DeepSeek-V4-Pro-0813 vs Muse Spark 1.1
DeepSeek-V4-Pro-0813 and Muse Spark 1.1 are closely matched at 52.1 and 49.6 on the LLM Stats Score. DeepSeek-V4-Pro-0813 is 3.7x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Muse Spark 1.1 are closely matched on the overall LLM Stats Score at 52.1 and 49.6.
In the 3 individual benchmarks reported for both models, Muse Spark 1.1 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Pro-0813 is roughly 3.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- cost matters — it's about 3.7x cheaper per token
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.1
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for DeepSeek-V4-Pro-0813 · 11 for Muse Spark 1.1
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Terminal-Bench 2.1), while Muse Spark 1.1 is better at 2 benchmarks (Humanity's Last Exam, Toolathlon).
Muse Spark 1.1 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 2.9x cheaper than Muse Spark 1.1 ($1.25/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 4.9x cheaper than Muse Spark 1.1 ($4.25/1M tokens).
In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Muse Spark 1.1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark 1.1 supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Muse Spark 1.1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Muse Spark 1.1
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while Muse Spark 1.1 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-V4-Pro-0813 was released on 2026-08-13, while Muse Spark 1.1 was released on 2026-07-09.
DeepSeek-V4-Pro-0813 is 1 month newer than Muse Spark 1.1.
Aug 13, 2026
4 weeks ago
1mo newerJul 9, 2026
2 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Muse Spark 1.1 is available from Meta Model API.
DeepSeek-V4-Pro-0813
Muse Spark 1.1
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Muse Spark 1.1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Muse Spark 1.1.