DeepSeek-V4.1-Flash-1017 vs MiMo-V2.5-Pro
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 25.4. DeepSeek-V4.1-Flash-1017 is 1.0x cheaper per token.
DeepSeek · Xiaomi · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 25.4, ranking #15 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; 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-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Oct 2026
Choose MiMo-V2.5-Pro
- you want predictable pricing at $0.43/M input and $0.87/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 31 for MiMo-V2.5-Pro
DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (Humanity's Last Exam), while MiMo-V2.5-Pro is better at 0 benchmarks.
DeepSeek-V4.1-Flash-1017 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-1017 ($0.30/1M tokens) is 1.4x cheaper than MiMo-V2.5-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 1.4x more expensive than MiMo-V2.5-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2.5-Pro is more expensive than DeepSeek-V4.1-Flash-1017.*
* 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-1017, making it 34.1% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4.1-Flash-1017 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-1017 supports multimodal inputs, whereas MiMo-V2.5-Pro does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
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-1017 was released on 2026-10-17, while MiMo-V2.5-Pro was released on 2026-04-27.
DeepSeek-V4.1-Flash-1017 is 6 months newer than MiMo-V2.5-Pro.
Oct 17, 2026
2 days ago
5mo newerApr 27, 2026
5 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-1017 is available from DeepSeek. MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita.
DeepSeek-V4.1-Flash-1017
MiMo-V2.5-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and MiMo-V2.5-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs MiMo-V2.5-Pro.
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