How to compare AI models without flattening the facts
Model comparison works better when capabilities, modalities, releases, and provider access stay visible as separate facts.
“Which model should I use?” is rarely answered by one score. A useful comparison needs to show what each model is, what it can process, which release is being compared, and where it is available.
Keep the dimensions separate
ModelRegistry treats these as different pieces of catalog data:
- Release tells you which version you are evaluating and when it was published.
- Input and output modalities show whether the model accepts or produces text, images, audio, video, or other formats.
- Capability tags describe supported work such as reasoning, tool use, structured output, or embeddings.
- Provider offerings show which providers expose the version and how their external IDs map back to the canonical model.
Separating these dimensions prevents a missing provider offering from being mistaken for an unavailable model, and prevents a capability label from being confused with a benchmark result.
Comparison is a working space
The comparison page is designed for iterative research. Start with one model, open the add-model dialog, search the catalog, and add another version without leaving the comparison context. The page keeps the selected models side by side so differences are easier to scan.
Try the model comparison workspace with the current public catalog.