Qwen3.5 122B A10B int4 AutoRound EC

Qwen3.5

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

Released April 2026context N/A122B params

A large mixture-of-experts model that punches above its weight by activating only 10B parameters per forward pass despite having 122B total parameters. The int4 AutoRound quantization keeps memory footprint manageable while preserving reasoning quality. Handles both text and image inputs, making it a versatile open-weight option for multimodal tasks.

Capabilities

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

Reasoning & Logic

Exceptional

Coding

Strong

Tool Use

Use Case Fit

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

Qwen3.5 122B A10B int4 AutoRound EC

Qwen3.5

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

Released April 2026context N/A122B params

A large mixture-of-experts model that punches above its weight by activating only 10B parameters per forward pass despite having 122B total parameters. The int4 AutoRound quantization keeps memory footprint manageable while preserving reasoning quality. Handles both text and image inputs, making it a versatile open-weight option for multimodal tasks.

Capabilities

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

Reasoning & Logic

Exceptional

Coding

Strong

Tool Use

Use Case Fit

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

Glossary

AutoRoundAn automated quantization method that intelligently rounds weights to lower precision while minimizing the loss in model performance.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal TasksAI tasks that require processing multiple types of input data at once, such as understanding both an image and a text question about it.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.