Introducing the Tamarind Model Router.

You want to know which molecular AI model will work best for this specific input, not which model wins on average.

Tamarind runs hundreds of scientific AI models, and picks the best tool for the job.

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Introducing the Tamarind Molecular Model Router

Source: https://www.tamarind.bio/blog/introducing-the-tamarind-molecular-model-router What molecular AI model is best?

Introducing the TamarindModel Router

Benchmark leaderboards help. But the model that performs beston averageis not necessarily the model that will perform best on your specific input. And now that there’s dozens of tools for any given task, it’s increasingly difficult to know which to pick.

Two leading structure prediction models can take the exact same complex and return meaningfully different structures. And before running the experiment, there often isn’t an obvious, human-interpretable rule that tells you which prediction to trust.

PDB 9F91 is an antibody-antigen complex. For this modality, models including OpenDDE, Protenix-v2, and ESMFold2 perform particularly well according to recent benchmarks.

If you simply looked at aggregate benchmark performance, you might start with one of those models.

But for this particular complex, OpenFold3 produces the best prediction.

That’s the routing problem.

As the number of scientific AI models grows from dozens to hundreds (400+ and counting onTamarind), we think model selection itself becomes an important part of the scientific AI stack.

That’s why we built the Tamarind Bio Model Router.

The router combines Tamarind’s independent benchmarking across molecular AI tools with public benchmark data, characteristics of the user’s input, and agentic search.

Instead of forcing a scientist, or an agent, to manually track every new model release and benchmark, Tamarind can route each job toward the model most likely to work well for that particular problem.

Tamarind already provides the infrastructure to run hundreds of scientific AI models across structure prediction, protein design, docking, molecular dynamics, property prediction, and other workloads.

That means the same system that determineswhichmodel to use can provision it, run it, manage the compute, capture the outputs, and make those outputs available to downstream scientists, applications, pipelines, and agents.

When a new model comes out it is automatically benchmarked, ready to be used as part of the Model Router, and your team doesn’t need to run a bespoke custom benchmark for that use case.

Today, we’re making the first Tamarind Molecular AI Model Router available, starting with biomolecular structure prediction.