Xing4.0 29B A4B

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

Released December 2025262K context≈ 196,608 words

Xing4.0 29B A4B is a sparse mixture-of-experts style model from XingChen-AGI, activating around 4 billion parameters per forward pass despite its 29 billion total parameter count — meaning it aims to deliver capable text reasoning at a lower computational cost than its full size suggests. It handles an unusually large context window of 262,144 tokens, making it suited for long-document tasks. Specific capability details beyond its architecture are limited.

Capabilities

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

Long Context

Strong

Factual Knowledge

Xing4.0 29B A4B

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

Released December 2025262K context≈ 196,608 words

Xing4.0 29B A4B is a sparse mixture-of-experts style model from XingChen-AGI, activating around 4 billion parameters per forward pass despite its 29 billion total parameter count — meaning it aims to deliver capable text reasoning at a lower computational cost than its full size suggests. It handles an unusually large context window of 262,144 tokens, making it suited for long-document tasks. Specific capability details beyond its architecture are limited.

Capabilities

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

Long Context

Strong

Factual Knowledge

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.Text ReasoningA model's capability to analyze, interpret, and draw logical conclusions from textual information.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary