Can LLM output be fully deterministic?

Not guaranteed. Setting temperature to 0 and a fixed seed makes output highly repeatable, but providers run on distributed hardware where floating-point and batching differences can still cause rare variation. Treat it as best-effort reproducibility, not a hard guarantee.

What does the seed parameter do?

OpenAI's seed parameter asks the API to sample tokens the same way given identical inputs, so repeated calls tend to return the same completion. It works best combined with temperature 0 and an unchanged prompt, model and parameters.

Does Anthropic support a seed?

Anthropic's API does not expose a seed parameter. For Claude you maximize reproducibility by setting temperature to 0 and keeping the prompt, model version, and other parameters fixed. The helper reflects this per provider.

Is anything sent to a server?

No. The helper builds the checklist and config snippet entirely in your browser. Your inputs are never uploaded, stored or logged.

Seed & Determinism Helper

Make your LLM calls as repeatable as possible

Flaky, non-reproducible model output makes testing and debugging miserable. This helper builds a provider-specific checklist and a ready-to-paste configuration snippet — covering temperature, top_p, seed and the often-missed details — so you remove every controllable source of randomness from your calls.

What actually controls determinism

Several things have to line up for an LLM to return the same answer twice:

Temperature = 0 is the single biggest lever. It tells the model to pick its most likely token instead of sampling, which removes most run-to-run variation.
A fixed seed (OpenAI) asks the API to sample identically across calls. Anthropic and some others do not expose a seed, so you rely on temperature 0 there.
Identical inputs — the exact same prompt, message order, and any tool or function definitions. A single changed character can change the output.
A pinned model version. Provider model aliases (like latest) drift over time; pin a dated snapshot so an upgrade does not silently change your results.
Unchanged decoding parameters — top_p, max_tokens, stop sequences and penalties all influence the outcome.

Even with all of this, distributed inference can introduce rare floating-point differences, so reproducibility is best-effort, not absolute.

Tips

Pin the model snapshot rather than a moving alias — this is the most commonly missed source of drift.
Log the seed and model version alongside each response so you can reproduce a specific output later.
Hold the whole request constant when comparing prompts; change one variable at a time.
For Anthropic, lean entirely on temperature 0 and stable inputs, since no seed is available.