o3解读：OpenAI发力tool use，Manus们会被模型取代吗？

Core Insights - OpenAI has released two new models, o3 and o4-mini, which showcase advanced reasoning and multimodal capabilities, marking a significant upgrade in their product offerings [8][10][45]. - The o3 model is identified as the most advanced reasoning model with comprehensive tool use and multimodal capabilities, while o4-mini is optimized for efficient reasoning [8][10]. - The evolution of agentic capabilities in o3 allows it to perform tasks more like a human agent, enhancing its utility in various applications [14][15]. Group 1: Model Capabilities - The o3 model integrates tool use and reasoning processes seamlessly, outperforming previous models in task execution speed and effectiveness [14][10]. - OpenAI's approach to model training has shifted, focusing on creating a mini reasoning version first before scaling up, which contrasts with previous methods [9][10]. - The multimodal capabilities of o3 allow it to understand and manipulate images, enhancing its application in factual tasks [45][46]. Group 2: Agentic Evolution - The agentic capabilities of o3 enable it to perform complex tasks, such as web browsing and data analysis, with a level of efficiency comparable to human agents [14][16]. - There is a discussion on the divergence of agent product development into two technical routes: OpenAI's black-box approach versus Manus's white-box approach [15][16]. - Testing of o3 against classic use cases shows its ability to gather and analyze information effectively, although it still requires user prompts for optimal performance [16][19]. Group 3: Market Position and Pricing - OpenAI's o3 model is priced higher than its competitors, reflecting its advanced capabilities, while o4-mini is significantly cheaper, making it accessible for broader use [77][78]. - The pricing strategy indicates that all leading models are competing at a similar level, with o3 being the most expensive among them [77][79]. - The introduction of Codex CLI aims to democratize access to coding capabilities, allowing users to interact with AI models in a more integrated manner [64][68]. Group 4: User Feedback and Limitations - User feedback highlights some limitations in visual reasoning and coding capabilities of o3 and o4-mini, indicating areas for improvement [69][70]. - Specific tasks, such as counting fingers or reading clock times, have shown inconsistent results, suggesting that visual reasoning still requires refinement [70][72]. - Concerns have been raised regarding the coding capabilities of the new models, with some users finding them less effective than previous iterations [75][76]. Group 5: Future Directions - OpenAI's ongoing research into reinforcement learning (RL) suggests a focus on enhancing model performance through experience-based learning [81][85]. - The concept of "Era of Experience" emphasizes the need for agents to learn from interactions with their environment, moving beyond traditional training methods [85][88]. - Future developments may include improved planning and reasoning capabilities, allowing models to better integrate with real-world applications [89][90].