DeepSeek-V4.1-Flash-1017 vs MiMo-V2.5
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 35.9. MiMo-V2.5 is 2.5x 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 35.9, ranking #15 overall.
On price, MiMo-V2.5 is roughly 2.5x 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-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you want the most recent training data — it shipped Oct 2026
Choose MiMo-V2.5
- cost matters — it's about 2.5x cheaper per token
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 · 14 for MiMo-V2.5
DeepSeek-V4.1-Flash-1017 and MiMo-V2.5don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 1.8x more expensive than MiMo-V2.5 ($0.17/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 3.6x more expensive than MiMo-V2.5 ($0.34/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 452.4B more parameters than MiMo-V2.5, making it 145.6% 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 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and MiMo-V2.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
MiMo-V2.5
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 was released on 2026-04-22.
DeepSeek-V4.1-Flash-1017 is 6 months newer than MiMo-V2.5.
Oct 17, 2026
2 days ago
5mo newerApr 22, 2026
6 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 is available from Novita, DeepInfra.
DeepSeek-V4.1-Flash-1017
MiMo-V2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and MiMo-V2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs MiMo-V2.5.
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