Model Comparison
DeepSeek-V4-Pro-Max vs MiMo-V2-OmniWhich is better in 2026?
Both models are evenly matched across the benchmarks. MiMo-V2-Omni is 2.5x cheaper per token.
Verdict: DeepSeek-V4-Pro-Max vs MiMo-V2-Omni — which is better?
DeepSeek-V4-Pro-Max (by DeepSeek) and MiMo-V2-Omni (by Xiaomi) 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-Pro-Max outperforms in 1 benchmarks (SWE-Bench Verified), while MiMo-V2-Omni is better at 1 benchmark (GDPval-AA). Both models are evenly matched across the benchmarks.
On price, MiMo-V2-Omni is roughly 2.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-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-Pro-Max if…
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Apr 2026
- you need open weights you can self-host or fine-tune
Choose MiMo-V2-Omni if…
- cost matters — it's about 2.5x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-Max outperforms in 1 benchmarks (SWE-Bench Verified), while MiMo-V2-Omni is better at 1 benchmark (GDPval-AA).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-Max ($1.60/1M tokens) is 4.0x more expensive than MiMo-V2-Omni ($0.40/1M tokens).
For output processing, DeepSeek-V4-Pro-Max ($3.20/1M tokens) is 1.6x more expensive than MiMo-V2-Omni ($2.00/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than MiMo-V2-Omni.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-Max accepts 1,048,576 input tokens compared to MiMo-V2-Omni's 262,000 tokens. DeepSeek-V4-Pro-Max can generate longer responses up to 131,072 tokens, while MiMo-V2-Omni is limited to 16,384 tokens.
Input Capabilities
Supported data types and modalities
MiMo-V2-Omni supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.
MiMo-V2-Omni can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-Max
MiMo-V2-Omni
License
Usage and distribution terms
DeepSeek-V4-Pro-Max is licensed under MIT, while MiMo-V2-Omni 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-Pro-Max was released on 2026-04-23, while MiMo-V2-Omni was released on 2026-03-18.
DeepSeek-V4-Pro-Max is 1 month newer than MiMo-V2-Omni.
Apr 23, 2026
2 months ago
1mo newerMar 18, 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-Pro-Max is available from Novita, DeepInfra, DeepSeek, Fireworks, Together. MiMo-V2-Omni is available from Xiaomi.
DeepSeek-V4-Pro-Max
MiMo-V2-Omni
Outputs Comparison
Key Takeaways
DeepSeek-V4-Pro-Max
View detailsDeepSeek
MiMo-V2-Omni
View detailsXiaomi
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-Max and MiMo-V2-Omni side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Pro-Max vs MiMo-V2-Omni.