Tether's Medical AI Runs on Your Phone and Outperforms Models 16x Its Size

The headline number: a tiny 1.7 billion-parameter model capable of beating Google's MedGemma-4B on medical benchmarks despite being less than half its size. On HealthBench Hard—OpenAI's benchmark that evaluates AI on realistic, multi-turn clinical conversations graded by 262 physicians—Tether says its 1.7 billion-parameter model outscores MedGemma-27B, a model nearly sixteen times larger.

Parameters are all the configurations and values that a model learns during trading. The more the parameters, the better the model should be, in theory.

The test suite spans MedQA-USMLE, which measures clinical knowledge using US medical licensing exam-style questions scored as percentage accuracy, all the way to AfriMedQA, which tests performance specifically for underserved African healthcare contexts.

Tether CEO Paolo Ardoino credited the gains to efficiency rather than scale. "With QVAC MedPsy, our focus was improving efficiency at the model level, rather than scaling up size," he said in a statement. "Our 4 billion model exceeded results from models nearly seven times its size, while using up to three times fewer tokens per response."

That token efficiency is the other headline. The 4B model averages around 909 tokens per response versus 2,953 for comparable systems—a 3.2x reduction. Fewer tokens means lower compute cost, faster responses, and crucially, the ability to run locally without a cloud backend.

"You can run medical reasoning where the data already exists, inside a hospital system or on a device, without moving sensitive information through the cloud or waiting on external processing," Ardoino said.

The models ship as quantized GGUF files—1.2 GB for the 1.7 billion-parameter model and 2.6 GB for the 4 billion—with compressed versions retaining most benchmark performance while fitting on standard consumer hardware. That means a hospital system, rural clinic, or individual clinician could run the model entirely on-device, keeping patient records out of third-party cloud infrastructure and away from HIPAA exposure.