Qwen3.6 27B Claude Opus Sonnet Distilled NVFP4 MTP

Qwen 3

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

Released May 2026context N/A27B params

A distilled version of Claude Opus and Sonnet behavior packed into a 27B Qwen3 architecture, quantized to NVFP4 format with multi-token prediction support. It inherits reasoning patterns from its teacher models while running at reduced precision, which can mean faster inference at some cost to fidelity. Community-published and openly weighted, so behavior may vary from official releases.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Qwen3.6 27B Claude Opus Sonnet Distilled NVFP4 MTP

Qwen 3

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

Released May 2026context N/A27B params

A distilled version of Claude Opus and Sonnet behavior packed into a 27B Qwen3 architecture, quantized to NVFP4 format with multi-token prediction support. It inherits reasoning patterns from its teacher models while running at reduced precision, which can mean faster inference at some cost to fidelity. Community-published and openly weighted, so behavior may vary from official releases.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.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.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Multi-Token PredictionGenerating multiple future tokens in parallel instead of one at a time.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.Token PredictionThe core task of predicting what word or subword (token) should come next in a sequence based on previous text.