Ranking of the currently most reliable AI systems for tax, medicine and law.

Because they measure something else. The big public leaderboards mostly evaluate generic tasks — multiple-choice knowledge (MMLU), chat preference by gut feeling (LMArena), coding tasks, almost all in English. The AI-Roundtable Leaderboard measures something very narrow and concrete: German-language professional cases from tax law, medicine, law and business law, against manually defined target facts, checked source-label-blinded by three judge families.

A model that tops a generic English best-of list can perform differently on a § 8c-KStG client case or a differential diagnosis — which is exactly what our domain breakdowns show. Three questions decide whether two rankings are comparable at all: what is being measured, on which task type and language, and at which date (model versions change rapidly — hence our monthly rhythm). Our answers to these three questions are disclosed and verifiable in the audit trail; that is the real difference.

On top of that, manual scientific studies often face the problem that the period between conducting the study and publishing the (usually sobering) results can be very long — sometimes more than 1.5 years. That means the study publications you may have just read about in the news largely refer to AI models that were state of the art more than 12 months ago. In today's IT epochs that is literally light-years. But above all, that leaves the user still none the wiser in this very second about what the best model would be RIGHT NOW. That is precisely why our leaderboard ranking measures all the popular top models every month. Only that way do we create real, timely transparency and, above all, trend curves with practical value.

For demanding cases: often yes — and that is exactly how we position the leaderboard. The professional remains, for now, the benchmark and the final authority. Our own figures say the same: even the oracle (picking the best model for every question in advance — not achievable in practice) stays below 100 points across all domains. There is a hard remainder that currently no model solves (why a number per model is misleading).

Here AI is a tool for support and a second opinion, not a substitute for professional judgement. The leaderboard helps you choose the most reliable available tool for the next two weeks — substantive responsibility, the counter-review and the final decision remain with the licensed professional. The ranking values are methodically derived guidance, expressly not legal, tax or medical advice.

That said, a further everyday honesty must necessarily be acknowledged: how likely it is that one will reach, with a personal question, a human expert who can actually give "the" desired correct and well-founded answer is equally uncertain. Because not every person is in fact an unrestricted expert in their field. Example: our Roundtable AI model would have passed the Swiss state law examination in every run with a final grade of 1.x on the first attempt. How many people manage that too? In our experience, few. And even fewer are definitively better than an AI — for now. How likely it is that you will land with your matter at an actual expert, the reader may better judge for themselves.

In practice, in five steps:

• Choose a model per domain. For the next task, use the model currently ranked highest for your domain (tax / medicine / law / business law) — not "the best model" in general.
• Don't trust a single model. On important questions, have several models check each other; the gap to the oracle shows that every fixed solo model has gaps.
• Treat the output as a draft. Every answer is preparatory work; the professional counter-review remains mandatory — especially with a high score, which can feel deceptively safe.
• Read the hallucination rate alongside it. It calibrates how skeptically you should check citations, case numbers and figures.
• Check again every month. The recommendation from three months ago is often the wrong one today — model versions shift the order.

There are several effective levers that can be combined:

• Have several independent models cross-check each other. The principle behind triple-judge and the AI-Roundtable app: a finding supported by several models from different families is more dependable than the statement of a single model.
• Bind answers to sources. Instead of free generation, have the model work with real documents and citations (retrieval, document lookup, web/URL fetch) and counter-check every cited reference.
• Human in the loop. Final professional review by the licensed professional remains the most important safety anchor.
• Demand sources and verify them. Explicitly ask for the case number, statute, guideline — and check whether they really exist.
• Choose the right model for the domain — according to the current ranking, not out of habit.
• Pseudonymize sensitive data before a model ever sees it (safeguarded in the app via the client mode, Mandatsmodus).

Both are possible — a hallucination cannot be ruled out entirely, but it can be detected and significantly reduced.

Detecting:

• Consensus across several models. A fact claimed by only a single model and not backed by the others is suspect.
• Source check. Verify whether the cited case number, statute or referenced study really exists — fabricated citations are the most common case.
• Machine extraction. Our judges pull every fabricated claim (wrong case no., invented statute, non-existent study) verbatim out of the answer and publish the aggregated hallucination rate per run.

Reducing:

• Bind to real sources (retrieval / tool use): the model reasons over presented documents instead of from memory.
• Model debate, in which the models pin each other down on unsupported statements — exactly the procedure of the AI-Roundtable app.
• Demand and verify citations as well as tightly scoped, well-posed prompts instead of open "tell me about …" questions.

More on hallucination capture in the scoring procedure: Methodology → scoring.

Before the leaderboard went into operation in April 2026, the scoring pipeline was tested in a multi-month validation study across four professional fields. The current run parameters were only frozen after passing this cross-domain validation.

The methodological findings established during the validation phase (e.g. Mistral's tax weakness as a domain-stratification effect, the expert-roles lift in BFH, inter-judge stability after tier-1 prompts) feed into the methodology of the running leaderboard and are traceably documented in the full methodology document.

The complete cross-domain validation with all raw statistics, win/loss/tie tables per sub-stratum, limitations and critique response is publicly available as a 10-file gist:

→ Public Validation Gist · 10 files · ~80 sessions cross-domain · as of 2026-05-20

Contains, among others: methodology, legal study (BGH/BFH), medical study (BMJ + MedExpQA), raw-metrics JSON, production roadmap, limitations & threats to validity, critique response, layman abstract. Straight to the limitations file →