omnimodal

Xiaomi's MiMo team released MiMo-V2.6-Flash-RL, an efficiency-tier checkpoint in the MiMo-V2.6 series featuring a 309B-parameter (15B active) Mixture-of-Experts architecture, 1M-token context, and native support for text, image, video, and audio. The model uses a single mixed reinforcement learning run across coding, agentic, visual, and cybersecurity tasks rather than domain-specific training.

Xiaomi released MiMo-V2.5 on April 22, 2026, a native omnimodal model with a 1,048,576 token context window. The model is priced at $0.40 per million input tokens and $2 per million output tokens, positioning it as a cost-efficient alternative for agentic applications requiring multimodal perception across image and video understanding.

Alibaba has released Qwen3.5-Omni, an omnimodal model handling text, images, audio, and video with a 256,000-token context window. The model reportedly outperforms Google's Gemini 3.1 Pro on audio tasks with support for 74 languages in speech recognition, a 6x increase from its predecessor. An unexpected emergent capability: writing working code from spoken instructions and video input, which the team did not explicitly train.