> ## Content Index
> Fetch the complete content index at: https://blog.avis.net/llms.txt
> Use this file to discover other available public pages before exploring further.

# Why the Same Prompt Gives You a Different Answer Every Time
- URL: https://blog.avis.net/same-prompt-different-answer/
- Published: 2026-10-16T04:42:36.000Z
- Updated: 2026-10-16T04:42:45.000Z
- Description: One prompt, 1,000 runs at temperature 0, 80 different answers. Why LLMs vary, and how to get output that's consistent enough for production.
- Author: AVIS
- Tags: Blog, English

You send the same prompt twice and get two different answers. Your test passes on Monday and fails on Tuesday. A user reports a strange reply you can't reproduce. This happens because LLMs are non-deterministic by default, and not only because of temperature. In a 2025 experiment, one prompt sent 1,000 times at temperature 0 came back as 80 different completions. This guide explains the three layers that cause variation, which ones you control, and how to get output that's consistent enough for a production app.

## Why LLMs are non-deterministic

The same prompt gives different answers because of three separate layers, and most advice only covers the first. The model picks each word from a set of probable options (sampling). The servers running the model add tiny numerical differences depending on what else they're processing. And over time, the model, the infrastructure and your own inputs change underneath you.

| Layer            | What causes variation                                                                                                    | Can you control it?                                                       |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- |
| Sampling         | The model chooses each token from a probability distribution, with some randomness                                       | Mostly, through temperature, top-p and seed where the model supports them |
| Serving          | Your request is batched with other users' requests, and batch size changes the arithmetic slightly                       | No, not on a hosted API                                                   |
| Change over time | Model updates behind the same name, infrastructure changes, and differences in your prompt, history or retrieved context | Partly: pin versions and control your inputs                              |

Variation isn't a bug, and it isn't the model "changing its mind" in any meaningful sense. But it does mean that a single output tells you less than you think, and that "set temperature to 0" doesn't solve the problem on its own.

## Sampling: what temperature, top-p and seed control

Sampling is the layer you can adjust. When a model generates text, it scores every possible next token and then picks one. Sampling settings decide how adventurous that pick is.

### Temperature and top-p

**Temperature** reshapes the probabilities before the pick. Low values make the most likely token even more likely; high values flatten the distribution so less likely tokens get chosen more often. On the AVIS [text completion endpoint](https://docs.avis.net/api-reference/endpoints/text-completion?ref=blog.avis.net), `temperature` accepts 0 to 2, and higher values increase variation.

**Top-p** (nucleus sampling) limits the pick to the smallest set of tokens whose probabilities add up to p. At 0.9, the model ignores the long tail of unlikely tokens. **Top-k** does something similar with a fixed number of candidates. Changing one of these at a time is easier to reason about than changing several together.

### Why temperature 0 isn't enough

In theory, temperature 0 means the model always picks the single most likely token, so the output should be identical every time. In practice it isn't, and providers say so. One major provider's API reference states that results won't be fully deterministic even at temperature 0\. The cause sits in the serving layer, covered in the next section.

There's a newer complication. Some reasoning models are tuned to work at their default temperature. One provider strongly recommends leaving a recent model family at its default of 1.0, warning that lower values can cause looping or worse performance on complex maths and reasoning tasks. And not every model accepts the same sampling parameters. Before you lower temperature for consistency, check that the model supports it and doesn't advise against it. On AVIS, each model's accepted parameters are listed under `capabilities.text.supportedParameters` in the [model list](https://docs.avis.net/api-reference/endpoints/model-list?ref=blog.avis.net).

### What a seed does

A **seed** fixes the random numbers used during sampling, so the same seed and the same settings should produce the same choices. One provider describes its seed as a best effort: repeated requests should usually match, but determinism isn't guaranteed. The serving layer can still change the numbers the seed is applied to. Treat a seed as a way to reduce variation, not remove it, and expect that not every model supports one.

## The variation you can't switch off

Some variation comes from how hosted models run, and no request parameter removes it. Knowing where it comes from helps you decide how much to worry about it.

### Your request shares a batch with strangers

Hosted models process many users' requests together in batches, and the batch size changes with traffic. The maths inside the model gives very slightly different results depending on batch size, because floating-point numbers are rounded at each step and the order of those steps changes. Usually the difference is too small to matter. Occasionally it changes which token is most likely, and from that point the rest of the answer follows a different path.

The 2025 experiment mentioned above shows how this plays out. All 1,000 completions at temperature 0 were identical for the first 102 tokens. At token 103, 992 of them continued one way and 8 another, and the texts drifted apart from there. When the researchers ran the same test with batch-invariant code, all 1,000 completions matched, but generation was noticeably slower. That's the trade-off: full determinism exists, but it costs speed, and on a hosted API it isn't your choice to make.

### Updates behind the same model name

A model name doesn't guarantee the same model behaviour forever. Providers update the serving infrastructure in ways that can change outputs. One provider says its numerical configuration changes may happen a few times a year, and exposes a fingerprint field so you can tell when it has. Model aliases that point to "the latest version" move too, and models are eventually retired. Our guide to [model deprecation](https://blog.avis.net/ai-model-deprecation-what-to-do-when-your-model-is-retired/) covers what happens when they do.

### Your own inputs

Much of the variation people blame on the model comes from their side. A changed system prompt, a different order of retrieved documents, an extra turn of conversation history, a timestamp in the prompt or a tool that returned slightly different data all change the input. When you're debugging, compare the full request, not just the user's message.

## How to get consistent LLM output in production

You can't make a hosted LLM perfectly deterministic, but you can make it consistent enough for almost any app. The goal is to control what matters (format, facts, behaviour) and accept small differences in wording.

1. **Constrain the format.** If your code parses the output, use structured outputs or a JSON schema where the model supports them. Format is the most common thing that breaks, and it's the easiest to lock down.
2. **Pin the model version.** Use a dated or versioned model ID rather than a "latest" alias, so behaviour changes only when you choose to upgrade.
3. **Lower sampling randomness where the model allows it.** For extraction, classification and other tasks with one right answer, a low temperature helps. For models that recommend their default, leave it alone and rely on the other steps.
4. **Set a seed if it's supported.** It won't guarantee identical output, but it reduces variation between runs.
5. **Cache answers to identical requests.** If the same input should always get the same answer, such as a product description or an FAQ reply, store the first good result and serve it again.
6. **Log what you need to reproduce a result.** Store the full prompt, model ID, parameters and output for every request you might need to debug. AVIS keeps a [usage record](https://docs.avis.net/api-reference/introduction/use?ref=blog.avis.net) per generation, including the request payload, model and status, which gives you a starting point.
7. **Test on several runs, not one.** Run each test prompt several times and check properties ("valid JSON", "mentions the refund policy", "under 100 words") rather than an exact string. A test that compares one output to a saved one will fail for reasons that have nothing to do with your change.

Our four-step method for [choosing an AI model](https://blog.avis.net/how-to-choose-an-ai-model-a-four-step-method-that-wont-go-stale/) uses the same idea: judge a model on a set of runs, not a single good answer.

### Seeds for image and video models

Image and video models vary for the same reasons, and many accept a seed. On AVIS, the [video generation endpoint](https://docs.avis.net/api-reference/endpoints/video-generation?ref=blog.avis.net) takes a `seed`, and its documentation is explicit that the same seed gives similar, not guaranteed identical, output, with support and range varying by model. A fixed seed is most useful while you iterate on a prompt: keep the seed constant, change one word, and the difference you see is mostly down to that word.

## FAQ

### Why does an AI chatbot give different answers to the same question?

Chatbots sample each word from a set of probable options, so some randomness is built in. On top of that, hosted models run your request alongside others, and small numerical differences can change a word, after which the rest of the answer follows a different path. Changes to the model or to the conversation history add more variation.

### Does temperature 0 make an LLM deterministic?

No. Temperature 0 removes most sampling randomness, but hosted models can still return different outputs because of how requests are batched on the server. In one test, 1,000 runs of the same prompt at temperature 0 produced 80 different completions. For some newer models, providers also advise against lowering temperature at all.

### What does the seed parameter do?

A seed fixes the random choices made during sampling, so repeated requests with the same seed and settings usually produce the same output. Providers describe it as a best effort rather than a guarantee, because the serving layer can still introduce small differences. Not every model supports a seed.

### How do I get consistent output from an LLM API?

Use structured outputs for anything your code parses, pin a specific model version, and lower temperature only where the model supports it. Cache answers to identical requests and log the full prompt, model and parameters so you can reproduce problems. Test each prompt several times and check properties instead of exact text.

### Can I get exactly the same output every time?

Not reliably on a hosted API. Exact, bit-for-bit repeatability needs special serving code that runs more slowly, and hosted providers generally don't offer it. If you need the same answer every time, cache the first good response and serve it again.

### Is output variation a sign the model is wrong?

Not by itself. Different wording for the same correct answer is normal. Variation becomes a problem when the facts, the format or the decision changes between runs, which usually means the prompt leaves too much room for interpretation or the task needs a stricter output format.

## Wrapping up

LLMs are non-deterministic because of sampling, because of how hosted models batch requests, and because models, infrastructure and your inputs change over time. Temperature and seeds only reach the first layer. For production, aim for consistent enough: lock the format, pin the version, cache what should never change, log everything you'd need to reproduce a result, and test on several runs. With AVIS, you can check which sampling parameters each model accepts from the [model list](https://docs.avis.net/api-reference/endpoints/model-list?ref=blog.avis.net) and send them through one [text endpoint](https://docs.avis.net/api-reference/endpoints/text-completion?ref=blog.avis.net) across providers.

*Last updated: October 2026*