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DeepSeek-R1 vs Muse Wonder Full

Comparing DeepSeek-R1 and Muse Wonder Full across benchmarks, pricing, and capabilities.

DeepSeek · Meta · Updated for 2026

Which is better?

DeepSeek-R1 and Muse Wonder Full trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

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

Choose DeepSeek-R1

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

Choose Muse Wonder Full

  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

DeepSeek-R1
DeepSeek
Muse Wonder Full
Meta
Benchmark wins
—
—
Input price
$0.55 / M
— / M
Output price
$2.19 / M
— / M
Context window
131,072
—

Individual benchmarks

0 reported for DeepSeek-R1 · 16 for Muse Wonder Full

No common benchmarks found

DeepSeek-R1 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-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Meta
Muse Wonder Full
Input- tokens
Output- tokens
Tue Oct 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Wonder Full supports multimodal inputs, whereas DeepSeek-R1 does not.

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

DeepSeek-R1

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 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

MIT

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-R1 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.

DeepSeek-R1

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

DeepSeek-R1
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about DeepSeek-R1 vs Muse Wonder Full.

Which is better, DeepSeek-R1 or Muse Wonder Full?

DeepSeek-R1 (DeepSeek) and Muse Wonder Full (Meta) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1 compare to Muse Wonder Full in benchmarks?

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-R1 and Muse Wonder Full?

DeepSeek-R1 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-R1 and Muse Wonder Full?

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

Who makes DeepSeek-R1 and Muse Wonder Full?

DeepSeek-R1 is developed by DeepSeek and Muse Wonder Full is developed by Meta.

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