I’m testing the same prompt through two LLM APIs and getting noticeably different answers, even with temperature set to zero. I expected that setting to make the output stable. Does it only reduce randomness, or am I comparing two requests that aren’t really equivalent?
For context, I’m interested in differences between providers such as OpenAI, Claude, Grok, Perplexity, and DeepSeek. I’m not asking how to make one prompt portable across APIs; I’m trying to understand what temperature zero does and what else can change the result.
Does temperature zero guarantee the same response?
Not necessarily. Temperature controls how a model selects among possible next tokens, and setting it to zero generally favors the most likely choice. But it is not a universal promise that every request will produce identical text. Providers can implement decoding differently, and the setting may not control every source of variation in generation.
Even when decoding is greedy, tiny differences early in a response can lead the model down a different path. A different backend, model build, or implementation detail can change which token is considered most likely at a particular point. Once the wording diverges, the rest of the answer may diverge too.
“The same prompt” may not mean the same input
One common source of confusion is comparing requests that look alike in an application but differ in what the model actually receives. Chat APIs represent conversations as structured messages, not just one plain-text prompt. Differences in message roles and formatting can affect how instructions are interpreted: a sentence sent as a system message may not have the same influence as the same sentence sent as a user message.
Some platforms or application frameworks may also add provider-specific system instructions, format messages for a particular model, or omit content that does not fit the request. Check the actual API request payload sent to each provider rather than comparing only the prompt shown in your interface. Pay particular attention to role assignments, message order, tool definitions, output-format settings, and any instruction added by a wrapper.
The model itself matters, too. Two products may offer different models behind similar-looking names or aliases. Even within one provider, an alias can point to a newer build over time. A changed model snapshot can alter responses without any change to your prompt or application code.
Other settings and limits worth checking
- Tokenization differences between models: models split text into tokens differently. This can affect how the same input fits into a context limit and where truncation happens.
- Context and truncation: a long conversation may be shortened, summarized, or partially excluded by one integration but not another.
- Sampling parameters: temperature is only one possible setting. Top-p, penalties, seed support, and provider-specific defaults can change generation behavior. Check whether each API accepts and applies the parameters you send.
- Tools and retrieval: a model that can search, call a function, or retrieve documents may answer from different material—or take a different route—than a text-only request.
- Output limits: different maximum-token settings can make one answer stop early, which may look like a difference in reasoning or completeness.
A practical way to investigate it
Start with a short, self-contained test prompt and remove tools, conversation history, and retrieved documents. Record the provider, exact model identifier, request settings, and full messages for each call. If the providers expose versioned model identifiers, use those rather than a moving alias while you compare results.
Then change one thing at a time. First verify that the messages and generation settings match as closely as the APIs allow. Next, compare the same prompt across repeated calls to one model. Finally, compare the providers. This helps separate variation within a single service from genuine differences between models or provider implementations.
If the output feeds an application, test the behavior you need rather than expecting identical prose. For example, measure whether both models follow a constraint, include required fields, or answer from supplied evidence. A small set of repeatable LLM reproducibility tests can show whether a change is harmless wording variation or a regression that affects users.
So, when the same prompt at temperature zero produces different answers, it does not by itself prove that one API is ignoring the setting. The useful first question is whether both calls used the same effective input, model version, and generation configuration. After that, some differences are simply differences between the models—and exact text matching may not be a realistic expectation.
