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LatestMeituan2.0
MeituanText / LLM

Meituan releases LongCat-2.0 language model

The Chinese delivery giant continues its push into open AI with a new text model on Hugging Face.

Jun 30, 2026
NotableOther
LongCat-2.0

Meituan, the Chinese super-app best known for food delivery and local services, has released LongCat-2.0, a text-focused large language model now published on Hugging Face. The release continues the company's expansion beyond its core commerce business into foundation models.

The LongCat line is part of a growing wave of releases from Chinese technology firms that have made their models openly downloadable rather than gating them entirely behind APIs. As with many of these efforts, LongCat-2.0 ships under a custom license rather than a standard permissive one, so teams evaluating it for production should read the terms closely before building on top of it.

Why it matters

Meituan is not a traditional AI lab, and its move to publish model weights signals how broadly large-platform companies now see in-house language models as strategic infrastructure. For developers, each new openly available model widens the pool of options outside the handful of dominant Western and Chinese labs.

  • The model targets text generation, Meituan's stated primary modality.
  • It is distributed openly through Hugging Face for download and inspection.
  • Licensing is custom, which carries implications for commercial use.

Detailed specifications — including parameter count, context length, and benchmark results — were not part of the initial record, so the model card itself remains the authoritative reference for anyone planning to test it.

Sources

  • meituan-longcat/LongCat-2.0

    Hugging Face

    Visit
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Get the model

Hugging Face

Specs

Context window262K tokens
Size3.6 TB
PrecisionBF16
ArchitectureLongcatCausalLM
LicenseOTHER
Downloads2.8K
Likes284

Can you run it?

Datacenter only — about 1.1 TB even at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    3.6 TB
  • 8-bit

    Multi-node cluster

    1.9 TB
  • 4-bit

    Multi-node cluster

    1.1 TB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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