Evidence Comparison

AlphaFold 3 vs Chai-1: Evidence Comparison

How do AlphaFold 3 and Chai-1 differ for multimolecular structure prediction?

BioAtlas does not create a universal winner. The useful choice depends on scientific task, inputs, access, evaluation protocol, context of use, uncertainty and required validation.

Structure Prediction

AlphaFold 2 / 3

The model that solved the 50-year protein-folding problem.

OrganizationGoogle DeepMind

AccessLimited open access

DeploymentHybrid

Evidence coverage4/7 evidence fields documented

BenchmarkCASP14 / complex evaluations

Metric / taskStructure accuracy and confidence

Inputs

Biomolecular sequences · Ligand / ion identities · Optional templates and MSA depending on release

Outputs

3D biomolecular structures · Confidence estimates

Known limitations

Performance depends on the evaluation dataset and operating conditions.
Task-specific benchmark results should not be compared across unlike domains.
Outputs require task-specific scientific and experimental validation.

Open full passport

Structure Prediction

Chai-1 / Chai-2

An AlphaFold3-class complex predictor, made freely usable.

OrganizationChai Discovery

AccessLimited open access

DeploymentHybrid

Evidence coverage4/7 evidence fields documented

BenchmarkComplex and antibody-design evaluations

Metric / taskStructure / design performance

Inputs

Biomolecular sequence / complex context · Target or epitope context for design releases

Outputs

Complex structures · Designed antibody / binder candidates

Known limitations

Performance depends on the evaluation dataset and operating conditions.
Task-specific benchmark results should not be compared across unlike domains.
Outputs require task-specific scientific and experimental validation.

Open full passport

Benchmark comparability · none

Not directly comparable

The claims evaluate different scientific tasks.

A direct comparison requires aligned task, dataset, metric and split/protocol context. Otherwise BioAtlas treats the evidence as partial or contextual rather than manufacturing a winner.

AlphaFold 2 / 3 claim →Chai-1 / Chai-2 claim →

Decision rules

What should determine the choice?

Scientific task

Confirm that both systems are being evaluated for the same task. Structure, pose, affinity and design claims are not interchangeable.

Protocol comparability

Only compare benchmark results when dataset, split, metric and implementation conditions align closely enough to support the comparison.

Access and reproducibility

Code, weights, API access, commercial terms and deployment constraints can materially change whether a model is usable in a governed programme.

Validation plan

Use the comparison to design the next validation step—not as a substitute for prospective evaluation on your own scientific problem.

Open interactive comparison Evidence methodology

Why?

Why might one model be chosen over the other?

Why this model?

Choose the model whose task, access constraints, evidence and deployment fit the actual scientific decision—not the one with the most impressive headline metric.

Why not the alternative?

A model can be scientifically strong yet inappropriate when its benchmark context, licensing, inputs, reproducibility or validation burden does not match your programme.

What evidence is missing?

If benchmark comparability is partial or contextual, the next step should be a matched evaluation on the same data, protocol and decision-relevant endpoint.