DeepSeek-V4.1-Flash vs MiMo-V2.5-Pro
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 24.7. DeepSeek-V4.1-Flash is 1.6x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 24.7, ranking #12 overall.
In the 2 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4.1-Flash is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.5-Pro 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-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 1.6x cheaper per token
- you want the most recent training data — it shipped Sep 2026
Choose MiMo-V2.5-Pro
- you process long inputs — it offers a 1,048,576 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 31 for MiMo-V2.5-Pro
DeepSeek-V4.1-Flash outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while MiMo-V2.5-Pro is better at 0 benchmarks.
DeepSeek-V4.1-Flash 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-V4.1-Flash ($0.22/1M tokens) is 2.0x cheaper than MiMo-V2.5-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.3x cheaper than MiMo-V2.5-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2.5-Pro is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.5-Pro has 260.0B more parameters than DeepSeek-V4.1-Flash, making it 34.1% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5-Pro accepts 1,048,576 input tokens compared to DeepSeek-V4.1-Flash's 1,040,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while MiMo-V2.5-Pro is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas MiMo-V2.5-Pro does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
MiMo-V2.5-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-V4.1-Flash was released on 2026-09-10, while MiMo-V2.5-Pro was released on 2026-04-27.
DeepSeek-V4.1-Flash is 5 months newer than MiMo-V2.5-Pro.
Sep 10, 2026
4 days ago
4mo newerApr 27, 2026
4 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita.
DeepSeek-V4.1-Flash
MiMo-V2.5-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and MiMo-V2.5-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs MiMo-V2.5-Pro.