Comparison
JSON Mode vs Sampling
JSON Mode and Sampling are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for JSON Mode
JSON Mode comes up when the question is fundamentally about inference.
OpenAI `response_format: { type: "json_object" }`.
When you would reach for Sampling
Sampling comes up when the question is fundamentally about inference.
OpenAI default: temperature 1.0, top-p 1.0.
Frequently asked
What is the difference between JSON Mode and Sampling?
JSON Mode: JSON mode is a provider-specific feature that forces the model to emit syntactically valid JSON. Stronger than asking nicely; weaker than full structured output with a schema. Sampling: Sampling is the act of choosing the next token from the model's output distribution, typically after applying temperature and a truncation strategy like top-p or top-k.
When should I use JSON Mode vs Sampling?
JSON Mode is the right concept when you are focused on inference. Sampling applies when you are focused on inference.
Are JSON Mode and Sampling the same thing?
No. JSON Mode is inference; Sampling is inference. They are related but address different parts of the AI stack.