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DeepSeek R1 Distill Qwen 14B vs Muse Wonder Full

Muse Wonder Full leads the LLM Stats Score 52.1 to 10.9.

DeepSeek · Meta · Updated for 2026

Which is better?

Muse Wonder Full leads the overall LLM Stats Score 52.1 to 10.9, ranking #13 overall.

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

Choose DeepSeek R1 Distill Qwen 14B

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

Choose Muse Wonder Full

  • overall performance matters — it scores 52.1 and ranks #13 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 R1 Distill Qwen 14B
DeepSeek
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
10.9
#276
52.1
#13
Reasoning
11.2
#272
38.4
#71
Coding
5.4
#212
41.5
#8
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
—

Individual benchmarks

4 reported for DeepSeek R1 Distill Qwen 14B · 16 for Muse Wonder Full

No common benchmarks found

DeepSeek R1 Distill Qwen 14B 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

Muse Wonder Full supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 14B does not.

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

DeepSeek R1 Distill Qwen 14B

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 14B 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 R1 Distill Qwen 14B

MIT

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 14B was released on 2025-01-20, while Muse Wonder Full was released on 2026-10-12.

Muse Wonder Full is 21 months newer than DeepSeek R1 Distill Qwen 14B.

DeepSeek R1 Distill Qwen 14B

Jan 20, 2025

1.7 years ago

Muse Wonder Full

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 DeepSeek R1 Distill Qwen 14B and Muse Wonder Full side-by-side, then vote on the output you prefer.

DeepSeek R1 Distill Qwen 14B
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about DeepSeek R1 Distill Qwen 14B vs Muse Wonder Full.

Which is better, DeepSeek R1 Distill Qwen 14B or Muse Wonder Full?

Muse Wonder Full leads the LLM Stats Score 52.1 to 10.9. DeepSeek R1 Distill Qwen 14B 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 R1 Distill Qwen 14B compare to Muse Wonder Full in benchmarks?

DeepSeek R1 Distill Qwen 14B scores MATH-500: 93.9%, AIME 2024: 80.0%, GPQA: 59.1%, LiveCodeBench: 53.1%. 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 main differences between DeepSeek R1 Distill Qwen 14B and Muse Wonder Full?

Key differences include LLM Stats Score (10.9 vs 52.1), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek R1 Distill Qwen 14B and Muse Wonder Full?

DeepSeek R1 Distill Qwen 14B is developed by DeepSeek and Muse Wonder Full is developed by Meta.

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