Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled

Qwen3.6

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A35B params

A reasoning-focused distilled model that combines Qwen3.6's 35B parameter architecture with a sparse 3B active parameter design, drawing on Claude 4.7 Opus reasoning patterns through distillation. It operates efficiently by activating only a fraction of its total parameters per inference, balancing capability with compute cost. As a community-published derivative, its consistency and reliability may vary compared to first-party releases.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Qwen3.6 35B A3B Claude 4.7 Opus Reasoning Distilled

Qwen3.6

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A35B params

A reasoning-focused distilled model that combines Qwen3.6's 35B parameter architecture with a sparse 3B active parameter design, drawing on Claude 4.7 Opus reasoning patterns through distillation. It operates efficiently by activating only a fraction of its total parameters per inference, balancing capability with compute cost. As a community-published derivative, its consistency and reliability may vary compared to first-party releases.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Glossary

Active Parameter DesignA model architecture where only a subset of parameters are used for each token, reducing computational cost while maintaining model capacity.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.DistillationA technique that compresses a large, complex model into a smaller one by training it to mimic the larger model's behavior, resulting in faster inference with minimal loss of quality.DistilledA model that has been compressed by training a smaller model to mimic a larger, more capable model, reducing size and computational requirements while retaining performance.Distilled ModelA smaller, faster version of a larger model created by training it to mimic the larger model's behavior, reducing computational requirements while maintaining reasonable performance.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning-FocusedA model specifically trained to work through multi-step logical problems methodically rather than generating quick responses.

Capabilities

Capabilities

Use Case Fit

Glossary