So I've been using gpt-5.6 for awhile...
Summary
An early access user extensively tested the GPT 5.6 model, incurring an estimated \$180,000 to \$240,000 in inference costs over 1.5 months. The user reports GPT 5.6 demonstrates significantly improved capabilities, particularly in computer and browser interaction, intent understanding, and sustained execution of long-running tasks without losing context. Key projects undertaken include migrating a monolithic JavaScript project to TypeScript, building new CI pipelines, and implementing complex authentication systems for "Lakebed." For "T3 Code," the model performed complete native rewrites of a React Native app to Swift and SwiftUI in hours. It also successfully rewrote the "Hermes agent" to Rust and a TypeScript Go port to Rust, achieving up to 18x faster transpilation. Other feats include autonomously registering for PlanetScale for a Dropbox-like cloud service (FS2) and fixing a broken Linux boot partition via remote KVM access. The model's ability to tackle "impossible tasks" and maintain context for extended periods marks a significant advancement.
Key takeaway
For AI Engineers and Architects evaluating advanced models for complex development, GPT 5.6 offers a powerful workhorse capable of sustained, multi-hour autonomous execution. You should explore its enhanced computer use and context management for large-scale code rewrites, CI/CD pipeline creation, and infrastructure configuration. This model can significantly reduce manual intervention in multi-step projects, enabling you to tackle previously "impossible tasks" and accelerate development cycles.
Key insights
GPT 5.6 demonstrates unprecedented capability for complex, long-running, multi-step tasks, including extensive code rewrites and autonomous computer interaction.
Principles
- Sustained context enables multi-hour task execution.
- Model excels at complex intent understanding.
- Autonomous computer/browser use is highly effective.
Method
Deploy agents across a machine fleet with centralized configuration and SSH computer access. Provide high-level goals, allowing the model to autonomously manage context and execute long-running, multi-step tasks.
In practice
- Automate large-scale code refactoring and rewrites.
- Implement complex web-based automation via browser use.
- Configure and manage network infrastructure autonomously.
Topics
- GPT 5.6
- AI Agents
- Code Generation
- Web Automation
- Software Development
- Infrastructure Automation
Best for: AI Architect, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by Theo - t3․gg.