DeepSeek-V4-Flash-0423 vs MiMo-V2.5-Pro
DeepSeek-V4-Flash-0423 leads the LLM Stats Score 36.1 to 25.7. DeepSeek-V4-Flash-0423 is 4.8x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0423 leads the overall LLM Stats Score 36.1 to 25.7, ranking #87 overall.
The models split the 6 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4-Flash-0423 is roughly 4.8x 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-Flash-0423
- overall performance matters — it scores 36.1 and ranks #87 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 4.8x cheaper per token
Choose MiMo-V2.5-Pro
- your work emphasizes agents — it leads those capability indexes
- you want the most recent training data — it shipped Apr 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for DeepSeek-V4-Flash-0423 · 31 for MiMo-V2.5-Pro
DeepSeek-V4-Flash-0423 outperforms in 3 benchmarks (GPQA, Humanity's Last Exam, MMLU-Pro), while MiMo-V2.5-Pro is better at 3 benchmarks (SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0423 ($0.09/1M tokens) is 4.8x cheaper than MiMo-V2.5-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V4-Flash-0423 ($0.18/1M tokens) is 4.8x cheaper than MiMo-V2.5-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2.5-Pro is more expensive than DeepSeek-V4-Flash-0423.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.5-Pro has 739.2B more parameters than DeepSeek-V4-Flash-0423, making it 260.3% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4-Flash-0423 can generate longer responses up to 1,048,576 tokens, while MiMo-V2.5-Pro is limited to 131,072 tokens.
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-0423 was released on 2026-04-23, while MiMo-V2.5-Pro was released on 2026-04-27.
MiMo-V2.5-Pro is 0 month newer than DeepSeek-V4-Flash-0423.
Apr 23, 2026
5 months ago
Apr 27, 2026
4 months ago
4d 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-Flash-0423 is available from DeepInfra, Novita. MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita.
DeepSeek-V4-Flash-0423
MiMo-V2.5-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0423 and MiMo-V2.5-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0423 vs MiMo-V2.5-Pro.