LongCat-Flash-Lite vs Muse Wonder
Muse Wonder leads the LLM Stats Score 47.9 to 18.6. LongCat-Flash-Lite is 45.7x cheaper per token.
Meituan · Meta · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to 18.6, ranking #29 overall.
On price, LongCat-Flash-Lite is roughly 45.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Wonder also accepts a larger context window (1,048,576 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
- cost matters — it's about 45.7x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Muse Wonder
- overall performance matters — it scores 47.9 and ranks #29 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
13 reported for LongCat-Flash-Lite · 16 for Muse Wonder
LongCat-Flash-Lite and Muse Wonderdon'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
Pricing Analysis
Price comparison per million tokens
For input processing, LongCat-Flash-Lite ($0.10/1M tokens) is 40.0x cheaper than Muse Wonder ($4.00/1M tokens).
For output processing, LongCat-Flash-Lite ($0.40/1M tokens) is 50.0x cheaper than Muse Wonder ($20.00/1M tokens).
In conclusion, Muse Wonder is more expensive than LongCat-Flash-Lite.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Wonder accepts 1,048,576 input tokens compared to LongCat-Flash-Lite's 256,000 tokens. Only LongCat-Flash-Lite specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder supports multimodal inputs, whereas LongCat-Flash-Lite does not.
Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.
LongCat-Flash-Lite
Muse Wonder
License
Usage and distribution terms
LongCat-Flash-Lite is licensed under MIT, while Muse Wonder uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
LongCat-Flash-Lite was released on 2026-02-05, while Muse Wonder was released on 2026-10-12.
Muse Wonder is 8 months newer than LongCat-Flash-Lite.
Feb 5, 2026
8 months ago
Oct 12, 2026
0 days ago
8mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
LongCat-Flash-Lite is available from Meituan. Muse Wonder is available from Meta Model API.
LongCat-Flash-Lite
Muse Wonder
Outputs Comparison
Judge for yourself.
Run your own prompts against LongCat-Flash-Lite and Muse Wonder side-by-side, then vote on the output you prefer.
FAQ
Common questions about LongCat-Flash-Lite vs Muse Wonder.