Model Comparison
DeepSeek-V3.2-Speciale vs MiMo-V2.5-ProWhich is better in 2026?
MiMo-V2.5-Pro significantly outperforms across most benchmarks. DeepSeek-V3.2-Speciale is 1.7x cheaper per token.
Verdict: DeepSeek-V3.2-Speciale vs MiMo-V2.5-Pro — which is better?
DeepSeek-V3.2-Speciale (by DeepSeek) and MiMo-V2.5-Pro (by Xiaomi) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2-Speciale outperforms in 0 benchmarks, while MiMo-V2.5-Pro is better at 3 benchmarks (Humanity's Last Exam, SWE-Bench Verified, Terminal-Bench 2.0). MiMo-V2.5-Pro significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2-Speciale is roughly 1.7x 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.
Choose DeepSeek-V3.2-Speciale if…
- cost matters — it's about 1.7x cheaper per token
Choose MiMo-V2.5-Pro if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Apr 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Speciale outperforms in 0 benchmarks, while MiMo-V2.5-Pro is better at 3 benchmarks (Humanity's Last Exam, SWE-Bench Verified, Terminal-Bench 2.0).
MiMo-V2.5-Pro significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 1.6x cheaper than MiMo-V2.5-Pro ($0.43/1M tokens).
For output processing, DeepSeek-V3.2-Speciale ($0.42/1M tokens) is 2.1x cheaper than MiMo-V2.5-Pro ($0.87/1M tokens).
In conclusion, MiMo-V2.5-Pro is more expensive than DeepSeek-V3.2-Speciale.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.5-Pro has 338.2B more parameters than DeepSeek-V3.2-Speciale, making it 49.4% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5-Pro accepts 1,048,576 input tokens compared to DeepSeek-V3.2-Speciale's 131,072 tokens. Both models can generate responses up 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-V3.2-Speciale was released on 2025-12-01, while MiMo-V2.5-Pro was released on 2026-04-27.
MiMo-V2.5-Pro is 5 months newer than DeepSeek-V3.2-Speciale.
Dec 1, 2025
7 months ago
Apr 27, 2026
2 months ago
4mo 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-V3.2-Speciale is available from DeepSeek. MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita.
DeepSeek-V3.2-Speciale
MiMo-V2.5-Pro
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Speciale and MiMo-V2.5-Pro side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2-Speciale vs MiMo-V2.5-Pro.