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Hy4 preview vs Muse Wonder Full

Hy4 preview and Muse Wonder Full are closely matched at 49.9 and 52.1 on the LLM Stats Score.

Tencent · Meta · Updated for 2026

Which is better?

Hy4 preview and Muse Wonder Full are closely matched on the overall LLM Stats Score at 49.9 and 52.1.

In the 2 individual benchmarks reported for both models, Muse Wonder Full wins 2; this is a narrower head-to-head signal than the composite indexes.

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

Choose Hy4 preview

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

Choose Muse Wonder Full

  • your work emphasizes agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

Hy4 preview
Tencent
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
49.9
#20
52.1
#13
Reasoning
49.9
#14
38.4
#71
Coding
37.8
#21
41.5
#8
Agents
34.9
#22
38.5
#8
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
—

Individual benchmarks

32 reported for Hy4 preview · 16 for Muse Wonder Full

2 shared

Hy4 preview outperforms in 0 benchmarks, while Muse Wonder Full is better at 2 benchmarks (Program Bench, SWE-Marathon).

Muse Wonder Full significantly outperforms across most benchmarks.

Sat Oct 17 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Input capabilities

Documented input modalities across available providers

Muse Wonder Full supports multimodal inputs, whereas Hy4 preview does not.

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

Hy4 preview

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

Hy4 preview is licensed under Apache 2.0, while Muse Wonder Full uses a proprietary license.

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

Hy4 preview

Apache 2.0

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

Hy4 preview was released on 2026-08-28, while Muse Wonder Full was released on 2026-10-12.

Muse Wonder Full is 2 months newer than Hy4 preview.

Hy4 preview

Aug 28, 2026

1 months ago

Muse Wonder Full

Oct 12, 2026

4 days ago

1mo 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 Hy4 preview and Muse Wonder Full side-by-side, then vote on the output you prefer.

Hy4 preview
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about Hy4 preview vs Muse Wonder Full.

Which is better, Hy4 preview or Muse Wonder Full?

Hy4 preview and Muse Wonder Full are closely matched on the LLM Stats Score at 49.9 and 52.1. Hy4 preview is made by Tencent and Muse Wonder Full is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Hy4 preview compare to Muse Wonder Full in benchmarks?

Hy4 preview scores GPQA: 92.3%, Terminal-Bench 2.1: 85.4%, WideSearch: 83.9%, MCP Atlas: 83.7%, SWE-bench Multilingual: 82.9%. Muse Wonder Full scores MemoryBench: 85.5%, DeepSWE 1.1: 79.0%, OSWorld 2.1 (partial): 78.4%, JobBench: 75.3%, HealthBench Professional: 62.3%.

What are the main differences between Hy4 preview and Muse Wonder Full?

Key differences include LLM Stats Score (49.9 vs 52.1), multimodal support (no vs yes), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Hy4 preview and Muse Wonder Full?

Hy4 preview is developed by Tencent and Muse Wonder Full is developed by Meta.

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