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GLM-5.3-Flash vs Muse Wonder Full

GLM-5.3-Flash and Muse Wonder Full are closely matched at 49.1 and 52.1 on the LLM Stats Score.

Zhipu AI · Meta · Updated for 2026

Which is better?

GLM-5.3-Flash and Muse Wonder Full are closely matched on the overall LLM Stats Score at 49.1 and 52.1.

The models split the 2 individual benchmarks reported for both models evenly.

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

Choose GLM-5.3-Flash

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

Choose Muse Wonder Full

  • your work emphasizes 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.

GLM-5.3-Flash
Zhipu AI
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
49.1
#24
52.1
#13
Reasoning
48.1
#22
38.4
#71
Coding
32.1
#48
41.5
#8
Agents
35.0
#20
38.5
#8
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
GLM-5.3-Flash
Muse Wonder Full
Vision
30.9#33
31.7#28
Multimodal
30.5#33
31.8#25
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GLM-5.3-Flash · 16 for Muse Wonder Full

2 shared

GLM-5.3-Flash outperforms in 1 benchmarks (Artificial Analysis), while Muse Wonder Full is better at 1 benchmark (DeepSWE 1.1).

Both models are evenly matched across the benchmarks.

Sun Oct 18 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (1,048,576 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output1,048,576 tokens
Meta
Muse Wonder Full
Input- tokens
Output- tokens
Sun Oct 18 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both GLM-5.3-Flash and Muse Wonder Full support multimodal inputs.

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

GLM-5.3-Flash

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Muse Wonder Full uses a proprietary license.

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

GLM-5.3-Flash

MIT

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Muse Wonder Full was released on 2026-10-12.

Muse Wonder Full is 2 months newer than GLM-5.3-Flash.

GLM-5.3-Flash

Aug 26, 2026

1 months ago

Muse Wonder Full

Oct 12, 2026

5 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 GLM-5.3-Flash and Muse Wonder Full side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Muse Wonder Full.

Which is better, GLM-5.3-Flash or Muse Wonder Full?

GLM-5.3-Flash and Muse Wonder Full are closely matched on the LLM Stats Score at 49.1 and 52.1. GLM-5.3-Flash is made by Zhipu AI 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 GLM-5.3-Flash compare to Muse Wonder Full in benchmarks?

GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, Chartography: 78.0%. 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 context window sizes for GLM-5.3-Flash and Muse Wonder Full?

GLM-5.3-Flash supports 1.0M tokens and Muse Wonder Full 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 GLM-5.3-Flash and Muse Wonder Full?

Key differences include LLM Stats Score (49.1 vs 52.1), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Muse Wonder Full?

GLM-5.3-Flash is developed by Zhipu AI and Muse Wonder Full is developed by Meta.

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