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EXAONE 4.5 33B vs Muse Wonder Full

Muse Wonder Full leads the LLM Stats Score 52.6 to 25.5.

LG AI Research · Meta · Updated for 2026

Which is better?

Muse Wonder Full leads the overall LLM Stats Score 52.6 to 25.5, ranking #12 overall.

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

Choose EXAONE 4.5 33B

  • you are already invested in the LG AI Research ecosystem

Choose Muse Wonder Full

  • overall performance matters — it scores 52.6 and ranks #12 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.

EXAONE 4.5 33B
LG AI Research
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
25.5
#177
52.6
#12
Reasoning
26.1
#172
41.2
#62
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
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Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
EXAONE 4.5 33B
Muse Wonder Full
Vision
13.3#114
31.4#31
Tool use
9.8#147
24.7#39
Multimodal
16.1#100
31.4#31
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for EXAONE 4.5 33B · 16 for Muse Wonder Full

No common benchmarks found

EXAONE 4.5 33B and Muse Wonder Fulldon'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

Input capabilities

Documented input modalities across available providers

Both EXAONE 4.5 33B and Muse Wonder Full support multimodal inputs.

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

EXAONE 4.5 33B

Text
Images
Audio
Video

Muse Wonder Full

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.

EXAONE 4.5 33B

Proprietary

Closed source

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

EXAONE 4.5 33B was released on 2026-04-09, while Muse Wonder Full was released on 2026-10-12.

Muse Wonder Full is 6 months newer than EXAONE 4.5 33B.

EXAONE 4.5 33B

Apr 9, 2026

6 months ago

Muse Wonder Full

Oct 12, 2026

1 weeks ago

6mo newer

Knowledge Cutoff

When training data ends

EXAONE 4.5 33B has a documented knowledge cutoff of 2024-12-01, while Muse Wonder Full's cutoff date is not specified.

We can confirm EXAONE 4.5 33B's training data extends to 2024-12-01, but cannot make a direct comparison without Muse Wonder Full's cutoff date.

EXAONE 4.5 33B

Dec 2024

Muse Wonder Full

—

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against EXAONE 4.5 33B and Muse Wonder Full side-by-side, then vote on the output you prefer.

EXAONE 4.5 33B
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about EXAONE 4.5 33B vs Muse Wonder Full.

Which is better, EXAONE 4.5 33B or Muse Wonder Full?

Muse Wonder Full leads the LLM Stats Score 52.6 to 25.5. EXAONE 4.5 33B is made by LG AI Research 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 EXAONE 4.5 33B compare to Muse Wonder Full in benchmarks?

EXAONE 4.5 33B scores AIME 2025: 92.9%, AIME 2026: 92.6%, IFEval: 89.6%, AI2D: 89.0%, MathVista-Mini: 85.0%. 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 main differences between EXAONE 4.5 33B and Muse Wonder Full?

Key differences include LLM Stats Score (25.5 vs 52.6). See the full comparison above for benchmark-by-benchmark results.

Who makes EXAONE 4.5 33B and Muse Wonder Full?

EXAONE 4.5 33B is developed by LG AI Research and Muse Wonder Full is developed by Meta.

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