GPT-5.6 Sol

Sol is the flagship of the GPT-5.6 line, positioned for the hardest reasoning work OpenAI's lineup is asked to do. Like every flagship it is priced for tasks where being wrong is expensive, which makes the selection question — when do you actually need this? — more important than the capability question.

It is a general model rather than a specialist. That is its advantage: one model that handles reasoning, code, long documents and vision without switching, which simplifies architecture even where a specialist would edge it out.

Sol is strong at problems that need to be decomposed before they can be solved — the kind where the first useful step is working out what the question actually is. It handles ambiguous, underspecified prompts better than smaller tiers, which matters because real user input is usually underspecified.

Tool use and structured output are reliable, which is what makes it viable as the reasoning core of an agent rather than just a chat endpoint.

Cost and latency, in that order. Sol used for tasks a mid-tier handles is the single most common source of surprise API bills. It is also, like all flagships, unnecessary for anything with a well-defined answer format.

Being general means it is beaten by specialists on their own ground — a dedicated code model will often produce better completions per dollar.

Use Sol where the problem is genuinely hard or genuinely ambiguous, and where a wrong answer costs more than the call. For structured, repeatable work, drop down the lineup. The teams that spend well on flagships are the ones that measured which tasks needed them rather than assuming.