Best LLM for Medical Questions: 2026 Benchmarks

What 'best' means for a medical question

A medical question can mean three different jobs. The first is an exam question with four options and one correct answer. The second is a real clinical decision with incomplete history, conflicting results, and a patient in front of you. The third is a patient asking what a lab value means at 11pm. A model can win the first job and still be a poor fit for the other two.

Exam benchmarks measure pattern recall on curated questions. Clinical care requires asking the right follow-up, noticing what is missing, weighing risk, and knowing when to stop. Patient explanation requires plain language, citations, and a clear escalation path. These are different skills, and they are measured by different tests.

That is why a leaderboard alone cannot answer which LLM you should use. It can tell you which model has the strongest published exam result. It cannot tell you whether the product will ask about chest pain before it explains a high cholesterol result, or whether it will keep a copy of your report. Our AI doctor accuracy guide breaks down the same distinction between benchmark scores and clinical behavior.

The published MedQA leaderboard

MedQA is a set of four-option questions written in the style of the US Medical Licensing Examination. It was released by Jin and colleagues in 2021 and has become the default scoreboard for medical language models. The table below uses the numbers each team published, with the caveat that prompting and test conditions differ.

The ordering is useful but narrow. A two-point gap can disappear with a different prompt, a newer test subset, or a single retry. Treat these as reported results under each team's conditions, not as a controlled head-to-head trial.

It also helps to separate foundation models from products. A model is the engine; a product adds retrieval, tools, guardrails, privacy controls, and a user interface. Premedice 4 is a product model, which is why its score sits in the same table but its job is different. For the model-only comparison, see our best AI medical models guide.

Why a 91% MedQA score is not a diagnosis

MedQA gives the model a short vignette and four options. The correct answer is in the list. The model does not have to generate the differential from scratch, decide which question to ask next, or notice that the patient looks unwell. It also does not have to live with the consequence of being wrong.

Real patients arrive with vague symptoms, normal labs that do not fit, and social context that changes the plan. A model that has never been trained to ask 'how long has this been going on?' can still score well on an exam that supplies the duration in the stem. That gap is the difference between test accuracy and clinical usefulness.

There is also a contamination problem. Public benchmark questions can leak into training data, and teams do not always disclose their decontamination method. A high score is evidence of capability, not proof that the model reasoned to the answer.

The practical test is whether the model behaves safely when the answer is not in a list. Ask it to evaluate a vague symptom, and watch whether it asks for duration, severity, and red flags before offering a differential. That behavior is what a patient experiences, and no multiple-choice exam measures it. Our plain-language guide to AI doctors explains the difference between a model, a product, and a diagnosis.

The strongest real-world evidence: AMIE and HealthBench

In 2025, Google published AMIE, a conversational diagnostic system evaluated in a simulated objective structured clinical examination. AMIE reached 59.1% top-1 diagnostic accuracy, compared with 33.6% for primary care physicians in the same simulation. The result was widely reported, and it is genuinely important.

It is also easy to overread. The patients were trained actors, the interaction was text-only, and the physicians were working inside an artificial setup. A simulated OSCE is a better test than MedQA, but it is still not live care with real uncertainty, real time pressure, and real follow-up.

HealthBench, released by OpenAI in 2025, takes a different approach. It uses thousands of realistic health conversations scored against physician-written rubrics covering accuracy, safety, communication, and completeness. That is closer to what patients experience, but it measures conversation quality, not whether the patient got better.

Read both studies together and a pattern appears. The more realistic the test, the smaller the gap between AI and clinicians, but the more important the human parts become. AMIE's communication scores were strong, yet the study still relied on actors and a controlled script. The next generation of trials will need real patients, real follow-up, and a clear definition of what counts as a good outcome.

Where Premedice 4 fits

Premedice 4 is the model behind our patient product. It reports 84.6% on an internal replication of MedQA. We publish that number with a plain label: internal, not peer-reviewed, and not directly comparable to the published scores above. We do not claim to top the leaderboard.

What we optimize for is the patient-facing job. Premedice 4 runs with medical tools for PubMed, OpenFDA, RxNorm, DailyMed, and ClinicalTrials, so an answer can be grounded in a source rather than recalled from training. It accepts lab reports, explains them in plain English, and keeps the report's own reference ranges instead of guessing.

The limits matter as much as the features. Premedice is not a diagnosis, not a substitute for a clinician, and not an emergency service. If you have chest pain, trouble breathing, sudden weakness, or a severe allergic reaction, call emergency services instead of typing into any chatbot.

For the lab-report job specifically, the product has to do more than summarize. It has to respect the report's own units and reference ranges, notice when a value is high for one lab but normal for another, and explain what the pattern could mean without pretending to diagnose. That is the workflow our blood test interpretation guide compares across tools, and it is where a medical product earns its place over a general chat window.

Best model by use case

The honest answer is a table, not a single name. The best choice changes with the job, the audience, and the risk of being wrong. Use the shortest column that matches your actual task.

How to read a benchmark claim without being fooled

Most benchmark headlines omit the conditions. A score without the test set, the prompt, the number of attempts, and the evaluation method is not reproducible. When a company says its model 'scores 96% on medical exams,' ask which exam, which version, and whether the questions were in the training data.

A credible claim names the benchmark, the date, the evaluator, and the limitation. It also separates a model result from a product result. A model can score well in a lab and still fail in a product that retrieves the wrong source, truncates the report, or hides the uncertainty.

The medical AI market is full of numbers that are technically true and practically misleading. The Defense Department's DIU evaluation, academic leaderboards, and vendor blog posts all measure different things. Treat a single number as a starting point, not a verdict, and look for the sentence that explains what the number does not cover.

A useful rule is to ask what a model would have to do to fail the benchmark. If the answer is 'pick a different letter,' the benchmark is measuring recall, not clinical reasoning. If the answer is 'miss a red flag in a conversation,' the benchmark is closer to the skill that matters.

That rule also helps with vendor claims. A company can be truthful about a high score and still mislead you about what it means. The missing sentence is usually the one that says the model was tested on exam questions, not on patients, and that no clinician reviewed the output in the loop.

How to test an LLM with your own medical question

Pick a question you already know the answer to, ideally one where the obvious answer is wrong. Ask the model without hints. Then check five behaviors. Did it ask a clarifying question? Did it cite a source you can open? Did it name the red flags that would change the urgency? Did it state uncertainty? Did it avoid pretending to diagnose?

A model that fails the red-flag check is not safe for symptom questions, no matter what it scored on an exam. A model that cites a source you cannot verify is asking for trust it has not earned. A model that answers every question with equal confidence is the most dangerous kind.

Run the same prompt twice. Consistency is not proof of correctness, but wild variation on the same question is a warning sign. Then run it in the product you actually plan to use, because the model and the interface are not the same thing. If the product hides its sources or buries the emergency advice, the underlying score does not matter.

Finally, test the handoff. Ask what you should do next and whether the answer changes for a child, a pregnant patient, or someone on blood thinners. A model that gives the same generic advice to every group is not doing a medical assessment. Our AI medical statistics overview tracks how these systems perform across published evaluations, including the studies that show the gap between lab and clinic.

What to ask instead of 'which model is best'

Ask whether the thing you are using is a model or a product. Ask what it does with your data, who reviewed its medical content, and when it was last updated. Ask whether it works in your language and your health system, and what happens when it is wrong.

The best LLM for medical questions is the one that makes its limits legible. A leaderboard can start the conversation. It cannot finish it. For a patient, a cited explanation and a clear next step are worth more than a two-point gap on an exam.

That is also the right frame for the doctor-versus-AI question. The useful comparison is not which system scores higher on a test, but which tasks each does safely. Our AI doctor vs real doctor comparison takes that question apart task by task, from history-taking to procedures to accountability.