# We Tested 8 LLMs on Regulated Enterprise Data. Here's What Actually Happened. > Original benchmark data from NeuralSeek's bake-off suite — accuracy, hallucination rate, latency, cost, and confidence calibration across 8 models tested against real regulated-sector knowledge bases. No vendor-supplied benchmarks, just production-representative results. **Category:** Benchmarks **Author:** NeuralSeek Team · **Published:** June 16, 2026 **Canonical:** https://neuralseek.ai/ai-grounded/we-tested-8-llms-on-regulated-enterprise-data **Section index:** https://neuralseek.ai/ai-grounded Every model vendor publishes benchmarks, and every one of them looks spectacular. The problem is that those numbers come from public quiz sets and curated prompts that bear almost no resemblance to what an LLM actually faces inside a regulated enterprise: messy internal documents, domain jargon, strict accuracy requirements, and a low tolerance for confident nonsense. So we ran our own. Using NeuralSeek's built-in LLM bake-off, we tested 8 models against real regulated-sector knowledge bases — the kind of production-representative data that a bank, insurer, or healthcare firm would actually deploy against — and measured the five dimensions that decide whether a model is fit for that work: accuracy, hallucination rate, latency, cost per call, and confidence calibration. No vendor-supplied numbers. Here's what actually happened. ## Why public benchmarks don't survive contact with regulated data A leaderboard score tells you how a model performs on questions whose answers are already on the open internet. Regulated enterprise work is the opposite: the answer lives in a private, often poorly structured knowledge base, the question is phrased in the firm's own dialect, and a wrong answer carries compliance and customer consequences. That gap is why a model that tops a public benchmark can still hallucinate against your own policy documents. The only benchmark that matters is the one run against representative versions of your data — which is exactly what a Built-in LLM bake-off is designed to produce, side by side, under identical conditions. ## Accuracy: the spread was wider than the leaderboards suggest On public tests, top models cluster within a point or two of each other. Against regulated knowledge bases, the field fanned out dramatically — the best models answered correctly far more often than the weakest, and the ranking did not match public leaderboard order. The lesson is blunt: you cannot infer real-world accuracy from a marketing chart. Run the Accuracy comparison on your own corpus, because the model that wins on Trivia may not be the one that reads your underwriting manual correctly. > The model at the top of the public leaderboard was not the model that read our regulated knowledge base most accurately. They were not even close. ## Hallucination rate: where 'impressive' models fell apart Hallucination — inventing facts not supported by the source — is the single most disqualifying behavior in a regulated setting, and it's where the field separated most sharply. Some models stayed tightly grounded, declining to answer rather than guessing; others fabricated plausible, well-written, completely wrong responses at an alarming rate. A Hallucination rate comparison turns this from a vibe into a number you can govern against, and it is the metric we'd weight most heavily for anything touching customers or filings. ## Latency and cost: the trade-offs nobody advertises Accuracy isn't free. The most grounded models were not always the fastest, and the fastest were not always the cheapest. Under production-representative load, a Latency comparison exposed real differences in time-to-usable-answer, while a Cost-per-call comparison showed spend-per-answer varying by multiples across the field. For a high-volume deployment, those gaps compound into very different monthly bills and very different user experiences — which is why both belong in the decision alongside accuracy, not after it. ## Confidence calibration: the metric that decides if you can trust the rest The most underrated result was calibration: whether a model's stated confidence actually matched its real accuracy. A well-calibrated model that says 'high confidence' is usually right, so you can route low-confidence answers to a human. A poorly calibrated one is confidently wrong, which is the worst possible failure mode in a regulated workflow because nothing flags it. A Confidence comparison is what lets you build automated guardrails on top of the model at all — without it, every answer has to be treated as equally suspect. ## No single winner — and that's the point Across the five dimensions, no model swept the board. The most accurate wasn't the cheapest; the fastest wasn't the best-calibrated; the cheapest-per-call hallucinated more than we'd accept for customer-facing work. The right choice is the one whose trade-offs fit your specific regulated workload — and the only way to know that is to see all five metrics at once, on your own data, and hand the result to whoever signs off on the deployment. That's why the bake-off produces Exportable comparison reports: the evidence travels with the decision. If you take one thing from this: stop choosing models from vendor charts. Run your own bake-off against representative regulated data, look at accuracy, hallucination, latency, cost, and calibration together, and export the result. The model that wins on paper is rarely the one that wins on your documents. **Run your own bake-off** - [Built-in LLM bake-off](https://neuralseek.ai/ai-grounded/llm-bake-off) - [Accuracy comparison](https://neuralseek.ai/ai-grounded/accuracy-comparison) - [Hallucination rate comparison](https://neuralseek.ai/ai-grounded/hallucination-rate-comparison) - [Latency comparison](https://neuralseek.ai/ai-grounded/latency-comparison) - [Cost-per-call comparison](https://neuralseek.ai/ai-grounded/cost-per-call-comparison) - [Confidence comparison](https://neuralseek.ai/ai-grounded/confidence-comparison) - [Exportable comparison reports](https://neuralseek.ai/ai-grounded/exportable-comparison-reports) --- From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.