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LongCat-Flash-Thinking-2601 vs Muse Wonder

Muse Wonder leads the LLM Stats Score 49.2 to 35.2.

Meituan · Meta · Updated for 2026

Which is better?

Muse Wonder leads the overall LLM Stats Score 49.2 to 35.2, ranking #25 overall.

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

Choose LongCat-Flash-Thinking-2601

  • 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 coding and agents — 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-2601
Meituan
Muse Wonder
Meta
Core performance indexes
LLM Stats Score
35.2
#105
49.2
#25
Reasoning
35.3
#99
37.7
#81
Coding
18.7
#127
38.2
#21
Agents
9.4
#144
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-2601
Muse Wonder
Tool use
24.3#41
19.1#75
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for LongCat-Flash-Thinking-2601 · 16 for Muse Wonder

No common benchmarks found

LongCat-Flash-Thinking-2601 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-2601 specifies input context (128,000 tokens). Only LongCat-Flash-Thinking-2601 specifies output context (128,000 tokens).

Meituan
LongCat-Flash-Thinking-2601
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-2601 does not.

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

LongCat-Flash-Thinking-2601

Text
Images
Audio
Video

Muse Wonder

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Thinking-2601 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-2601

MIT

Open weights

Muse Wonder

Proprietary

Closed source

Release Timeline

When each model was launched

LongCat-Flash-Thinking-2601 was released on 2026-01-14, while Muse Wonder was released on 2026-10-12.

Muse Wonder is 9 months newer than LongCat-Flash-Thinking-2601.

LongCat-Flash-Thinking-2601

Jan 14, 2026

9 months ago

Muse Wonder

Oct 12, 2026

1 weeks ago

9mo 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-2601 and Muse Wonder side-by-side, then vote on the output you prefer.

LongCat-Flash-Thinking-2601
✓ Preferred
Muse Wonder
Open in Playground

FAQ

Common questions about LongCat-Flash-Thinking-2601 vs Muse Wonder.

Which is better, LongCat-Flash-Thinking-2601 or Muse Wonder?

Muse Wonder leads the LLM Stats Score 49.2 to 35.2. LongCat-Flash-Thinking-2601 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-2601 compare to Muse Wonder in benchmarks?

LongCat-Flash-Thinking-2601 scores AIME 2025: 99.6%, Tau2 Telecom: 99.3%, Tau2 Retail: 88.6%, LiveCodeBench: 82.8%, GPQA: 80.5%. 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-2601 and Muse Wonder?

LongCat-Flash-Thinking-2601 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-2601 and Muse Wonder?

Key differences include LLM Stats Score (35.2 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-2601 and Muse Wonder?

LongCat-Flash-Thinking-2601 is developed by Meituan and Muse Wonder is developed by Meta.

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