Models in this group take text and return embeddings or task-specific labels. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
GatorTron (base 345M, medium 3.9B, large 8.9B) / GatorTronS
Vendor: University of Florida with NVIDIA
What it does: was trained on a 90-billion-word real clinical corpus, and it shows: best-in-class on clinical entity and relation extraction rather than on published literature, which is the distinction that matters for hospital text.
Best-in-class on clinical NER, relation extraction, semantic textual similarity and NLI over a 90-billion-word real clinical corpus (independent, npj Digital Medicine).
BioBERT v1.2
Vendor: DMIS Lab / Korea University
What it does: is the long-standing biomedical encoder — a small, cheap and well-characterised base for literature-facing extraction.
Plus 0.6–2.8 F1 over BERT on biomedical NER, relation extraction and QA (independent).
Bio_ClinicalBERT / BioClinicalBERT
Vendor: MIT CSAIL / Alsentzer et al.
What it does: remains the default clinical baseline at F1 0.87–0.90 on the standard entity sets. Larger encoders beat it, and it is the sensible first benchmark before paying for them.
Strong baseline on i2b2 NER, F1 about 0.87–0.90; superseded by larger encoders but still the default clinical baseline (independent).
ClinicalBERT
Vendor: ClinicalBERT authors
What it does: encodes clinical notes for prediction and classification tasks, at a footprint small enough to run over a whole archive.
Task-dependent; performance varies by clinical prediction and NLP dataset.
Vendor: Microsoft Research
What it does: is top-tier on the biomedical NLP benchmark suite and the better choice than a clinical encoder when your text is published literature.
Top-tier on the BLURB benchmark, average about 81–83 (independent).
BioLinkBERT
Vendor: BioLinkBERT authors
What it does: brings document links into the representation, which helps on biomedical question answering across connected sources.
Task-dependent; evaluated on biomedical QA and NLP benchmarks.
Vendor: Yale and community
What it does: reads up to 4096 tokens, so a whole admission record fits in one pass. That is worth 2–5 points on readmission and phenotyping, where truncation loses exactly the part the model needs.
Plus 2–5 points over ClinicalBERT on long-document tasks such as readmission and phenotyping (independent).
Clinical-T5
Vendor: Clinical-T5 authors / PhysioNet
What it does: generates as well as encodes, which suits summarisation and structured rewriting of clinical text in the same deployment.
Task-dependent; evaluated on MIMIC clinical text tasks.
BioGPT
Vendor: Microsoft Research
What it does: generates biomedical text and extracts relations, for literature-facing pipelines rather than clinical notes.
Task-dependent; evaluated on biomedical relation extraction and text-generation benchmarks.