Where They're Actually Similar
Both OpenAI's and Anthropic's flagship models handle the bulk of common business use cases well: drafting and summarizing content, extracting structured data, answering questions over internal documents (RAG), and powering a customer-facing chat assistant. For most integrations, either family is a defensible choice on capability alone.
Where the Decision Actually Gets Made
In practice, the deciding factors are usually: existing tooling and API familiarity on the team, pricing at the specific usage volume and context-length the product needs, latency requirements, and how much the workflow benefits from longer context windows or specific tool-use/agentic capabilities that vary by model version.
The Practical Answer
Don't pick a provider on brand reputation — prototype the actual task (the real prompts, the real data) against both, and let cost-per-task and output quality on that specific job decide. Integration work is also not locked to one provider forever: a well-built integration layer can swap models later without a rebuild.