DeepSeek-R1-0528 vs Muse Wonder
Muse Wonder leads the LLM Stats Score 47.9 to 24.1. DeepSeek-R1-0528 is 8.8x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to 24.1, ranking #29 overall.
On price, DeepSeek-R1-0528 is roughly 8.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Wonder also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- cost matters — it's about 8.8x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Muse Wonder
- overall performance matters — it scores 47.9 and ranks #29 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 16 for Muse Wonder
DeepSeek-R1-0528 and Muse Wonderdon'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
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 8.0x cheaper than Muse Wonder ($4.00/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 9.3x cheaper than Muse Wonder ($20.00/1M tokens).
In conclusion, Muse Wonder is more expensive than DeepSeek-R1-0528.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Wonder accepts 1,048,576 input tokens compared to DeepSeek-R1-0528's 163,840 tokens. Only DeepSeek-R1-0528 specifies output context (163,840 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Muse Wonder
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Muse Wonder uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while Muse Wonder was released on 2026-10-12.
Muse Wonder is 17 months newer than DeepSeek-R1-0528.
May 28, 2025
1.4 years ago
Oct 12, 2026
5 days ago
1.4yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Muse Wonder is available from Meta Model API.
DeepSeek-R1-0528
Muse Wonder
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Muse Wonder side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Muse Wonder.