DeepSeek-V4-Pro-0813 vs Muse Spark Open
DeepSeek-V4-Pro-0813 leads the LLM Stats Score 50.3 to 44.9. DeepSeek-V4-Pro-0813 is 1.2x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 leads the overall LLM Stats Score 50.3 to 44.9, ranking #18 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4-Pro-0813 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Pro-0813 is roughly 1.2x 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
- overall performance matters — it scores 50.3 and ranks #18 on LLM Stats
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.2x cheaper per token
Choose Muse Spark Open
- you want the most recent training data — it shipped Oct 2026
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 · 17 for Muse Spark Open
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Terminal-Bench 2.1), while Muse Spark Open is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 significantly outperforms across most 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 ($1.30/1M tokens) is 1.0x more expensive than Muse Spark Open ($1.25/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($2.60/1M tokens) is 1.6x cheaper than Muse Spark Open ($4.25/1M tokens).
In conclusion, Muse Spark Open is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 784.0B more parameters than Muse Spark Open, making it 96.1% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 1,048,576 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark Open supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Muse Spark Open can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Muse Spark Open
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 was released on 2026-08-13, while Muse Spark Open was released on 2026-10-15.
Muse Spark Open is 2 months newer than DeepSeek-V4-Pro-0813.
Aug 13, 2026
2 months ago
Oct 15, 2026
2 days ago
2mo 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-V4-Pro-0813 is available from DeepInfra, DeepSeek, Novita, Together. Muse Spark Open is available from Together.
DeepSeek-V4-Pro-0813
Muse Spark Open
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Muse Spark Open side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Muse Spark Open.