LongCat-Flash-Chat
LongCat-Flash-Chat is a 560-billion-parameter open-source language model developed by Meituan using a Mixture-of-Experts (MoE) architecture. It dynamically activates 18.6–31.3 billion parameters per token (averaging ~27B) based on context, enabling efficient inference at >100 tokens/second. Licensed under MIT, it is free for commercial use. The model is positioned for agentic tasks, reasoning, and coding, with evaluation results competitive with DeepSeek V3.1 and Qwen3 MoE on most benchmarks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | meituan-longcat |
| Parameters | 561.9B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 83.6k |
| Likes | 537 |
| Last updated | 2025-09-24 |
| Source | meituan-longcat/LongCat-Flash-Chat |
What LongCat-Flash-Chat is
LongCat-Flash-Chat is a 560B-parameter MoE model with dynamic token-level parameter activation, shortcut-connected MoE (ScMoE) architecture to reduce communication overhead, and a multi-stage training pipeline optimized for agentic capabilities. Key architectural features include zero-computation experts, PID-controller-based expert load balancing, and deterministic computation for reproducibility. Context length extended to 128k during mid-training. Inference throughput >100 TPS. Model card references a technical report (arxiv:2509.01322) and eval results on 30+ benchmarks including MMLU, ArenaHard-V2, AIME25, and agentic tool-use tasks (τ²-Bench).
Run LongCat-Flash-Chat locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="meituan-longcat/LongCat-Flash-Chat")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE (not explicitly stated in card): ~560B parameters at fp32 = ~2.2TB (not practical); at bfloat16/fp16 = ~1.1TB. With MoE dynamic activation (~27B avg per token), inference-time memory demand reduced. Typical deployment: multi-GPU setup (8× H100/A100 80GB or equivalent) for full model; quantized/LoRA variants may run on 2–4 GPUs. Requires verification of actual VRAM footprint, quantization strategies, and batching overhead from source repo.
Model card lists 'custom_code' tag, indicating custom modules (likely ScMoE, zero-computation experts). Standard LoRA/QLoRA compatibility Unknown. Full fine-tuning feasible on multi-GPU clusters; LoRA suitability requires review of expert layer structure and framework support (transformers, PEFT). Recommend consulting official repository or technical report for quantization and fine-tuning guidance.
When to avoid it — and what to weigh
- Requirement for Proprietary Model Guarantees — Community-maintained open-source model with Unknown support SLA. If production SLA and incident response are critical, consider commercial alternatives (GPT-4, Claude, Gemini).
- Visual or Multimodal Tasks — Model card specifies text-generation pipeline only. No evidence of vision, image generation, or audio capabilities. Use multimodal models if required.
- Highly Constrained Edge Devices — 560B parameters require significant VRAM even with MoE sparsity. Quantization and pruning strategies Unknown. If deployment target is mobile, embedded systems, or <8 GB VRAM environments, smaller models are necessary.
- Real-Time Compliance or Deterministic Output Guarantees — Deterministic computation mentioned for training reproducibility, not inference. Output consistency across runs and formal compliance audit trails are Unknown.
License & commercial use
Licensed under MIT, an OSI-approved permissive open-source license. Permits commercial use, modification, and distribution with attribution. No copyleft restrictions.
MIT license is permissive and explicitly allows commercial use. No gating, no model card restrictions stated. However: (1) Model is provided as-is with no warranty or support guarantee; (2) Deployment in production requires own infrastructure, testing, and compliance review (data privacy, bias, output guardrails); (3) No mention of SLA, liability limitations, or commercial indemnification in card. Recommend legal review for high-stakes use cases.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, red-teaming results, or adversarial robustness claims in card. Model trained on unknown data provenance. Custom code tag implies non-standard modules; code review recommended before production deployment. Standard LLM risks apply: prompt injection, hallucination, bias, token leakage. Meituan-controlled model; no independent security certification stated. For sensitive applications, conduct own security review and implement input/output filters.
Alternatives to consider
DeepSeek V3.1
Comparable 671B MoE model with 37B activation, strong benchmarks (ArenaHard-V2: 84.10 vs LongCat's 86.50, but DeepSeek leads AIME25: 49.27 vs 61.25). Consider if stricter performance on specific reasoning tasks is priority.
Qwen3 MoE-2507
Smaller footprint (235B, 22B activation) with stronger MMLU-Pro (84.83 vs 82.68) and MATH500 (98.80 vs 96.40). Better for hardware-constrained deployments.
Llama 3.2 (405B) or Mistral Large
Non-MoE dense alternatives with established production pipelines, better documentation, and commercial support options if full service SLA required.
Ship LongCat-Flash-Chat with senior software developers
LongCat-Flash-Chat offers competitive performance and low-latency inference for agentic workflows and reasoning tasks. Explore Devco's custom LLM app development and private deployment services to integrate this model into your production stack—with full data privacy and no third-party API dependency.
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LongCat-Flash-Chat FAQ
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Ready to Deploy an Efficient Open-Source LLM?
LongCat-Flash-Chat offers competitive performance and low-latency inference for agentic workflows and reasoning tasks. Explore Devco's custom LLM app development and private deployment services to integrate this model into your production stack—with full data privacy and no third-party API dependency.