to follow and verify the AI's internal logic. Software development applications benefit from transparent step-by-step code reasoning and explanation. Mathematical problem-solving improves through visible intermediate steps and logical progression. Research and analysis tasks gain credibility when the reasoning process remains transparent throughout. Educational applications benefit from the model's ability to explain complex concepts with clear reasoning scaffolding. Projects requiring audit trails or explainable AI decisions find value in the visible thinking process. Consider pairing this with applications involving extended context reasoning or exploring the broader technical foundations of Qwen architectures.
Things to try
Examine how the model handles problems you provide by studying the contents of the `` tags—this reveals whether the reasoning matches your expectations or identifies gaps in logical progression. Test the model on problems that previously produced vague or unexplained answers to see if visible reasoning improves clarity. Try using it for code review scenarios where you provide buggy code and ask it to reason through the issues step-by-step. Challenge the model with multi-part problems that require maintaining context across several reasoning steps within the 8,192 token window. Verify the quality of its reasoning on math problems by checking intermediate calculations rather than relying solely on final answers. Compare outputs with and without detailed instructions to observe how well it structures thinking in response to explicit scaffolding requests.
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