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DeepSeek-V3.2-Speciale vs Muse Wonder Full

Muse Wonder Full leads the LLM Stats Score 52.6 to 33.5.

DeepSeek · Meta · Updated for 2026

Which is better?

Muse Wonder Full leads the overall LLM Stats Score 52.6 to 33.5, ranking #12 overall.

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

Choose DeepSeek-V3.2-Speciale

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

Choose Muse Wonder Full

  • overall performance matters — it scores 52.6 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and 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.

DeepSeek-V3.2-Speciale
DeepSeek
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
33.5
#118
52.6
#12
Reasoning
32.1
#125
41.2
#62
Coding
18.5
#131
41.4
#9
Agents
10.8
#136
40.6
#6
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.28 / M
— / M
Output price
$0.42 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V3.2-Speciale
Muse Wonder Full
Tool use
10.2#141
24.7#39
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

8 reported for DeepSeek-V3.2-Speciale · 16 for Muse Wonder Full

No common benchmarks found

DeepSeek-V3.2-Speciale 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

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Speciale specifies input context (131,072 tokens). Only DeepSeek-V3.2-Speciale specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Meta
Muse Wonder Full
Input- tokens
Output- tokens
Fri Oct 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Wonder Full supports multimodal inputs, whereas DeepSeek-V3.2-Speciale does not.

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

DeepSeek-V3.2-Speciale

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2-Speciale 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.

DeepSeek-V3.2-Speciale

MIT

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2-Speciale was released on 2025-12-01, while Muse Wonder Full was released on 2026-10-12.

Muse Wonder Full is 11 months newer than DeepSeek-V3.2-Speciale.

DeepSeek-V3.2-Speciale

Dec 1, 2025

10 months ago

Muse Wonder Full

Oct 12, 2026

1 weeks ago

10mo 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 DeepSeek-V3.2-Speciale and Muse Wonder Full side-by-side, then vote on the output you prefer.

DeepSeek-V3.2-Speciale
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about DeepSeek-V3.2-Speciale vs Muse Wonder Full.

Which is better, DeepSeek-V3.2-Speciale or Muse Wonder Full?

Muse Wonder Full leads the LLM Stats Score 52.6 to 33.5. DeepSeek-V3.2-Speciale is made by DeepSeek 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 DeepSeek-V3.2-Speciale compare to Muse Wonder Full in benchmarks?

DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%. 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 DeepSeek-V3.2-Speciale and Muse Wonder Full?

DeepSeek-V3.2-Speciale supports 131K 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 DeepSeek-V3.2-Speciale and Muse Wonder Full?

Key differences include LLM Stats Score (33.5 vs 52.6), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2-Speciale and Muse Wonder Full?

DeepSeek-V3.2-Speciale is developed by DeepSeek and Muse Wonder Full is developed by Meta.

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