DeepSeek-V4.1-Flash vs Muse Spark Open
DeepSeek-V4.1-Flash leads the LLM Stats Score 49.8 to 44.9. DeepSeek-V4.1-Flash is 6.1x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 49.8 to 44.9, ranking #20 overall.
In the 6 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 5; 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 Open 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
- overall performance matters — it scores 49.8 and ranks #20 on LLM Stats
- your work emphasizes agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 6 exact shared results
- cost matters — it's about 6.1x cheaper per token
Choose Muse Spark Open
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
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 · 17 for Muse Spark Open
DeepSeek-V4.1-Flash outperforms in 5 benchmarks (BabyVision, DeepSWE 1.1, GPQA, Humanity's Last Exam (with tools), Terminal-Bench 2.1), while Muse Spark Open is better at 1 benchmark (ZEROBench).
DeepSeek-V4.1-Flash 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-V4.1-Flash ($0.22/1M tokens) is 5.7x cheaper than Muse Spark Open ($1.25/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 6.4x cheaper than Muse Spark Open ($4.25/1M tokens).
In conclusion, Muse Spark Open is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Muse Spark Open has 52.8B more parameters than DeepSeek-V4.1-Flash, making it 6.9% larger.
Context Window
Maximum input and output token capacity
Muse Spark Open accepts 1,048,576 input tokens compared to DeepSeek-V4.1-Flash's 1,040,000 tokens. Muse Spark Open can generate longer responses up to 1,048,576 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 Open support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Muse Spark Open
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Muse Spark Open uses Muse Model License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Muse Model License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Muse Spark Open was released on 2026-10-15.
Muse Spark Open is 1 month newer than DeepSeek-V4.1-Flash.
Sep 10, 2026
1 months ago
Oct 15, 2026
5 days 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-V4.1-Flash is available from Fireworks, DeepInfra, Novita. Muse Spark Open is available from Together.
DeepSeek-V4.1-Flash
Muse Spark Open
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Muse Spark Open side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Muse Spark Open.