DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash
DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash are closely matched at 44.3 and 45.6 on the LLM Stats Score.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash are closely matched on the overall LLM Stats Score at 44.3 and 45.6.
In the 3 individual benchmarks reported for both models, MiMo-V2.6-Flash wins 3; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- you want predictable pricing at $0.06/M input and $0.18/M output
Choose MiMo-V2.6-Flash
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- 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
9 reported for DeepSeek-V4-Flash-0731 · 16 for MiMo-V2.6-Flash
DeepSeek-V4-Flash-0731 outperforms in 0 benchmarks, while MiMo-V2.6-Flash is better at 3 benchmarks (Agents' Last Exam, CyberGym, Terminal-Bench 2.1).
MiMo-V2.6-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
MiMo-V2.6-Flash has 5.0B more parameters than DeepSeek-V4-Flash-0731, making it 1.6% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4-Flash-0731 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0731 specifies output context (1,048,576 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Flash supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
MiMo-V2.6-Flash
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-Flash-0731 was released on 2026-07-31, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 2 months newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
1 months ago
Sep 22, 2026
-1 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.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash.