Answer Stability at Temperature 0: How to Measure Risk

What breaks even at temperature 0

Temperature 0 does not turn a model into a calculator. It only pulls the choice harder toward the most likely next token. If there are two almost equal options, the model may pick one in one run and the other in the next. From the outside it looks odd: the request is the same, but the answer changes slightly.

Most often, the meaning does not change first. The form changes. The model reorders bullet points, uses different wording, shortens the explanation, or adds an extra sentence. For marketing copy, that is a minor issue. For a workflow where the answer goes to a customer, a CRM, or a report, even that small difference can break the process.

Sometimes the mismatch is no longer cosmetic. The same prompt returns "approve" in one run and "send for manual review" in another. Or it assigns a different case type: "complaint" instead of "question about the plan." That does not happen every time, but it is better to measure the risk in advance than after a user complaint or an integration rework.

It helps to split the difference into two types. Harmless differences are a different order of points, different wording, or a shorter or longer answer. Dangerous differences are a changed label, final conclusion, number, action priority, or field value.

This is especially visible in operational tasks. Suppose a bank asks the model to review an incoming message and choose one of three routes: answer automatically, send it to an operator, or mark it as risky. If the answer changes slightly, nothing bad happens. If the route changes, the team loses time and the customer gets the wrong response.

The same thing happens in data extraction. Today the model pulls the correct date and amount from a contract, and on the next run it swaps the fields or misses a condition. Formally, there is an answer in both cases. In practice, one of them can no longer move forward.

That is why stability should be checked not on abstract questions like "explain what credit is," but on real scenarios: ticket classification, form checking, call summarization, JSON filling. That is where you can see whether variability is harmless or whether it changes the decision and costs money.

Where the differences come from

Temperature 0 does not make the model fully deterministic. It only asks it to take the most likely next token. If two tokens have almost the same chance, even a very small shift can change the choice. After that, the whole sentence may diverge: a different first token leads the model down a different path.

That kind of shift often comes not from the prompt text itself, but from everything around it. A provider may send the same call to different replicas, builds, or even different versions of the same model under the same name. The decoder, floating-point rounding, batching with neighboring requests, and hardware all affect token selection. The system prompt, chat template, JSON schema, and tool descriptions also add hidden text that the user does not see.

Hidden updates happen more often than people think. A provider may update the weights, tokenizer, safety layer, or internal chat template without changing the public model name. From the outside, the API call looks the same, but inside it follows a different path.

The same is true for the system message and tools. One SDK adds a phrase like "answer briefly," another inserts a strict JSON schema, and a third passes functions in a different format. For the model, that is already a different prompt, even if your user message did not change.

Context also matters more than many people expect. If the conversation is long, one platform may trim early messages, another may cut down tool output, and a third may shorten the retrieval block. The result changes not because the model "changed its mind," but because it saw a different set of tokens.

There is also a very simple case: max_tokens is too small. Then the model cuts the answer off early and starts saving words from the first lines. Sometimes this looks like strange variability, although the real cause is the limit.

The bottom line is simple: the same API call does not always mean the same internal path. The external contract may stay the same, while the chain inside still shifts a little. That is exactly the kind of small shift that later shows up in production.

Where the differences cost money

Temperature 0 does not protect you from extra costs if the model is part of operational decisions. In a demo, it looks like a small issue. In production, the same request may go to a different queue, receive a different status, or make an employee double-check something that passed automatically yesterday.

The most common example is ticket and case classification. Suppose a bank receives a complaint: "Money was charged, I did not lose the card, and I did not approve the transaction." In one run, the model assigns the class "disputed transaction," and in another, "general card complaint." The route changes after that: in the first case, the ticket goes to the fraud team and lands in a 2-hour SLA, while in the second it goes to standard support with a one-day response. The cost of that mismatch is obvious: late penalties, an extra customer contact, and more manual work.

The risk is no lower when extracting fields into tables and forms. The model may extract the contract number without a prefix one time, swap the start date with the signing date the next time, and leave a field blank the third time, even though the text is the same. If that data goes into a CRM, a report, or a payout request, the mistake quickly moves down the chain. Then an analyst fixes the table by hand, an operator calls the customer again, and the team argues about where exactly things broke.

Customer-facing answers do not differ only in style either. Sometimes the fact itself changes. For example, one answer says a refund will take up to 3 days, while another says up to 10. Even if both texts are polite, one creates a false expectation. It can be softer too, but still unpleasant: today the answer is calm and businesslike, and tomorrow the same prompt produces a dry tone that sounds like a rejection.

Usually, money is lost in four places: the wrong task route, extra manual review, a repeat customer contact, and an error in a report, form, or status. The problem rarely lives in one answer alone. One shift pulls the next step with it: the wrong class, the wrong template, the wrong owner. If the chain is long, even small variability starts to matter a lot.

How to check stability step by step

Checking one or two good examples tells you almost nothing. You need real requests from one working scenario: for example, extracting details from emails, classifying messages, or checking a contract for risk.

First, freeze the environment. Lock in one model, one provider, one endpoint, and the full set of parameters: temperature = 0, top_p, max_tokens, system prompt, and seed, if it is available. Then collect 30–100 examples for one scenario. Do not mix support chat, summarization, and field extraction in a single run. The narrower the scenario, the fairer the result.

After that, run each example many times under the same conditions. Usually 10–20 repeats per request are enough to see where the model starts to wobble. Between repeats, do not change anything, not even a single space in the prompt.

Do not save only the final text. Log the full answer, response time, model name and version, provider ID, finish_reason, and other service fields. Those are often what explain why two similar answers drifted apart.

Then break the differences into types. One answer changes only the wording, another breaks the JSON, and a third changes the actual decision. You cannot put all of these into one group, because for business they have different costs.

After the run, do not look only at "matched" and "did not match." For a scenario with fixed JSON, it is useful to count the share of fully identical answers. For text, the more important metric is the share of identical decisions: for example, both answers put the case in the "complaint" category, even if the explanation differs.

This kind of test quickly shows the real picture on your data. Sometimes the model matches in meaning in 95% of cases but breaks the structure in 8% of answers. For a production system, that is already a noticeable risk: one broken JSON in twenty can easily turn into extra retries, manual review, and a failure in the next step.

How to build a scenario set

One set, one task. Do not mix classification, summarization, and data extraction in the same table, even if they are part of the same business process. Each task needs its own way of checking: one label is correct for classification, several wordings can be acceptable for summarization, and each field matters for extraction.

The set should look like real work, not a showcase of easy examples. Include not only clean requests, but also the ones that usually annoy the team: short messages without context, long emails with extra details, noisy texts where the useful part is hidden among signatures, quotes, and service lines.

A good sample usually includes short requests of 1–2 lines, long messages with extra text, forwarded emails, logs, chat snippets, examples with typos and missing pieces, and rare cases where the mistake is more expensive than usual.

Rare cases often give the most useful picture. For a bank, that might be a complaint where the model has to tell a normal question apart from signs of fraud. For retail, it might be a return request with a contradictory description. There are not many of these examples, but that is where the differences are most visible and most expensive.

Do not clean the input data before the test. If the user writes with mistakes, leaves out dates, or phrases the thought vaguely, leave it as is. Otherwise, you will measure the model in a lab, not in production.

Each scenario should have a separate note with the expected result. It is better to store not only the answer itself, but also the check format: exact match, acceptable variants, or a list of required fields. Then the team will not argue after every run about whether the answer counts as correct.

In practice, a simple table is enough: scenario ID, task type, input, expected result, acceptable variants, and cost of error. That is already enough to compare models not by general impression, but by risk on the same cases.

How to measure risk without complex math

In this kind of testing, risk is not some abstract "instability". It is the share of runs where the answer drifted off course. For this topic, that measure is usually enough: you are not arguing whether the model is "generally fine"; you are seeing which cases fail and how often.

It is better to run the same case as a series, not just once. In practice, 30–50 repeats per scenario are enough. If a case is rare but expensive, use more runs.

It is useful to calculate risk in three layers. First, check exact text match if wording matters to you. That is important for template emails, legal phrases, and fixed responses. Then compare the JSON structure: are all fields present, are the data types correct, did the labels and names change? Finally, look at the final business decision. Even if the text is different, the decision may stay the same.

Suppose you have a scenario called "identify the ticket category and return JSON." You run the same request 50 times. The text matches perfectly in 34 cases, the JSON structure matches in 47, and the business label matches in 49. Then the risk is 32% for text, 6% for structure, and 2% for the business decision.

That breakdown quickly sobers everyone up. Sometimes the team argues about wording variability, while the real problem is that in 4% of runs the model changes the label and sends the ticket into the wrong flow.

For production release, you need a simple threshold. For example, for critical JSON integrations, 0% structure breakage may be acceptable; for the business label, no more than 1%; and for text, you may not need a threshold at all if wording does not affect the process. If even one important case is over the threshold, it does not ship.

Example of a real workflow

Take a common support task: the model reads a customer message and must return two things — the ticket topic and the urgency. On paper, that is simple. In a real queue, even a small mismatch changes who gets the ticket and how fast they start working on it.

Example text: "I can't log into my account. I need to make a payment today, otherwise the shipment will fail." The same request can be run many times even at temperature 0 and still produce slightly different results.

In some runs, you get:

• topic: "access", urgency: "high"
• topic: "technical issue", urgency: "high"
• topic: "payments", urgency: "medium"

The difference looks small. But the route is already different. "Access" goes to the authentication team, "technical issue" goes to the general support line, and "payments" goes to billing. If urgency drops from high to medium, the ticket may miss the fast SLA. The customer wrote almost the same thing, but the business got a different outcome.

This happens because the short text contains two signals. The phrase "can't log in" pulls the model toward access. The phrase "need to make a payment today" pulls it toward payments and urgency. The model has to choose which part is the main one. On repeated runs, that choice does not always match.

The spread usually gets smaller when the prompt sets strict boundaries. Instead of asking "identify the topic and urgency," it is better to give fixed options and a rule for choosing between them. For example: the topic must be only one of [access, billing, tech], urgency must be only one of [low, medium, high], if the customer cannot make the payment today, set high, and if the text contains two topics, choose the one that prevents the user from finishing the action.

It is even better to require a strict answer format, with no free-form wording. For example, only JSON with the fields topic and priority. Then the model thinks out loud less and drifts into neighboring categories less often.

The spread usually does not disappear completely, but it gets noticeably smaller. And that is already a practical result: you are no longer debating abstract LLM determinism; you are looking at real risk in a workflow where every label affects the ticket route.

Common mistakes in testing

Even careful teams often overestimate the result of the first check. Temperature 0 reduces spread, but it does not make the model fully predictable. That is why stability cannot be judged from one successful run.

The most common mistake is simple: they run it once, see a good answer, and check the box. That tests luck, not risk. The same request should be run in series, at least 20–50 repeats on one set of inputs. Otherwise, the small differences simply will not have time to appear.

An overly convenient sample is just as harmful. The team takes clean, short, well-written examples and celebrates the smooth results. Real work does not look like that. You need typos, long messages, mixed languages, extra fields, and broken text. If you only test showcase cases, the failure will come from ordinary live traffic later.

Another common mistake ruins the experiment itself: changing the model, prompt, and parser at the same time. After that, nobody knows what caused the differences. If you updated the model, rewrote the system prompt, and also tightened the JSON parser, any jump in errors can no longer be explained honestly.

A simple rule helps here: change only one factor at a time, keep the input data fixed during the test, save the same call parameters, and check the result before and after the change on the same set of cases.

Many people look only at the average score. That is a trap. The average can look fine even when 5 out of 100 answers break the workflow completely. For production, those outliers matter more than the average beauty, especially in field extraction, case classification, and format checks.

There is also a very down-to-earth mistake: people do not save the test date, model version, and prompt text. Two weeks later, the team sees a different result and argues whether the model got worse or the test was simply different. If you work through a gateway or directly with a provider, record everything that affects the answer: model, revision, parameters, prompt template, parser version, and run date.

A proper check looks boring, and that is good. The fewer guesses and "we remember it verbally" moments there are, the easier it is to understand where the risk of differences comes from.

Quick checklist before launch

Before release, lock in not only the prompt, but the whole request context. Temperature 0 will not save you if the model, version, top_p, seed, system instruction, tool calls schema, or routing through a different provider changes between runs.

The team should have a short checklist that can be completed in 15–20 minutes. If even one item is unclear, differences usually show up in live traffic.

• Record all request parameters in one place and do not change them between tests.
• Check that the test set does not contain only simple examples.
• Measure not only text similarity, but also the final decision.
• Agree on the acceptable spread in advance.
• Repeat the check after any noticeable change: a new model, a prompt edit, a provider switch, PII masking, or an SDK update.

A good quick test is simple: you take the scenario "identify the message category," run it many times, and look not at the beauty of the text, but at the final category. If the same request sometimes goes to "complaint" and sometimes to "consultation," the system is too early to release, even if the answers sound almost identical.

The list only looks strict on paper. In practice, it saves many hours of post-launch debugging.

What to do next

Do not try to measure everything at once. Pick one scenario where mistakes are expensive: a wrong answer to a customer, a failure in ticket routing, or a missing required field in a report. One such scenario will be more useful than ten abstract tests.

First, collect a small set of live examples and run them many times on one model. That is already enough to see the real picture. The average result may look good, but individual runs will still drift in meaning, format, or completeness.

After that, the plan is simple: choose 20–50 requests from the live workflow, run each request as a series of repeats, and then repeat the same set on a second model or with another provider. Compare not only quality, but also answer spread.

That spread is often what decides whether a scenario can go to production. One model may write a little worse on average, but hold format and facts more consistently. Another may answer better sometimes, but once in every twenty runs it breaks JSON or changes the meaning. For support, compliance, or internal assistants, the second option is usually worse.

If you compare several models through one OpenAI-compatible endpoint, these checks are easier to repeat without reworking the integration. For example, in AI Router on airouter.kz, you can change base_url to api.airouter.kz and run the same set of cases through different providers without changing the SDK, code, or prompts. That is useful when you need to compare models in the same setup, not figure out what changed around them.

Save the baseline report after the first run. Record the model, provider, prompt version, number of repeats, and the type of differences you found. Then return to that set every time you change the model, edit the prompt, or move to another provider.

One line in a prompt can shift behavior more than it seems. It is better to catch that in a short repeatable test than after release, when the error reaches a customer response or a business process.