DeepSeek-R1 vs Muse Spark Open
Comparing DeepSeek-R1 and Muse Spark Open across benchmarks, pricing, and capabilities.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-R1 and Muse Spark Open trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-R1 is roughly 2.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark Open 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
- cost matters — it's about 2.1x cheaper per token
Choose Muse Spark Open
- 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
0 reported for DeepSeek-R1 · 17 for Muse Spark Open
DeepSeek-R1 and Muse Spark Opendon'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 ($0.55/1M tokens) is 2.3x cheaper than Muse Spark Open ($1.25/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 1.9x cheaper than Muse Spark Open ($4.25/1M tokens).
In conclusion, Muse Spark Open is more expensive than DeepSeek-R1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Muse Spark Open has 145.0B more parameters than DeepSeek-R1, making it 21.6% larger.
Context Window
Maximum input and output token capacity
Muse Spark Open accepts 1,048,576 input tokens compared to DeepSeek-R1's 131,072 tokens. Muse Spark Open can generate longer responses up to 1,048,576 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark Open supports multimodal inputs, whereas DeepSeek-R1 does not.
Muse Spark Open can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Muse Spark Open
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Muse Spark Open uses Muse Model License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Muse Model License
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Muse Spark Open was released on 2026-10-15.
Muse Spark Open is 21 months newer than DeepSeek-R1.
Jan 20, 2025
1.7 years ago
Oct 15, 2026
2 days ago
1.7yr 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 is available from DeepSeek, DeepInfra, Together, Fireworks. Muse Spark Open is available from Together.
DeepSeek-R1
Muse Spark Open
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Muse Spark Open side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Muse Spark Open.