Forbes January 26, 2026
For years, the AI conversation has been dominated by a single question: Which model is the best? Every major release is accompanied by charts, benchmarks and bold claims, often suggesting that bigger models automatically mean better outcomes.
That way of thinking is now starting to break down.
While general-purpose large language models have reached broadly comparable performance for everyday tasks such as writing, summarization and research, real differences emerge once AI is deployed inside complex organizations. In large-scale coding projects, agentic workflows and highly specialized enterprise use cases, performance varies dramatically.
The most important question for leaders is no longer which model is best, but which combination of models best fits their business, their risks, and their goals.
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