DeepSeek-V3.2-Exp vs Muse Spark Open
Muse Spark Open leads the LLM Stats Score 45.3 to 27.7. DeepSeek-V3.2-Exp is 6.6x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
Muse Spark Open leads the overall LLM Stats Score 45.3 to 27.7, ranking #42 overall.
In the 1 individual benchmarks reported for both models, Muse Spark Open wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3.2-Exp is roughly 6.6x 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-V3.2-Exp
- cost matters — it's about 6.6x cheaper per token
Choose Muse Spark Open
- overall performance matters — it scores 45.3 and ranks #42 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- 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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek-V3.2-Exp · 17 for Muse Spark Open
DeepSeek-V3.2-Exp outperforms in 0 benchmarks, while Muse Spark Open is better at 1 benchmark (GPQA).
Muse Spark Open 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-V3.2-Exp ($0.27/1M tokens) is 4.6x cheaper than Muse Spark Open ($1.25/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 10.4x cheaper than Muse Spark Open ($4.25/1M tokens).
In conclusion, Muse Spark Open is more expensive than DeepSeek-V3.2-Exp.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Muse Spark Open has 131.0B more parameters than DeepSeek-V3.2-Exp, making it 19.1% larger.
Context Window
Maximum input and output token capacity
Muse Spark Open accepts 1,048,576 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. Muse Spark Open can generate longer responses up to 1,048,576 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark Open supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.
Muse Spark Open can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Exp
Muse Spark Open
License
Usage and distribution terms
DeepSeek-V3.2-Exp 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-V3.2-Exp was released on 2025-09-29, while Muse Spark Open was released on 2026-10-15.
Muse Spark Open is 13 months newer than DeepSeek-V3.2-Exp.
Sep 29, 2025
1.1 years ago
Oct 15, 2026
5 days ago
1.0yr 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-V3.2-Exp is available from Novita. Muse Spark Open is available from Together.
DeepSeek-V3.2-Exp
Muse Spark Open
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and Muse Spark Open side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Exp vs Muse Spark Open.