DeepSeek-V2.5 vs MiMo-V2.6-Flash
MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 8.1. DeepSeek-V2.5 and MiMo-V2.6-Flash cost the same.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 8.1, ranking #29 overall.
MiMo-V2.6-Flash 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
- you want predictable pricing at $0.14/M input and $0.28/M output
Choose MiMo-V2.6-Flash
- overall performance matters — it scores 45.6 and ranks #29 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 · 16 for MiMo-V2.6-Flash
DeepSeek-V2.5 and MiMo-V2.6-Flashdon'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) costs the same as MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) costs the same as MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, DeepSeek-V2.5 and MiMo-V2.6-Flash cost the same.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Flash has 73.0B more parameters than DeepSeek-V2.5, making it 30.9% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash 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-Flash supports multimodal inputs, whereas DeepSeek-V2.5 does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
MiMo-V2.6-Flash
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while MiMo-V2.6-Flash 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-Flash was released on 2026-09-22.
MiMo-V2.6-Flash 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-Flash is available from Xiaomi.
DeepSeek-V2.5
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs MiMo-V2.6-Flash.