LongCat-Flash-Lite-Sparse is a non-thinking Mixture-of-Experts (MoE) model with 69B total parameters and approximately 3B activated parameters per token. Built on LongCat-Flash-Lite, it replaces dense MLA with LongCat Sparse Attention (LSA) and natively supports context lengths of up to 1M tokens. Compared with its dense predecessor, LongCat-Flash-Lite-Sparse improves long-context inference efficiency and agentic capabilities while preserving strong reasoning and general-knowledge performance.
Key Features
🌟 LongCat Sparse Attention (LSA)
LSA is a hardware-efficient sparse-attention framework that extends DeepSeek Sparse Attention (DSA) with three complementary indexing mechanisms:
Streaming-Aware Indexing (SI) reallocates part of the token-selection budget to a fixed sink and local sliding window while retaining dynamic sparse selection for the remaining tokens. This design converts fragmented KV access into more predictable, contiguous reads, improving HBM access efficiency and effective bandwidth.
Cross-Layer Indexing (CLI) exploits the stability of attention saliency across adjacent layers, allowing multiple consecutive layers to reuse the results of a single indexing pass at inference time. This capability is learned through cross-layer distillation during training.
Hierarchical Indexing (HI) adopts a coarse-to-fine, two-stage scoring scheme: it first recalls candidate blocks using coarse-grained scores and then performs fine-grained token selection within those blocks. By reducing the candidate space processed for each query, HI lowers indexing overhead and can be enabled at inference time without additional training.
Together, these mechanisms enable LongCat-Flash-Lite-Sparse to process long contexts substantially more efficiently without compromising model quality.
🌟 Native 1M-Token Context Support
To strengthen long-context capabilities, we update the long-context extension stage of LongCat-Flash-Lite with an enriched corpus and a progressive schedule that scales to 1,024K, enabling native support for context lengths up to 1M.
🌟 Strong Agentic Performance
LongCat-Flash-Lite-Sparse delivers stronger performance than its dense predecessor on agentic coding, search, and tool-use tasks.
Note: Lite-Dense denotes the LongCat-Flash-Lite, with metrics sourced from its technical report. Lite-Sparse (w/o HI) and Lite-Sparse (w/ HI) denote the sparse variants evaluated without and with Hierarchical Indexing, respectively.
Long-Context Benchmarks
Capability Dimension
Benchmark
Lite-Sparse (w/o HI)
Lite-Sparse (w/ HI)
Foundational
Retrieval
MRCR (8-needle)
44.66
44.47
Aggregation
OOLong-Synth
38.42
37.88
Multi-step Reasoning
GraphWalks Extend
66.27
65.63
Application
Question Answering
LOFT Retrieval Extend
43.75
44.38
In-context Learning
HELMET-ICL Extend
91.63
90.50
Code Understanding
LongCodeQA
62.30
59.37
Long-range Memory
AMemBench-ACU
33.25
33.13
Holistic Assessment
LongBench-v2
52.50
53.64
Holistic Assessment
AA-LCR
48.00
47.33
Deployment
We have implemented basic adaptations in SGLang (PR) to support the deployment of LongCat-Flash-Lite-Sparse.
LongCat-Flash-Lite-Sparse can be served on a single node (e.g., 1xH20-141G).
This repository, including both the model weights and the source code, is released under the MIT License.
Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.
This model has not been specifically designed or comprehensively evaluated for every possible downstream application.
Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios.
It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.
Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
