OpenAI
Best for
General-purpose LLM use, structured outputs, and strong developer ergonomics.
Tradeoff
Broad and reliable, but not always the cheapest path for every workload.
Stack category
Pick models by output quality, latency, cost, context length, and whether the product needs structured outputs or multimodal support.
Use one provider for the workflow until a real quality gap or latency issue forces you to compare more than one. Most products do not need five model vendors on day one.
View stack libraryModel providers to compare
General-purpose LLM use, structured outputs, and strong developer ergonomics.
Broad and reliable, but not always the cheapest path for every workload.
Long-form writing, reasoning, product briefs, and workflow design.
Strong on reasoning and writing, but you still need to validate product-specific output quality.
Multimodal flows, lower-cost experiments, and narrower model fit checks.
Useful for specific workloads, but usually better as a second or third model choice.
Practical fit
Best when the model call sits behind an input form and a result renderer.
Best when model choice affects brief quality, draft generation, and human review efficiency.