DeepSeek-V4.1-Flash vs Muse Spark 1.3
DeepSeek-V4.1-Flash and Muse Spark 1.3 are closely matched at 51.8 and 54.4 on the LLM Stats Score. Muse Spark 1.3 is 2.6x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash and Muse Spark 1.3 are closely matched on the overall LLM Stats Score at 51.8 and 54.4.
In the 3 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Muse Spark 1.3 is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark 1.3 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-V4.1-Flash
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you want the most recent training data — it shipped Sep 2026
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.3
- cost matters — it's about 2.6x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 11 for Muse Spark 1.3
DeepSeek-V4.1-Flash outperforms in 2 benchmarks (AutomationBench, Terminal-Bench 2.1), while Muse Spark 1.3 is better at 1 benchmark (DeepSWE 1.1).
DeepSeek-V4.1-Flash shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 2.2x more expensive than Muse Spark 1.3 ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 3.3x more expensive than Muse Spark 1.3 ($0.20/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Muse Spark 1.3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Spark 1.3 accepts 1,048,576 input tokens compared to DeepSeek-V4.1-Flash's 1,040,000 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while DeepSeek-V4.1-Flash is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and Muse Spark 1.3 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Muse Spark 1.3
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while Muse Spark 1.3 was released on 2026-09-02.
DeepSeek-V4.1-Flash is 0 month newer than Muse Spark 1.3.
Sep 10, 2026
-1 days ago
1w newerSep 2, 2026
1 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-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Muse Spark 1.3 is available from Meta Model API.
DeepSeek-V4.1-Flash
Muse Spark 1.3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Muse Spark 1.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Muse Spark 1.3.