DeepSeek-V4-Pro-0813 vs MiMo-V2.6-Pro
DeepSeek-V4-Pro-0813 and MiMo-V2.6-Pro are closely matched at 51.1 and 50.0 on the LLM Stats Score. MiMo-V2.6-Pro is 3.0x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and MiMo-V2.6-Pro are closely matched on the overall LLM Stats Score at 51.1 and 50.0.
In the 3 individual benchmarks reported for both models, MiMo-V2.6-Pro wins 3; this is a narrower head-to-head signal than the composite indexes.
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-0813
- you want predictable pricing at $1.30/M input and $2.60/M output
Choose MiMo-V2.6-Pro
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- 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
12 reported for DeepSeek-V4-Pro-0813 · 18 for MiMo-V2.6-Pro
DeepSeek-V4-Pro-0813 outperforms in 0 benchmarks, while MiMo-V2.6-Pro is better at 3 benchmarks (Agents' Last Exam, CyberGym, Terminal-Bench 2.1).
MiMo-V2.6-Pro significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($1.30/1M tokens) is 3.0x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($2.60/1M tokens) is 3.0x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 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-0813 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-0813 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-0813 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-0813
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-0813 was released on 2026-08-13, while MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 1 month newer than DeepSeek-V4-Pro-0813.
Aug 13, 2026
1 months ago
Sep 22, 2026
0 days ago
1mo 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-0813 is available from DeepInfra, DeepSeek, Novita, Together. MiMo-V2.6-Pro is available from Xiaomi.
DeepSeek-V4-Pro-0813
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs MiMo-V2.6-Pro.