Temperature 0 reduces variation in AI code

You must now treat temperature 0 as a control that reduces variation in AI-generated code, not as proof of reproducibility. Reproducibility is a property of the entire pinned pipeline—model, infrastructure, seed, and validation—and requires repeated runs, documented configuration and explicit reporting of measured variation.

The prompt is an analysis of 2 November 2026 of temperature control in language model code generation, which argues that setting temperature to 0 lowers noise in model outputs but does not guarantee reproducible results across infrastructure or provider changes. A study examined safety evaluations using temperature control and found that identical runs can still vary, and that some newer models no longer support sampling parameters at all. In our assessment, this means you cannot rely on a single dial setting as evidence of deterministic behaviour; you must instead document and measure the entire chain, including hardware, model version, seed values and repeated validation runs.

Why does temperature 0 not guarantee reproducibility?

Temperature 0 instructs a language model to select the highest-probability next word at each step, which should theoretically produce identical output. Research shows this assumption fails in practice. Residual variation persists even with temperature set to 0 and with top_k restricted to the single highest-ranked candidate. GPU execution introduces small numerical differences that propagate through token probability calculations, with larger effects on mid-range probabilities. Infrastructure details—the specific hardware, the inference provider, the numerical precision of the underlying libraries—all contribute to divergence. A single successful run therefore masks this noise and creates false confidence in reproducibility.

What security risks emerge from sampling strategy?

Higher temperature increases output variation, which can surface both functionally correct solutions and insecure variants. Research on generated Python, Java and C++ code found that as temperature and sample count rise, the likelihood of finding a correct solution may improve, but the probability of that solution being secure often falls. This is not a marginal effect; in many tested configurations, broader sampling was associated with more vulnerable code alongside working candidates. Functional acceptance tests alone cannot catch this trade-off. A piece of code that passes your test suite can still contain a security flaw invisible to functional validation but detectable through static analysis or manual review.

What must you document and control?

Treat reproducibility as a property of the pinned pipeline, not of the model alone. This requires:

1. Record the model identity and inference configuration — document which model version, which provider, which sampling parameters (temperature, top_k, seed) and which hardware class ran each generation.
2. Run each code generation multiple times and report the spread — preserve the raw outputs and measure disagreement between runs to quantify residual variation.
3. Validate against a reference implementation — establish a known-good baseline and confirm each generated variant against it, not only against functional tests.
4. Apply static security analysis as a blocking gate — do not treat security scanning as optional review material; make it a mandatory step before code enters the repository.
5. Preserve the effective configuration and outcomes per delivery — keep the configuration, raw output and measured variation so that any decision can be reconstructed later for audit or incident response.

How should supplier contracts and model updates be handled?

Newer models increasingly phase out sampling parameters, which means a model update can silently change the behaviour of existing workflows. You cannot assume that a parameter you control today will remain available tomorrow. A supplier contract should include a notification duty for model and parameter changes, tied to a defined reassessment period. When a model is updated or a parameter is removed, your team must re-run validation and re-measure variation before deploying the new configuration to production.

What tooling can and cannot do

Automated tools can measure variation, run repeated generations, apply static analysis and preserve audit evidence. They cannot determine which level of reproducibility your use case requires—deterministic bit-identical output, a reproducible pipeline with validated outcomes, or semantically equivalent code that works but may be written differently each time. That distinction depends on your risk tolerance, your regulatory obligations and your operational context. A CISO must define in the acceptance policy which standard applies to each workflow. A director must require that the effective configuration and measured spread are preserved so that decisions can be reconstructed. Those judgements remain yours.