LLM
LongCat-2.0
Meituan 1.6 trillion parameter MoE language model with sparse attention for long-horizon coding and agentic tasks.
Levelmodel
ClassLLM
Parameters1.6T
Contextnot in the notes
Released29 Jun 2026
Opennessopen weights permissive
Runs onnot in the notes
Statusactive
R&D Topics
Identity
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- MoE LongCat Sparse Attention and N-gram Embedding (135B N-gram embedding parameters); trained on 1M-context data
- Total parameters
- 1.6T sources
- Active parameters
- 48B
- Checkpoint parameters
- –
- Note
- About 48B activated per token
- Context length
- not in the notes
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open weights permissive
- Licence
- MIT License sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperMeituanbig tech, China
Meituanbig tech, ChinaModelLongCat-2.0Meituan, LLM
LongCat-2.0Meituan, LLMR&D TopicsAgents and Tool UseEfficiency Architectures (MoE, Sparse Attention, Long Context)Open-Weight Frontier Releases as Research Substrate
Use CasesProcess Optimisation and Advanced Process ControlIndustrialClinical LLM (Open Weights)HealthcareTelecom Domain LLMs and BenchmarksTelecomUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026MIND IDResearch, 14 Oct 2025
Conch CementIn production, 23 Jun 2025
PT Kalbe Morinaga IndonesiaIn production, Oct 2024+2 moreDeployments of these use cases; none names this model.
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026MIND IDResearch, 14 Oct 2025Conch CementIn production, 23 Jun 2025PT Kalbe Morinaga IndonesiaIn production, Oct 2024+2 moreDeployments of these use cases; none names this model.Models 1
Organisations 1
R&D topics 3
Open-Weight Frontier Releases as Research SubstrateUsing released weights as the base for downstream research.used byEfficiency Architectures (MoE, Sparse Attention, Long Context)Reducing training and inference cost per capability.produced byAgents and Tool UseSystems that plan, call tools and act over multi-step tasks.produced by
Sources
- huggingface.co/meituan-longcat/LongCat-2.0 https://huggingface.co/meituan-longcat/LongCat-2.0
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "LongCat-2.0", https://aurion.semai.id/models/meituan-longcat-2-0/, records of October 2026.