agentic-AI

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.

Chinese lab AllSpark has released Iris-mini and Iris-pro, two open-weight search agents built on Qwen3 models that claim the top spot among open-weight systems in their size classes on four research benchmarks. The release includes model weights, an agent harness, and evaluation code, with training pipelines to follow.

Alibaba's Qwen team has released Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter mixture-of-experts model activating 95B parameters per token. The company claims it is the first open-weight release to reach Qwen-Max-class performance, with a hosted variant, Qwen3.8-Max, offering 1M-token context and vision input.

OpenAI released its GPT-5.6 flagship model family in three sizes: Luna ($1/$6 per 1M tokens), Terra ($2.50/$15), and Sol ($5/$30). The company claims GPT-5.6 Sol scores 53.6 on the Agents' Last Exam benchmark, outperforming Claude Fable 5's score by 13.1 points.