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Kimi-k1.5 vs Muse Wonder

Muse Wonder leads the LLM Stats Score 47.9 to 17.1.

Moonshot AI · Meta · Updated for 2026

Which is better?

Muse Wonder leads the overall LLM Stats Score 47.9 to 17.1, ranking #29 overall.

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

Choose Kimi-k1.5

  • you are already invested in the Moonshot AI ecosystem

Choose Muse Wonder

  • overall performance matters — it scores 47.9 and ranks #29 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

Core performance indexes
17.1
#234
47.9
#29
16.4
#231
33.1
#108
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$4.00 / M
Output price
— / M
$20.00 / M
Context window
—
1,048,576

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
Kimi-k1.5
Muse Wonder
11.0#123
25.4#52
14.6#105
25.4#52
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for Kimi-k1.5 · 16 for Muse Wonder

No common benchmarks found

Kimi-k1.5 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 Muse Wonder specifies input context (1,048,576 tokens).

Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Meta
Muse Wonder
Input1,048,576 tokens
Output- tokens
Tue Oct 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Kimi-k1.5 and Muse Wonder support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Kimi-k1.5

Text
Images
Audio
Video

Muse Wonder

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Kimi-k1.5

Proprietary

Closed source

Muse Wonder

Proprietary

Closed source

Release Timeline

When each model was launched

Kimi-k1.5 was released on 2025-01-20, while Muse Wonder was released on 2026-10-12.

Muse Wonder is 21 months newer than Kimi-k1.5.

Kimi-k1.5

Jan 20, 2025

1.7 years ago

Muse Wonder

Oct 12, 2026

1 days ago

1.7yr 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 Kimi-k1.5 and Muse Wonder side-by-side, then vote on the output you prefer.

Kimi-k1.5
✓ Preferred
Muse Wonder
Open in Playground

FAQ

Common questions about Kimi-k1.5 vs Muse Wonder.

Which is better, Kimi-k1.5 or Muse Wonder?

Muse Wonder leads the LLM Stats Score 47.9 to 17.1. Kimi-k1.5 is made by Moonshot AI 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 Kimi-k1.5 compare to Muse Wonder in benchmarks?

Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%. 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 Kimi-k1.5 and Muse Wonder?

Kimi-k1.5 supports an unknown number of tokens and Muse Wonder supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi-k1.5 and Muse Wonder?

Key differences include LLM Stats Score (17.1 vs 47.9). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi-k1.5 and Muse Wonder?

Kimi-k1.5 is developed by Moonshot AI and Muse Wonder is developed by Meta.