AI model comparison

LongCat Flash Lite vs LongCat Flash Lite Sparse

Compare pricing, context limits, supported inputs and deployment options to choose a model for your workflow.

Flash Lite and Lite Sparse are non-thinking LongCat checkpoints with large n-gram embeddings and sparse experts. Lite uses the documented 256K YaRN route; Lite Sparse changes attention and extends native long-context support.

Side-by-sideCapabilities and practical differences

Meituan LongCat

LongCat Flash Lite

LongCat Flash Lite is a non-thinking sparse language checkpoint with an n-gram embedding table for coding and agent workloads.

Context
262.14K

Accepts

  • Text input

Produces

  • Text output
VS

Meituan LongCat

LongCat Flash Lite Sparse

LongCat Flash Lite Sparse adds sparse attention and native long-context support to the non-thinking LongCat Lite family.

Context
983.04K

Accepts

  • Text input

Produces

  • Text output

At a glance

Key differences

Compare the available figures below, then try both models on the work you need to do.

LongCat Flash Lite Sparse

  • Context capacity: 983.04K

Context capacity

Higher listed limit

LongCat Flash Lite:262.14K

LongCat Flash Lite Sparse:983.04K

LongCat Flash Lite Sparse

Specifications

Pricing, limits and capabilities

Specifications for LongCat Flash Lite and LongCat Flash Lite Sparse. Higher limits and lower prices describe these figures only.
SpecificationLongCat Flash LiteLongCat Flash Lite Sparse
ProviderMeituan LongCatMeituan LongCat
Listed pricing routeMeituan LongCatMeituan LongCat
Model family——
Accepted inputstexttext
Produced outputstexttext
Context window262.14K983.04KHigher
Reasoning modeNot documentedNot documented
Tool callingNot documentedNot documented
Image inputNot documentedNot documented
Audio inputNot documentedNot documented
Structured outputNot documentedNot documented
LicenseMITMIT

Workload guidance

Which model should you choose?

Use ordinary Lite as a baseline for shorter direct-response tasks. Evaluate Sparse for long histories that depend on widely separated evidence. Keep runtime versions and parser settings consistent, then measure prefill, complete task time and memory at the intended concurrency.

Documentation and model profiles

Explore each model’s features, API requirements and licence terms in the sources below.

Questions people ask

It brings together documented inputs and outputs, token limits, deployment terms and available pricing. When both models have source-linked, verified results on a shared benchmark, those results are shown with their individual sources. The comparison is a starting point for evaluating your own workload.

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