Hugging Face released Holo3, achieving 78.85% on OSWorld-Verified computer-use benchmark. Architecture: 10B active parameters (122B total), trained via agentic flywheel (synthetic navigation data, out-of-domain augmentation, curated RL). Includes Synthetic Environment Factory (auto-generated enterprise UI scenarios) and H Corporate Benchmarks (486 multi-step tasks: e-commerce, business software, collaboration, multi-app workflows). Open-source 35B variant under Apache2. Significantly outperforms base Qwen3.5; cost orders of magnitude lower than GPT-5.4/Opus 4.6.

OpenAI released GPT-5.4 mini and nano, compact models optimized for latency-sensitive workloads. Mini: 2x faster than GPT-5 mini, approaches GPT-5.4 performance on SWE-Bench Pro and OSWorld, strong on coding/reasoning/multimodal. Nano: smallest/cheapest variant, designed for classification, data extraction, ranking, subagent delegation. Both excel in responsive coding assistants, parallel subagent orchestration, real-time screenshot interpretation. Architecture pattern: large model (planning/judgment) delegates narrow tasks to mini/nano in parallel.

OpenAI released GPT-5.4, a frontier model combining reasoning, coding, and agentic capabilities. Key features: native computer-use for agents (1M token context), 83% professional competency on GDPval benchmark (vs. 70.9% for GPT-5.2), improved tool-use efficiency via tool search, state-of-the-art token efficiency. Supports extended thinking with mid-response adjustment in ChatGPT. Integrated into ChatGPT (thinking variant), API, and Codex. Performance gains span spreadsheets, presentations, documents, and long-horizon workflows.

OpenAI released GPT-5, the base frontier model scaling further on pre-training beyond GPT-4o. Training combined supervision techniques (SFT, RLHF) with new supervision methods. Tested for natural interaction quality, broader knowledge, improved alignment, stronger emotional intelligence. Pitched for writing, programming, practical problem-solving with fewer hallucinations. Deployed as research preview with extensive safety evals—no significant safety risk increase vs. prior models. Follows OpenAI's Preparedness Framework.

OpenAI announced o3 and o4-mini, reasoning-focused models. o3 emphasizes extended reasoning at multiple effort levels (low/medium/high/xhigh). o4-mini: smaller, cheaper reasoning model for cost-sensitive applications. Both designed for complex problem-solving requiring multi-step inference. Follows similar safety and alignment training as GPT-5 (SFT, RLHF, new supervision). Research preview status with extensive pre-deployment safety evaluation.

OpenAI released GPT-4.5 as a research preview—largest, most knowledgeable model to date. Built on GPT-4o with further pre-training scaling, general-purpose focus (vs. STEM-heavy reasoning models). Training: new supervision techniques + SFT + RLHF. Early testing shows natural interaction, broader knowledge, stronger intent alignment, improved emotional intelligence, fewer hallucinations. Extensive pre-deployment safety evals showed no significant risk increase. Follows Preparedness Framework.

Article stub—insufficient content provided. Appears to reference Sora (OpenAI video generation) and additional models but lacks substantive technical or strategic detail.