DeepSeek-R1 vs Muse Spark 1.3
Comparing DeepSeek-R1 and Muse Spark 1.3 across benchmarks, pricing, and capabilities.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-R1 and Muse Spark 1.3 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Muse Spark 1.3 is roughly 7.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark 1.3 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
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.3
- cost matters — it's about 7.7x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 11 for Muse Spark 1.3
DeepSeek-R1 and Muse Spark 1.3don'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 5.5x more expensive than Muse Spark 1.3 ($0.10/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 10.9x more expensive than Muse Spark 1.3 ($0.20/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Muse Spark 1.3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Spark 1.3 accepts 1,048,576 input tokens compared to DeepSeek-R1's 131,072 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark 1.3 supports multimodal inputs, whereas DeepSeek-R1 does not.
Muse Spark 1.3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Muse Spark 1.3
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Muse Spark 1.3 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 was released on 2025-01-20, while Muse Spark 1.3 was released on 2026-09-02.
Muse Spark 1.3 is 20 months newer than DeepSeek-R1.
Jan 20, 2025
1.6 years ago
Sep 2, 2026
0 days ago
1.6yr 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 1.3 is available from Meta Model API.
DeepSeek-R1
Muse Spark 1.3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Muse Spark 1.3.