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LongCat-Flash-Lite vs Parse

Comparing LongCat-Flash-Lite and Parse across benchmarks, pricing, and capabilities.

Meituan · Cohere · Updated for 2026

Which is better?

LongCat-Flash-Lite and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

LongCat-Flash-Lite also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose LongCat-Flash-Lite

  • you process long inputs — it offers a 256,000 token context window
  • you need open weights you can self-host or fine-tune

Choose Parse

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
Input price
$0.10 / M
— / M
Output price
$0.40 / M
— / M
Context window
256,000
8,192

Individual benchmarks

13 reported for LongCat-Flash-Lite · 1 for Parse

No common benchmarks found

LongCat-Flash-Lite and Parsedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

66.2B diff

LongCat-Flash-Lite has 66.2B more parameters than Parse, making it 2878.3% larger.

Meituan
LongCat-Flash-Lite
68.5Bparameters
Cohere
Parse
2.3Bparameters
68.5B
LongCat-Flash-Lite
2.3B
Parse

Context Window

Maximum input and output token capacity

LongCat-Flash-Lite accepts 256,000 input tokens compared to Parse's 8,192 tokens. Only LongCat-Flash-Lite specifies output context (128,000 tokens).

Meituan
LongCat-Flash-Lite
Input256,000 tokens
Output128,000 tokens
Cohere
Parse
Input8,192 tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Parse supports multimodal inputs, whereas LongCat-Flash-Lite does not.

Parse can handle both text and other forms of data like images, making it suitable for multimodal applications.

LongCat-Flash-Lite

Text
Images
Audio
Video

Parse

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Lite is licensed under MIT, while Parse uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

LongCat-Flash-Lite

MIT

Open weights

Parse

Proprietary

Closed source

Release Timeline

When each model was launched

LongCat-Flash-Lite was released on 2026-02-05, while Parse was released on 2026-08-27.

Parse is 7 months newer than LongCat-Flash-Lite.

LongCat-Flash-Lite

Feb 5, 2026

6 months ago

Parse

Aug 27, 2026

4 days ago

6mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

LongCat-Flash-Lite is available from Meituan. Parse is available from Azure, Cohere.

LongCat-Flash-Lite

meituan logo
Meituan
Input Price:Input: $0.10/1MOutput Price:Output: $0.40/1M

Parse

azure logo
Azure
cohere logo
Cohere
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against LongCat-Flash-Lite and Parse side-by-side, then vote on the output you prefer.

LongCat-Flash-Lite
✓ Preferred
Parse
Open in Playground

FAQ

Common questions about LongCat-Flash-Lite vs Parse.

Which is better, LongCat-Flash-Lite or Parse?

LongCat-Flash-Lite (Meituan) and Parse (Cohere) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does LongCat-Flash-Lite compare to Parse in benchmarks?

LongCat-Flash-Lite scores MATH-500: 96.8%, MMLU: 85.5%, CMMLU: 82.5%, MMLU-Pro: 78.3%, Tau2 Retail: 73.1%. Parse scores ParseBench: 79.2%.

What are the context window sizes for LongCat-Flash-Lite and Parse?

LongCat-Flash-Lite supports 256K tokens and Parse supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between LongCat-Flash-Lite and Parse?

Key differences include context window (256K vs 8K), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes LongCat-Flash-Lite and Parse?

LongCat-Flash-Lite is developed by Meituan and Parse is developed by Cohere.