Temperature 0 reduces sampling variation, but it does not guarantee that a language model will return the same text every time. Greedy decoding chooses the highest-scoring next token; small changes in the scores, model version, or serving configuration can still change that choice. A fixed seed can improve consistency where supported, but hosted systems do not necessarily promise bit-for-bit repeatability.
What temperature 0 actually changes
Temperature is a decoding setting. At zero, a model typically uses greedy decoding: it selects the token with the highest score at each step rather than sampling among alternatives. That reduces ordinary sampling randomness, but it does not lock every part of inference into an identical calculation. The scores themselves depend on the model and the computation used to produce them.
As an Amazon Associate I earn from qualifying purchases.
So “temperature 0” is not equivalent to “deterministic.” It narrows one source of variation; it is not a whole-system reproducibility switch.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Why the same prompt can produce a different completion
Close token scores can make tiny numerical changes matter
Floating-point calculations round intermediate results. Changing the order of operations can therefore change a result slightly. A September 2026 technical preprint describes how GPU matrix-multiplication kernels may use different configurations and reduction orders depending on architecture or workload shape. Those small numerical differences can alter token scores, and when the leading candidates are close, can flip which token wins the greedy choice. The preprint examines cross-architecture reproducibility and proposes fixed-configuration kernels; it does not establish that every hosted provider uses the same implementation or that this mechanism explains every instance of drift.
#1 Best Overall
Generation then proceeds token by token. Once one token changes, the model receives a different preceding sequence for the next step, so the continuation can diverge too. That cascade follows from sequential generation; it is not a measured drift rate from the preprint.
The serving backend or model may have changed
A prompt can stay identical while the model snapshot or serving configuration changes. OpenAI says its API outputs are non-deterministic by default and that model behavior can change between snapshots and families. For OpenAI API requests, the system_fingerprint indicates backend configuration and can change when OpenAI updates its numerical serving setup. This fingerprint is an OpenAI-specific mechanism, not a universal feature of LLM APIs. OpenAI’s advanced-usage guide and seed cookbook explain the controls and metadata.
Rank #2
Repeated-run variation has been observed, but no universal rate follows
A January 2026 preprint reports repeated-run variability at temperature 0.0 for gpt-4o-mini and llama3.1-8b. Its study covers five prompt categories, three prompting modes, two temperatures, and API-served versus local deployment. It measures unique-output fractions, lexical similarity, and word counts, while noting limitations of lexical metrics. This supports the bounded claim that zero-temperature outputs can vary; it does not establish a general drift percentage or rank all current models. The study’s abstract and inspected passages do not provide a single rate suitable for a universal claim.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
What a seed and fingerprint can—and can’t—do
Where an API supports a seed, reusing it can help produce more consistent outputs. For OpenAI, the documented approach is to keep the seed and all other request parameters the same, then compare the returned system_fingerprint. Even when the seed, parameters, and fingerprint match, OpenAI says there remains a small chance of different responses. A seed is therefore a best-effort control, not a promise of identical output. Consult OpenAI’s seed documentation for the provider’s stated behavior; support and guarantees may differ across providers and models.
How to make LLM runs more reproducible
- Freeze the request. Keep the exact prompt, system instructions, decoding settings, and other request fields unchanged. Record the requested model identifier.
- Set and log a seed where available. Reuse the same seed for comparable runs, but treat it as a consistency aid rather than a guarantee.
- Capture version metadata. Save any returned backend fingerprint or equivalent version information alongside each result. For OpenAI, record
system_fingerprint; other providers may expose different metadata or none. - Save the complete run record. For auditability, retain raw inputs and outputs, parameters, timestamps, and provider/version metadata. This makes comparisons possible; it does not by itself guarantee that a run can be replayed exactly.
- Evaluate representative cases repeatedly. Establish a baseline and rerun representative test inputs after a model or configuration change. OpenAI’s model-optimization guide recommends evals for measuring behavior. Choose what counts as success for the task: exact wording may matter for a strict format, while semantic equivalence or task outcomes may matter more for other applications.
When bit-for-bit replay is a requirement
Temperature and seed alone are not enough to establish exact replay on a hosted API. Check whether the deployment lets you pin the model snapshot, runtime, hardware, kernels, and batching behavior, and what version controls it provides. If those factors are not exposed or fixed, exact text reproduction cannot be inferred from a matching prompt and decoding settings. A reproducibility claim should also say what is being reproduced: the exact string, meaning, structure, or task result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a reproducibility claim
When comparing deployments or workflows, ask what is fixed and what is measured:
- Is there a fixed model snapshot or version identifier?
- Is a seed supported, and what guarantee does the provider state?
- Are backend fingerprints or equivalent configuration metadata returned?
- Can the infrastructure, hardware, kernels, and batching be pinned?
- Do evaluations compare exact text, semantic equivalence, or task success?
These checks distinguish repeatable behavior for a particular task from a stronger claim of identical computation and output. Neither a temperature setting nor one matching response establishes that stronger claim.
Quick Recap
Best Value
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
