Some teams have built much of their work inside Claude, Anthropic’s AI assistant, while others are standardising on ChatGPT, Google’s Gemini or a mix of tools. OpenAI’s GPT 5.5 model put the company back at the centre of the AI race. Each of those teams has to decide whether to switch.
OpenAI describes the model as built for complex work in coding, research, data analysis, document creation, spreadsheets, software use and tasks that span tools. It is available in ChatGPT, in the Codex coding tool and to developers. The harder question behind any switch is how quickly the best model for a piece of work can change.
As the models’ performance, cost and access shift, a business needs to be able to move work without rebuilding the process from scratch. That flexibility affects cost, speed, quality and margins, and it depends on documentation. Models change, but a company’s knowledge, examples, rules and standards should not disappear with them. Clear documentation lets work move between models. Poor documentation traps work inside one tool, which forces teams to teach the same context again and again.
A bigger reading limit does not remove that need. AI models read text in small chunks called tokens, and OpenAI’s documentation says GPT 5.5 can use 1,050,000 of them at once. That lets it work with large bodies of company material in one session. The extra room helps only when the business has useful material to give it, because it does not fix unclear instructions, scattered knowledge or weak processes.
Useful documentation has two parts. Company knowledge, such as products, customers, pricing, policies, past decisions and market context, tells the model what the business knows. Instructions tell it how the business works: how tasks are done, what quality means, when to escalate and how the finished work should look. Without both, AI has to guess, ask for the same clarification repeatedly or produce generic work.
Research on AI that looks up outside documents before answering points the same way. The original paper found that models produced more specific and factual language when they could draw on those documents. AI does better work when company knowledge is structured, current and easy to find.
Once that knowledge is written down, the switching question becomes a test. The same documented workflow can be run on ChatGPT, Claude and Gemini before a company standardises on one.
