During the rollout of GPT-5, OpenAI experienced a significant outage due to the auto-switcher router randomly directing traffic between different model variants, leading to increased latency and incoherent responses. This incident highlighted the importance of understanding the distinct architectures and capabilities within the GPT-4 family, which includes GPT-4, GPT-4 Turbo, and GPT-4o, each designed for specific use cases like high-stakes reasoning, high-volume chat, and multimodal processing. The technical playbook emphasizes the need to match model capabilities to production priorities, considering factors such as architecture, inference speed, context handling, training data recency, and cost. The performance characteristics of each variant influence deployment strategies, with GPT-4 excelling in accuracy for complex tasks, GPT-4 Turbo offering cost-effective high-speed processing, and GPT-4o providing integrated multimodal capabilities. To optimize usage, the text suggests leveraging tools like Galileo for real-time observability, evaluation, and safety protection, thus ensuring models meet specific production requirements and avoid performance surprises.