DeepSeek-V2.5 vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads the LLM Stats Score 49.8 to 8.1. DeepSeek-V2.5 is 3.1x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.8 to 8.1, ranking #19 overall.
On price, DeepSeek-V2.5 is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Pro 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-V2.5
- cost matters — it's about 3.1x cheaper per token
Choose MiMo-V2.6-Pro
- overall performance matters — it scores 49.8 and ranks #19 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Individual benchmarks
15 reported for DeepSeek-V2.5 · 18 for MiMo-V2.6-Pro
DeepSeek-V2.5 and MiMo-V2.6-Prodon'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-V2.5 ($0.14/1M tokens) is 3.1x cheaper than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 3.1x cheaper than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2.6-Pro is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 784.0B more parameters than DeepSeek-V2.5, making it 332.2% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Only DeepSeek-V2.5 specifies output context (8,192 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Pro supports multimodal inputs, whereas DeepSeek-V2.5 does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
MiMo-V2.6-Pro
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while MiMo-V2.6-Pro uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 29 months newer than DeepSeek-V2.5.
May 8, 2024
2.4 years ago
Sep 22, 2026
0 days ago
2.4yr 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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. MiMo-V2.6-Pro is available from Xiaomi.
DeepSeek-V2.5
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs MiMo-V2.6-Pro.