# Xing4.0 arrives as a 29B MoE with 4B active params

> inclusionAI's new text model uses a mixture-of-experts design to keep compute low while shipping under an Apache-2.0 license.

Published by The Open Weights on Sep 16, 2026. Canonical: https://theopenweights.com/news/xing4-0-29b-a4b-dpmk

## Key facts

- Company: XingChen AGI
- Model: Xing4.0-29B-A4B
- Version: 4.0-29B-A4B
- Category: Text / LLM
- Modalities: Text / LLM
- License: Apache 2.0 (Open weights, commercial use allowed)
- Parameters: 29B (mixture of experts)
- Active parameters: 4B
- Context window: 262K tokens
- Architecture: Xing4_0ForCausalLM
- Precision: BF16
- Weights size: 62.4 GB
- Significance: minor
- Published: 2026-09-16
- Last verified: 2026-10-04
- Hugging Face: https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B
- Canonical URL: https://theopenweights.com/news/xing4-0-29b-a4b-dpmk

inclusionAI has published Xing4.0-29B-A4B, a text-focused large language model built on a mixture-of-experts (MoE) architecture. The [model is available on Hugging Face](https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B) under a permissive Apache-2.0 license, which allows both commercial and research use.

The headline detail is the balance between total and active parameters. Xing4.0 carries 29 billion parameters overall but activates only about 4 billion per token, following the increasingly popular MoE playbook of routing each input through a small subset of specialized experts.

## Why it matters

The appeal of a sparse design like this is efficiency. By keeping the active parameter count near 4B, the model can aim for the quality of a larger network while running with the compute and memory footprint closer to a much smaller one — useful for teams that want capable text generation without heavyweight inference costs.

- 29B total parameters, roughly 4B active per token
- Mixture-of-experts architecture
- Text-only (LLM) modality
- Apache-2.0 license

As an initial release for the Xing family, the model's real-world performance and context handling remain to be seen; inclusionAI has not detailed benchmarks or context length in the release record. Interested developers can find weights and any accompanying documentation on the [Hugging Face repository](https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B).

## Get the model

- [Hugging Face](https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B)

## Sources

- [XingChen-AGI/Xing4.0-29B-A4B](https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B) — Hugging Face, Sep 16, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Xing4.0 arrives as a 29B MoE with 4B active params", The Open Weights, Sep 16, 2026, https://theopenweights.com/news/xing4-0-29b-a4b-dpmk