Model Comparison
DeepSeek-V4-Flash-0731 vs Muse Spark 1.1Which is better in 2026?
Both models are evenly matched across the benchmarks. DeepSeek-V4-Flash-0731 is 17.8x cheaper per token.
Verdict: DeepSeek-V4-Flash-0731 vs Muse Spark 1.1 — which is better?
DeepSeek-V4-Flash-0731 (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-V4-Flash-0731 outperforms in 1 benchmarks (Terminal-Bench 2.1), while Muse Spark 1.1 is better at 1 benchmark (Toolathlon). Both models are evenly matched across the benchmarks.
On price, DeepSeek-V4-Flash-0731 is roughly 17.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose DeepSeek-V4-Flash-0731 if…
- cost matters — it's about 17.8x cheaper per token
- you want the most recent training data — it shipped Jul 2026
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.1 if…
- you want predictable pricing at $1.25/M input and $4.25/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 outperforms in 1 benchmarks (Terminal-Bench 2.1), while Muse Spark 1.1 is better at 1 benchmark (Toolathlon).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 13.9x cheaper than Muse Spark 1.1 ($1.25/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 23.6x cheaper than Muse Spark 1.1 ($4.25/1M tokens).
In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V4-Flash-0731.*
* 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. Muse Spark 1.1 can generate longer responses up to 131,072 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Muse Spark 1.1 supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 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-Flash-0731
Muse Spark 1.1
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 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-Flash-0731 was released on 2026-07-31, while Muse Spark 1.1 was released on 2026-07-09.
DeepSeek-V4-Flash-0731 is 1 month newer than Muse Spark 1.1.
Jul 31, 2026
3 days ago
3w newerJul 9, 2026
3 weeks 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-Flash-0731 is available from DeepInfra, Fireworks, Novita. Muse Spark 1.1 is available from Meta Model API.
DeepSeek-V4-Flash-0731
Muse Spark 1.1
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Muse Spark 1.1 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Muse Spark 1.1.