DeepSeek-V4-Pro-Max vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads the LLM Stats Score 49.9 to 43.0. MiMo-V2.6-Pro is 3.0x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.9 to 43.0, ranking #20 overall.
On price, MiMo-V2.6-Pro is roughly 3.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-Max
- you want predictable pricing at $1.30/M input and $2.60/M output
Choose MiMo-V2.6-Pro
- overall performance matters — it scores 49.9 and ranks #20 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 3.0x cheaper per token
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for DeepSeek-V4-Pro-Max · 18 for MiMo-V2.6-Pro
DeepSeek-V4-Pro-Max 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-V4-Pro-Max ($1.30/1M tokens) is 3.0x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V4-Pro-Max ($2.60/1M tokens) is 3.0x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-Max has 580.0B more parameters than MiMo-V2.6-Pro, making it 56.9% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Only DeepSeek-V4-Pro-Max specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Pro supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-Max
MiMo-V2.6-Pro
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-Max was released on 2026-04-23, while MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 5 months newer than DeepSeek-V4-Pro-Max.
Apr 23, 2026
5 months ago
Sep 22, 2026
1 weeks ago
5mo 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-Pro-Max is available from DeepInfra, Novita, DeepSeek, Fireworks, Together. MiMo-V2.6-Pro is available from Xiaomi.
DeepSeek-V4-Pro-Max
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-Max and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-Max vs MiMo-V2.6-Pro.