Model Comparison
DeepSeek-V4-Flash-Max vs Mistral Medium 3.5Which is better in 2026?
DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks. DeepSeek-V4-Flash-Max is 24.0x cheaper per token.
Verdict: DeepSeek-V4-Flash-Max vs Mistral Medium 3.5 — which is better?
DeepSeek-V4-Flash-Max (by DeepSeek) and Mistral Medium 3.5 (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V4-Flash-Max outperforms in 3 benchmarks (BrowseComp, GDPval-AA, SWE-Bench Verified), while Mistral Medium 3.5 is better at 0 benchmarks. DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Flash-Max is roughly 24.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-Max also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-Max if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- cost matters — it's about 24.0x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose Mistral Medium 3.5 if…
- you want the most recent training data — it shipped Apr 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-Max outperforms in 3 benchmarks (BrowseComp, GDPval-AA, SWE-Bench Verified), while Mistral Medium 3.5 is better at 0 benchmarks.
DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-Max ($0.10/1M tokens) is 15.0x cheaper than Mistral Medium 3.5 ($1.50/1M tokens).
For output processing, DeepSeek-V4-Flash-Max ($0.20/1M tokens) is 37.5x cheaper than Mistral Medium 3.5 ($7.50/1M tokens).
In conclusion, Mistral Medium 3.5 is more expensive than DeepSeek-V4-Flash-Max.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-Max has 156.0B more parameters than Mistral Medium 3.5, making it 121.9% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-Max accepts 1,048,576 input tokens compared to Mistral Medium 3.5's 256,000 tokens. Mistral Medium 3.5 can generate longer responses up to 256,000 tokens, while DeepSeek-V4-Flash-Max is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Mistral Medium 3.5 supports multimodal inputs, whereas DeepSeek-V4-Flash-Max does not.
Mistral Medium 3.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-Max
Mistral Medium 3.5
License
Usage and distribution terms
DeepSeek-V4-Flash-Max 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-Flash-Max was released on 2026-04-23, while Mistral Medium 3.5 was released on 2026-04-29.
Mistral Medium 3.5 is 0 month newer than DeepSeek-V4-Flash-Max.
Apr 23, 2026
2 months ago
Apr 29, 2026
2 months ago
6d 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-Flash-Max is available from DeepInfra, DeepSeek, Fireworks, Novita. Mistral Medium 3.5 is available from Mistral AI.
DeepSeek-V4-Flash-Max
Mistral Medium 3.5
Outputs Comparison
Key Takeaways
Mistral Medium 3.5
View detailsMistral AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-Max and Mistral Medium 3.5 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-Max vs Mistral Medium 3.5.