DeepSeek-V4.1-Flash vs Muse Spark 1.1
DeepSeek-V4.1-Flash and Muse Spark 1.1 are closely matched at 51.8 and 49.7 on the LLM Stats Score. DeepSeek-V4.1-Flash is 6.1x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash and Muse Spark 1.1 are closely matched on the overall LLM Stats Score at 51.8 and 49.7.
In the 4 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4.1-Flash is roughly 6.1x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash
- your work emphasizes coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 4 exact shared results
- cost matters — it's about 6.1x cheaper per token
- 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 1.1
- 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 1.1
DeepSeek-V4.1-Flash outperforms in 3 benchmarks (BabyVision, DeepSWE 1.1, Terminal-Bench 2.1), while Muse Spark 1.1 is better at 1 benchmark (Humanity's Last Exam).
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 5.7x cheaper than Muse Spark 1.1 ($1.25/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 6.4x cheaper than Muse Spark 1.1 ($4.25/1M tokens).
In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V4.1-Flash.*
* 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-V4.1-Flash's 1,040,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Muse Spark 1.1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and Muse Spark 1.1 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Muse Spark 1.1
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while Muse Spark 1.1 was released on 2026-07-09.
DeepSeek-V4.1-Flash is 2 months newer than Muse Spark 1.1.
Sep 10, 2026
5 days ago
2mo newerJul 9, 2026
2 months 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 1.1 is available from Meta Model API.
DeepSeek-V4.1-Flash
Muse Spark 1.1
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Muse Spark 1.1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Muse Spark 1.1.