Model Comparison
DeepSeek-V3.2-Exp vs Muse Spark 1.1Which is better in 2026?
Muse Spark 1.1 significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is 6.6x cheaper per token.
Verdict: DeepSeek-V3.2-Exp vs Muse Spark 1.1 — which is better?
DeepSeek-V3.2-Exp (by DeepSeek) and Muse Spark 1.1 (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2-Exp outperforms in 0 benchmarks, while Muse Spark 1.1 is better at 1 benchmark (Humanity's Last Exam). Muse Spark 1.1 significantly outperforms across most benchmarks.
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 1.1 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2-Exp if…
- cost matters — it's about 6.6x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.1 if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 0 benchmarks, while Muse Spark 1.1 is better at 1 benchmark (Humanity's Last Exam).
Muse Spark 1.1 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
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 1.1 ($1.25/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 10.4x cheaper than Muse Spark 1.1 ($4.25/1M tokens).
In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V3.2-Exp.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Spark 1.1 accepts 1,048,576 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. Muse Spark 1.1 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Muse Spark 1.1 supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.
Muse Spark 1.1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Exp
Muse Spark 1.1
License
Usage and distribution terms
DeepSeek-V3.2-Exp 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-V3.2-Exp was released on 2025-09-29, while Muse Spark 1.1 was released on 2026-07-09.
Muse Spark 1.1 is 9 months newer than DeepSeek-V3.2-Exp.
Sep 29, 2025
9 months ago
Jul 9, 2026
2 weeks ago
9mo 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 1.1 is available from Meta Model API.
DeepSeek-V3.2-Exp
Muse Spark 1.1
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and Muse Spark 1.1 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2-Exp vs Muse Spark 1.1.