# Meituan Ships a Lighter, Sparser LongCat-Flash

> The food-delivery giant's newest open model trims its mixture-of-experts design for more efficient inference under an MIT license.

Published by The Open Weights on Jul 31, 2026. Canonical: https://theopenweights.com/news/longcat-flash-lite-sparse-guvc

## Key facts

- Company: Meituan
- Model: LongCat-Flash-Lite-Sparse
- Version: Flash-Lite-Sparse
- Category: Text / LLM
- Modalities: Text / LLM
- License: MIT (Open weights, commercial use allowed)
- Context window: 983K tokens
- Architecture: LongcatCausalLM
- Precision: BF16
- Weights size: 138.3 GB
- Significance: notable
- Published: 2026-07-31
- Last verified: 2026-08-07
- Hugging Face: https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse
- Canonical URL: https://theopenweights.com/news/longcat-flash-lite-sparse-guvc

Meituan has released [LongCat-Flash-Lite-Sparse](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse), a lighter and more sparsely activated variant of its LongCat-Flash family of large language models. The model is published on Hugging Face under a permissive MIT license, making it freely available for both research and commercial use.

Like its siblings, the model uses a mixture-of-experts (MoE) architecture, in which only a subset of the network's parameters activate for any given token. The "Lite" and "Sparse" labels signal an emphasis on efficiency: fewer active parameters per forward pass typically means lower memory pressure and faster inference, which matters for teams running models at scale rather than in a lab.

## Why it matters

Meituan is better known as one of China's largest on-demand delivery platforms than as an AI lab, and its steady stream of open releases is a reminder that serious open-weight work now comes from beyond the usual research houses. The choice of an MIT license is notable, placing the model among the more permissively licensed open releases available today.

A few things stand out about this release:

- It targets text generation as its primary modality.
- It leans on a sparse MoE design aimed at efficient serving.
- It carries an MIT license with no additional usage restrictions.

Meituan has not published detailed parameter counts or context-length figures alongside the initial listing, so practitioners will want to check the [model card](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse) for configuration details and any benchmark numbers before deploying.

## Get the model

- [Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse)

## Sources

- [meituan-longcat/LongCat-Flash-Lite-Sparse](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite-Sparse) — Hugging Face, Jul 31, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Meituan Ships a Lighter, Sparser LongCat-Flash", The Open Weights, Jul 31, 2026, https://theopenweights.com/news/longcat-flash-lite-sparse-guvc