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LongCat-Flash-Thinking vs Muse Wonder

Muse Wonder leads the LLM Stats Score 49.2 to 28.5.

Meituan · Meta · Updated for 2026

Which is better?

Muse Wonder leads the overall LLM Stats Score 49.2 to 28.5, ranking #25 overall.

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

Choose LongCat-Flash-Thinking

  • you need open weights you can self-host or fine-tune

Choose Muse Wonder

  • overall performance matters — it scores 49.2 and ranks #25 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

LongCat-Flash-Thinking
Meituan
Muse Wonder
Meta
Core performance indexes
LLM Stats Score
28.5
#155
49.2
#25
Reasoning
28.6
#148
37.7
#81
Coding
13.7
#156
38.2
#21
Agents
12.9
#116
37.2
#17
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
128,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
LongCat-Flash-Thinking
Muse Wonder
Tool use
15.7#100
19.1#75
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for LongCat-Flash-Thinking · 16 for Muse Wonder

No common benchmarks found

LongCat-Flash-Thinking 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

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Thinking specifies input context (128,000 tokens). Only LongCat-Flash-Thinking specifies output context (128,000 tokens).

Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Meta
Muse Wonder
Input- tokens
Output- tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Wonder supports multimodal inputs, whereas LongCat-Flash-Thinking does not.

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

LongCat-Flash-Thinking

Text
Images
Audio
Video

Muse Wonder

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Thinking 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.

LongCat-Flash-Thinking

MIT

Open weights

Muse Wonder

Proprietary

Closed source

Release Timeline

When each model was launched

LongCat-Flash-Thinking was released on 2025-09-22, while Muse Wonder was released on 2026-10-12.

Muse Wonder is 13 months newer than LongCat-Flash-Thinking.

LongCat-Flash-Thinking

Sep 22, 2025

1.1 years ago

Muse Wonder

Oct 12, 2026

1 weeks ago

1.1yr 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

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

LongCat-Flash-Thinking
✓ Preferred
Muse Wonder
Open in Playground

FAQ

Common questions about LongCat-Flash-Thinking vs Muse Wonder.

Which is better, LongCat-Flash-Thinking or Muse Wonder?

Muse Wonder leads the LLM Stats Score 49.2 to 28.5. LongCat-Flash-Thinking is made by Meituan and Muse Wonder is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does LongCat-Flash-Thinking compare to Muse Wonder in benchmarks?

LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%. Muse Wonder scores MemoryBench: 85.6%, DeepSWE 1.1: 78.8%, OSWorld 2.1 (partial): 72.6%, JobBench: 70.5%, HealthBench Professional: 60.5%.

What are the context window sizes for LongCat-Flash-Thinking and Muse Wonder?

LongCat-Flash-Thinking supports 128K tokens and Muse Wonder supports an unknown number of 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-Thinking and Muse Wonder?

Key differences include LLM Stats Score (28.5 vs 49.2), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes LongCat-Flash-Thinking and Muse Wonder?

LongCat-Flash-Thinking is developed by Meituan and Muse Wonder is developed by Meta.

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