DeepSeek-V4-Flash-0731 vs Muse Spark 1.3
Muse Spark 1.3 leads the LLM Stats Score 54.3 to 44.7. DeepSeek-V4-Flash-0731 is 22.2x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
Muse Spark 1.3 leads the overall LLM Stats Score 54.3 to 44.7, ranking #6 overall.
In the 2 individual benchmarks reported for both models, Muse Spark 1.3 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0731 is roughly 22.2x 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-Flash-0731
- cost matters — it's about 22.2x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.3
- overall performance matters — it scores 54.3 and ranks #6 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 11 for Muse Spark 1.3
DeepSeek-V4-Flash-0731 outperforms in 0 benchmarks, while Muse Spark 1.3 is better at 2 benchmarks (AutomationBench, Terminal-Bench 2.1).
Muse Spark 1.3 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-Flash-0731 ($0.06/1M tokens) is 20.8x cheaper than Muse Spark 1.3 ($1.25/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 23.6x cheaper than Muse Spark 1.3 ($4.25/1M tokens).
In conclusion, Muse Spark 1.3 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. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while Muse Spark 1.3 is limited to 943,718 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark 1.3 supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Muse Spark 1.3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Muse Spark 1.3
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Muse Spark 1.3 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 1.3 was released on 2026-09-02.
Muse Spark 1.3 is 1 month newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
1 months ago
Sep 2, 2026
2 weeks ago
1mo 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-Flash-0731 is available from DeepInfra, Novita, Fireworks. Muse Spark 1.3 is available from Meta Model API.
DeepSeek-V4-Flash-0731
Muse Spark 1.3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Muse Spark 1.3.