Run the same prompt twice and you may get two different answers. For a casual user that is a curiosity. For a team shipping AI into production, LLM non-determinism is a testing problem: a regression test that passes on Monday and fails on Tuesday cannot tell you whether the model changed or the dice did.
The topic is moving quickly. Over the past year, new research has overturned some comfortable assumptions, including the idea that setting temperature to zero solves the problem. This post covers the latest news and trends in LLM non-determinism, what they mean for how you test, and where structured evaluation fits.
A Quick Recap: Why the Same Input Gives Different Outputs
Our earlier guide, The Reproducibility Problem in LLM Testing: Same Input, Different Output, walks through the classic causes: sampling randomness (temperature, top-k, top-p), non-deterministic backend behavior, silent model updates and prompt sensitivity. It also covers practical fixes such as pinning model versions, logging every run and validating meaning instead of exact wording.
If you are new to the topic, start there. This post picks up where it leaves off: what has changed since, and what the newest evidence says about those fixes.
LLM Non-Determinism News and Trends to Watch in 2026
1. Temperature zero is "necessary but not sufficient"
A June 2026 arXiv study on LLM-as-a-judge safety evaluations ran 690 API calls across seven conditions, two providers and three model tiers, including forced greedy decoding (top-k of 1). Residual non-determinism persisted in every setup. The authors conclude that temperature control is necessary but not sufficient for reproducible grading. They are careful to note that they demonstrate that variance can persist, not a precise rate that applies everywhere.
A May 2026 literature review reaches a similar place from the vendor side: major providers describe reproducibility controls such as fixed seeds as best effort, and none publishes a hard guarantee of identical outputs. A 2026 analysis also points out that Anthropic's own API reference says results at temperature 0.0 will not be fully deterministic.
2. The culprit is shifting from randomness to server traffic
The most influential explanation came from Thinking Machines Lab in September 2025. Floating-point quirks and GPU concurrency were the usual suspects, but the lab argued the deeper cause is a lack of batch invariance: the numerical result for your request can depend on how many other requests the server is handling at the same time, because kernels change their reduction strategy with batch size.
In its experiment on Qwen3-235B, 1,000 completions of one prompt at temperature 0 produced 80 unique outputs, with the first divergence at token 103. With batch-invariant kernels, the completions matched. Open-source inference engines such as vLLM and SGLang have since added batch-invariant approaches.
The practical takeaway: in principle, the same prompt can behave differently on a busy afternoon than in a quiet hour, even with identical settings.
3. Determinism has a price tag
If batch-invariant kernels fix the problem, why is not everyone using them? A January 2026 paper, LLM-42, found that forcing an entire batch through batch-invariant kernels because a single request needs determinism cut throughput by 56% in its tests. The authors observe that deterministic inference is largely confined to debugging and verification today, and propose a verify-and-rollback approach that charges the cost only to traffic that needs determinism.
Expect deterministic options to keep evolving. For now, teams consuming hosted APIs should plan to manage variability rather than eliminate it.
4. Evaluation is moving from single runs to distributions
If one run is not trustworthy, the logical response is to run more than one. Practitioner guides in 2026 increasingly recommend N-rollout evaluation: run each test prompt several times and report the mean and variance instead of a single pass or fail. Seeds and system fingerprints help, but they are treated as best effort rather than proof.
One vendor-run benchmark shows why. Determinism rates ranged from 100% on short, constrained prompts to 0% on longer open-ended ones, and differed by model and deployment. Treat that as directional rather than universal, since it is a single test with a small number of runs.
5. LLM judges are non-deterministic too
Many teams now score outputs with another LLM, a pattern known as LLM-as-a-judge. That moves the variance problem rather than removing it: judges can return different scores for the same output, as the June 2026 study above shows. Two further findings shape current practice. A 2025 empirical study found that averaging several sampled judge scores aligned better with human judgments than a single greedy-decoded score. And Arize's experiments found that judge models tended to rate outputs from their own model family more favorably, a self-preference bias.
The emerging pattern is a hybrid: deterministic checks first for what code can verify (valid JSON, required disclaimers), an LLM judge for semantic quality, and human review to calibrate both.
6. Agents multiply the variance
As teams move from single prompts to agents that plan, call tools and take multiple steps, small differences can compound: different wording in step one can lead to a different tool call in step three. This is an implication of the research above rather than a measured result from it, but it is a strong reason multi-run, scenario-based testing is gaining ground for agentic systems.
What LLM Non-Determinism Means for Your Testing Strategy
Taken together, the trends point to a shift from chasing identical outputs to building a repeatable process around variable ones:
• Stop asserting exact strings. Assert on meaning, structure and policy: does the answer contain the required facts, follow the format and respect the rules?
• Report distributions, not single results. Run critical tests multiple times, track pass rate and spread, and set thresholds on the distribution.
• Pin and log everything. Record the model version, parameters, seed (where supported), system fingerprint (where exposed), prompt version and timestamp for every run. Re-baseline whenever a provider updates a model.
• Layer your graders. Use deterministic checks as the floor and a calibrated LLM judge above it. Where possible, use a judge from a different model family than the one being tested.
• Treat widening variance as a signal. If the spread in pass rates grows after a model update, that is a finding worth investigating, not noise to ignore.
• Keep an audit trail. In regulated sectors, being able to show how a result was produced and evaluated increasingly matters more than producing identical text.
For the foundational techniques behind several of these points, including golden datasets, prompt versioning and output validation, revisit the how-to-handle section of our reproducibility guide.
Where Structured Evaluation Fits
Every trend above points the same direction: a repeatable process beats a repeatable output. Trusys TruEval is built around that idea, offering structured, repeatable evaluation with logged, versioned and traceable runs, reusable prompt and dataset suites, and metrics covering accuracy, hallucination, safety and reliability. Teams can connect LLMs, RAG systems, voice models or agentic applications and compare results across model and prompt changes over time.
Frequently Asked Questions
Does temperature 0 make an LLM deterministic?
No. Temperature 0 removes sampling randomness, but serving-side factors such as batch size and kernel behavior can still change results between runs. A June 2026 study measured residual non-determinism even with forced greedy decoding, and vendor documentation describes reproducibility controls as best effort.
Why can the same prompt give different outputs at different times?
Besides sampling and silent model updates, the numerical result can depend on how many other requests a server is processing at the same moment. Thinking Machines Lab identified this lack of batch invariance as a primary reason hosted endpoints look non-deterministic.
Can I get fully deterministic outputs from a hosted API?
Generally not with a guarantee. Seeds and system fingerprints improve repeatability but are treated as best effort. Self-hosted inference engines with batch-invariant kernels can produce identical outputs, usually at a throughput cost.
How many times should I run each test?
There is no universal number. Practitioner guides recommend running each prompt several times and reporting mean and variance. Choose the count based on how variable the task is and how expensive each run is, then adjust as you observe the spread.
Are LLM judges reliable if they are non-deterministic too?
They are useful but need calibration. Averaging several judge scores, using deterministic checks for anything code can verify, checking for self-preference bias, and spot-checking against human review all make judge-based scores more dependable.
What is the first step if my LLM tests are flaky?
Log the model version, parameters and prompt version for every run, then re-run the failing cases several times to measure the spread. Once you know how much variance is normal, move assertions from exact-match to meaning-based checks.
Managing LLM Non-Determinism Is the New Baseline
The headline for 2026 is that LLM non-determinism is not a bug awaiting a patch. It is a property of how models are served at scale, and the research now shows that the usual fixes reduce it without removing it. Teams that adapt, by measuring distributions, layering graders and logging every run, will ship more reliable AI than teams waiting for perfect reproducibility.
If you have not read it yet, our guide to the reproducibility problem in LLM testing is the best companion to this post. And if you want to see how structured evaluation could work for your own AI systems, book a demo with the Trusys team.
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