Open source protein models: OpenFold vs Boltz
Today, there are two open source, transformer-based protein folding models that seem to get the most attention. They both use AlphaFold as a starting point, but with some important differences after that. This week I want to explore where these models came from and what makes them different.
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Teams and Timelines
The story starts when Google’s DeepMind released AlphaFold2 in November 2020. They released the model’s weights and the code for running inference as open source on GitHub, but kept the code for training the model private. This meant that while anyone could use the open source code to generate predictions, they couldn’t train their own version of the model or fine tine the weights using proprietary data.
To address this limitation, the OpenFold foundation was founded in early 2022 as a non-profit backed by both academic and industry/pharma partners. Their goal was to build a fully open source implementation of AlphaFold2, called OpenFold2. Since then, they have built a consortium that includes large pharma companies such as Bristol Myers Squibb, Novo Nordisk, Bayer, Roche, and UCB.
After DeepMind released AlphaFold3 in May 2024, the OpenFold foundation began work on OpenFold3, again with the goal of replicating the model with a fully open source implementation. (I’ll describe the technical differences between AlphaFold 2 and 3 in the next section.)
Shortly after that, in November 2024, a group of researchers at MIT released Boltz-1, a fully open source model that took the main ideas from AlphaFold3, but made changes designed to improve its performance. Then in June 2025, they released Boltz-2, a model that added capabilities that weren’t available in AlphaFold3. (More on that below too.)
In January 2026, the developers of the Boltz model founded a public benefit corporation called Boltz, headquartered in London. This allowed them to raise money from VCs while continuing to maintain the fully open source Boltz models.
Introducing diffusion models
To understand the technical differences between OpenFold and Boltz, we need to start with the differences between AlphaFold 2 and 3. Both models start with a transformer model where tokens represent amino acids to infer spatial and physical relationships between all pairs of amino acids in a protein. Where they differ is how they translate this into a protein structure:
AlphaFold2 uses a deterministic algorithm that translates the pairwise relationships into the final positions of the amino acids. AlphaFold3 uses a diffusion model to predict the locations of individual atoms from the token embeddings. This is similar to how image generation models predict the colors of individual pixels in an image based on the token embeddings of a sentence describing the desired picture. The sequence of amino acids is the sentence. The individual atoms are the pixels.
Roughly, the diffusion model creates embedding vectors for the individual atoms in the same latent space as the amino acid tokens are embedded by the transformer, then applies an attention algorithm to update the atom embeddings based on the embeddings of both the amino acids and the other atoms. This is similar to how a transformer re-embeds tokens, as described in my post about transformers. The model uses the final atom embeddings to predict their positions.
AlphaFold3 also made some changes to the transformer algorithm that put less emphasis on reference structures (MSAs) for inferring the relationships between amino acids, but that’s kind of in the weeds. The important change is the diffusion model.
Boltz and OpenFold
The goal of OpenFold3 is to replicate AlphaFold3 as closely as possible, so there are no major atchitectural differences between OpenFold3 and AlphaFold3. Boltz, however, is intended to improve on AlphaFold, so they made some specific changes to the architecture.
Most of these changes are too technical to go into here, but an important one is how the model interprets the outputs of the diffusion algorithm: In AlphaFold3 and OpenFold3, the predicted positions of the atoms are compared to the coordinates of the reference structure for a fixed position and rotation of the overall protein. However, that rotation and position don’t actually matter to the answer; it’s only the relative positions of the atoms that we care about. So before Boltz-1 compares the predicted positions of the atoms to the reference, it first applies a translation and rotation to the whole structure that gets the atoms as close as possible to the reference positions. That way, if the model predicts the right structure but with the wrong rotation, it still counts it as right. OpenFold3 would count it as wrong.
Because of this change and the other architectural differences, Boltz has performed slightly better than OpenFold3 on certain benchmarks, though the results aren’t completely conclusive.
The bigger difference came with Boltz-2, which introduced the ability to predict binding affinity between small molecules and a target protein. OpenFold3 can only. predict co-folding between the two, i.e. it can predict a stable position of a small molecule and a protein in close proximity. But it doesn’t predict the likelihood of the molecule getting into that position, or how strongly it will bind once there.
This means that Boltz-2 can be used directly for virtual binder screening. OpenFold3 can be part of a virtual binder screen, but only if you couple it with a model that predicts binding affinity of the predicted positions. Though as I argued recently, this isn’t that valuable on it’s own, and both models are probably cheating a little.
So which one is better?
Between OpenFold3 and Boltz-2, there isn’t a clear winner. The difference in accuracy will probably depend on the type of protein(s) you’re looking at, and in many cases may not be big enough to actually matter. The bigger differences are going to be related to what’s involved in actually getting them up and running.
OpenFold is focused on supporting large pharmas like the ones who fund their foundation. So that means providing enterprise support that can work with a large IT organization. Boltz is much more focused on smaller startups, and has worked to make their model as lightweight and cost effective as possible so anyone can get it up and running.
Which one you should use depends on a host of different factors from accuracy to application to ease of use, far too many to go into here. Luckily, since they’re both open source, you can try both and decide for yourself which one is the best fit.
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