DeepSeek-V4.1-Flash vs Devstral Small 1.1
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 9.3. Devstral Small 1.1 is 2.2x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 9.3, ranking #13 overall.
On price, Devstral Small 1.1 is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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 51.8 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Devstral Small 1.1
- cost matters — it's about 2.2x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 1 for Devstral Small 1.1
DeepSeek-V4.1-Flash and Devstral Small 1.1don'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 ($0.22/1M tokens) is 2.2x more expensive than Devstral Small 1.1 ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 2.2x more expensive than Devstral Small 1.1 ($0.30/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Devstral Small 1.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 739.2B more parameters than Devstral Small 1.1, making it 3080.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Devstral Small 1.1's 128,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Devstral Small 1.1 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Devstral Small 1.1 does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Devstral Small 1.1
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Devstral Small 1.1 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Devstral Small 1.1 was released on 2025-07-11.
DeepSeek-V4.1-Flash is 14 months newer than Devstral Small 1.1.
Sep 10, 2026
1 weeks ago
1.2yr newerJul 11, 2025
1.2 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 is available from Fireworks, DeepInfra, DeepSeek, Novita. Devstral Small 1.1 is available from Mistral AI.
DeepSeek-V4.1-Flash
Devstral Small 1.1
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Devstral Small 1.1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Devstral Small 1.1.