DeepSeek-V4.1-Flash-1017 vs Muse Spark 1.2
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 36.8. DeepSeek-V4.1-Flash-1017 is 3.8x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 36.8, ranking #15 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4.1-Flash-1017 is roughly 3.8x 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-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 3.8x cheaper per token
- you want the most recent training data — it shipped Oct 2026
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.2
- you want predictable pricing at $1.25/M input and $4.25/M output
At a glance
The differences that matter most.
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 3 for Muse Spark 1.2
DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (DeepSWE 1.1), while Muse Spark 1.2 is better at 0 benchmarks.
DeepSeek-V4.1-Flash-1017 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-1017 ($0.30/1M tokens) is 4.2x cheaper than Muse Spark 1.2 ($1.25/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 3.5x cheaper than Muse Spark 1.2 ($4.25/1M tokens).
In conclusion, Muse Spark 1.2 is more expensive than DeepSeek-V4.1-Flash-1017.*
* 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-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Muse Spark 1.2 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and Muse Spark 1.2 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
Muse Spark 1.2
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Muse Spark 1.2 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-1017 was released on 2026-10-17, while Muse Spark 1.2 was released on 2026-08-05.
DeepSeek-V4.1-Flash-1017 is 2 months newer than Muse Spark 1.2.
Oct 17, 2026
0 days ago
2mo newerAug 5, 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-1017 is available from DeepSeek. Muse Spark 1.2 is available from Meta Model API.
DeepSeek-V4.1-Flash-1017
Muse Spark 1.2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Muse Spark 1.2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Muse Spark 1.2.
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