Qwen3.5 35B A3B Claude 4.6 Opus Reasoning Distilled

Qwen3.5

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

Released March 2026context N/A

A reasoning-focused distillation that combines Qwen3.5's architecture with knowledge transferred from Claude Opus's reasoning capabilities. It punches above its active parameter count (3B active out of 35B total) thanks to its mixture-of-experts design, making it relatively efficient at inference time. As a distilled model, it may inherit some of the teacher model's reasoning patterns while potentially losing nuance at the edges of complex tasks.

Capabilities

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

Reasoning & Logic

Strong

Coding

Qwen3.5 35B A3B Claude 4.6 Opus Reasoning Distilled

Qwen3.5

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

Released March 2026context N/A

A reasoning-focused distillation that combines Qwen3.5's architecture with knowledge transferred from Claude Opus's reasoning capabilities. It punches above its active parameter count (3B active out of 35B total) thanks to its mixture-of-experts design, making it relatively efficient at inference time. As a distilled model, it may inherit some of the teacher model's reasoning patterns while potentially losing nuance at the edges of complex tasks.

Capabilities

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

Reasoning & Logic

Strong

Coding

Glossary

Active Parameter CountThe number of model parameters that are actually used during inference for a given input, as opposed to the total parameters available.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.Inference TimeThe amount of time it takes for a model to process input and generate output after it has been trained.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning CapabilitiesThe model's ability to work through multi-step problems methodically and show its thinking process rather than jumping to answers.Reasoning-FocusedA model specifically trained to work through multi-step logical problems methodically rather than generating quick responses.

Capabilities

Capabilities

Use Case Fit

Glossary