DeepSeek-V4.1-Flash vs Mistral Medium 3.5
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 26.9. DeepSeek-V4.1-Flash is 9.1x 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 26.9, ranking #12 overall.
On price, DeepSeek-V4.1-Flash is roughly 9.1x 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 #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 9.1x cheaper per token
- 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 Mistral Medium 3.5
- you want predictable pricing at $1.50/M input and $7.50/M output
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 Mistral Medium 3.5
DeepSeek-V4.1-Flash and Mistral Medium 3.5don'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 6.8x cheaper than Mistral Medium 3.5 ($1.50/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 11.4x cheaper than Mistral Medium 3.5 ($7.50/1M tokens).
In conclusion, Mistral Medium 3.5 is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 635.2B more parameters than Mistral Medium 3.5, making it 496.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Mistral Medium 3.5's 256,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Mistral Medium 3.5 is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and Mistral Medium 3.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Mistral Medium 3.5
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Mistral Medium 3.5 uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Mistral Medium 3.5 was released on 2026-04-29.
DeepSeek-V4.1-Flash is 4 months newer than Mistral Medium 3.5.
Sep 10, 2026
-1 days ago
4mo newerApr 29, 2026
4 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. Mistral Medium 3.5 is available from Mistral AI.
DeepSeek-V4.1-Flash
Mistral Medium 3.5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Mistral Medium 3.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Mistral Medium 3.5.