DeepSeek-V4.1-Flash-1017 vs Mistral Small
Comparing DeepSeek-V4.1-Flash-1017 and Mistral Small across benchmarks, pricing, and capabilities.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 and Mistral Small trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Mistral Small is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 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-1017
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Mistral Small
- cost matters — it's about 1.7x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 0 for Mistral Small
DeepSeek-V4.1-Flash-1017 and Mistral Smalldon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 1.5x more expensive than Mistral Small ($0.20/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.0x more expensive than Mistral Small ($0.60/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Mistral Small.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 741.2B more parameters than Mistral Small, making it 3369.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Mistral Small's 32,768 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Mistral Small is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Mistral Small does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
Mistral Small
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Mistral Small uses Mistral Research License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Mistral Research License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Mistral Small was released on 2024-09-17.
DeepSeek-V4.1-Flash-1017 is 25 months newer than Mistral Small.
Oct 17, 2026
2 days ago
2.1yr newerSep 17, 2024
2.1 years 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. Mistral Small is available from Mistral AI.
DeepSeek-V4.1-Flash-1017
Mistral Small
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Mistral Small side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Mistral Small.
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