Model Comparison
DeepSeek-V3.2 (Thinking) vs Muse Spark 1.1Which is better in 2026?
Muse Spark 1.1 significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 6.3x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs Muse Spark 1.1 — which is better?
DeepSeek-V3.2 (Thinking) (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 (Thinking) outperforms in 0 benchmarks, while Muse Spark 1.1 is better at 2 benchmarks (Humanity's Last Exam, Toolathlon). Muse Spark 1.1 significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2 (Thinking) is roughly 6.3x 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 (Thinking) if…
- cost matters — it's about 6.3x 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 2 of 2 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 (Thinking) outperforms in 0 benchmarks, while Muse Spark 1.1 is better at 2 benchmarks (Humanity's Last Exam, Toolathlon).
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 (Thinking) ($0.28/1M tokens) is 4.5x cheaper than Muse Spark 1.1 ($1.25/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 10.1x cheaper than Muse Spark 1.1 ($4.25/1M tokens).
In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V3.2 (Thinking).*
* 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 (Thinking)'s 131,072 tokens. Muse Spark 1.1 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Muse Spark 1.1 supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) 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 (Thinking)
Muse Spark 1.1
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) 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 (Thinking) was released on 2025-12-01, while Muse Spark 1.1 was released on 2026-07-09.
Muse Spark 1.1 is 7 months newer than DeepSeek-V3.2 (Thinking).
Dec 1, 2025
7 months ago
Jul 9, 2026
1 weeks ago
7mo 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 (Thinking) is available from DeepSeek. Muse Spark 1.1 is available from Meta Model API.
DeepSeek-V3.2 (Thinking)
Muse Spark 1.1
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) 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 (Thinking) vs Muse Spark 1.1.