MiMo-V2.5 Model Documentation and Integration Guide
Blog post from Deepinfra
XiaomiMiMo's MiMo-V2.5 is an advanced omnimodal AI model designed to process and understand diverse data types such as text, image, video, and audio through a unified architecture leveraging a 310-billion-parameter Sparse Mixture of Experts framework, activating only 15 billion parameters during inference. This model offers a substantial context window of 1 million tokens, supporting complex multimodal perception and autonomous workflows, while integrating native encoders for various data types to enhance cohesion. MiMo-V2.5 showcases significant improvements over its predecessor, particularly in reasoning and computational efficiency, by employing a hybrid attention architecture and multi-token prediction modules that enhance inference speed and reinforcement learning efficacy. Hosted on DeepInfra, it provides high-performance, low-latency inference via an API compatible with OpenAI, making it a versatile choice for developers aiming to implement agentic workflows and process extensive document sets. Pricing is usage-based, with options for standard and priority tiers to optimize cost and processing speed, making the model accessible for professional-grade deployments.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 3 | 7,655 | 1,347 | 245 | +22% |
| Reinforcement learning | 2 | 98 | 52 | 31 | +23% |
| Vector Search | 2 | 2,241 | 449 | 143 | +17% |
| AI Agents | 1 | 6,829 | 1,441 | 261 | +10% |
| AI Model Fine-tuning | 1 | 975 | 221 | 80 | +28% |
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