MiMo-V2-Flash vs Qwen2.5-Omni-7B Comparison
Comparing MiMo-V2-Flash and Qwen2.5-Omni-7B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
MiMo-V2-Flash outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Qwen2.5-Omni-7B is better at 0 benchmarks.
MiMo-V2-Flash significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
MiMo-V2-Flash has 302.0B more parameters than Qwen2.5-Omni-7B, making it 4314.3% larger.
Context Window
Maximum input and output token capacity
Only MiMo-V2-Flash specifies input context (256,000 tokens). Only MiMo-V2-Flash specifies output context (16,384 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5-Omni-7B supports multimodal inputs, whereas MiMo-V2-Flash does not.
Qwen2.5-Omni-7B can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiMo-V2-Flash
Qwen2.5-Omni-7B
License
Usage and distribution terms
MiMo-V2-Flash is licensed under MIT, while Qwen2.5-Omni-7B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
MiMo-V2-Flash was released on 2025-12-16, while Qwen2.5-Omni-7B was released on 2025-03-27.
MiMo-V2-Flash is 9 months newer than Qwen2.5-Omni-7B.
Dec 16, 2025
2 months ago
8mo newerMar 27, 2025
11 months ago
Knowledge 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
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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