DeepSeek VL2 Tiny vs MiniMax M2.1 Comparison
Comparing DeepSeek VL2 Tiny and MiniMax M2.1 across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek VL2 Tiny and MiniMax M2.1 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
MiniMax M2.1 has 227.0B more parameters than DeepSeek VL2 Tiny, making it 7566.7% larger.
Context Window
Maximum input and output token capacity
Only MiniMax M2.1 specifies input context (1,000,000 tokens). Only MiniMax M2.1 specifies output context (1,000,000 tokens).
Input Capabilities
Supported data types and modalities
DeepSeek VL2 Tiny supports multimodal inputs, whereas MiniMax M2.1 does not.
DeepSeek VL2 Tiny can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek VL2 Tiny
MiniMax M2.1
License
Usage and distribution terms
DeepSeek VL2 Tiny is licensed under deepseek, while MiniMax M2.1 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek VL2 Tiny was released on 2024-12-13, while MiniMax M2.1 was released on 2025-12-23.
MiniMax M2.1 is 13 months newer than DeepSeek VL2 Tiny.
Dec 13, 2024
1.3 years ago
Dec 23, 2025
2 months ago
1.0yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
DeepSeek VL2 Tiny
View detailsDeepSeek
MiniMax M2.1
View detailsMiniMax
Detailed Comparison
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