# MIT Boltz-2 vs Google DeepMind AlphaFold 3: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: MIT Boltz-1 / Boltz-2 wins as the premier commercially permissive (MIT-licensed) all-atom biomolecular foundation model—matching AlphaFold 3 on PoseBusters RMSD accuracy while adding direct small-molecule binding affinity (ΔG / IC50) prediction at 1,000x faster speed than Free Energy Perturbation (FEP) across 4.3k+ GitHub stars (97.9), while Google DeepMind AlphaFold 3 remains the academic reference benchmark across 8.6k+ GitHub stars (97.7).

- **Category**: MIT Commercial All-Atom Co-Folding & Affinity Model vs Reference Non-Commercial Biomolecular Diffusion Shootout
- **Canonical URL**: https://exactbaseline.com/vs/boltz-vs-alphafold3
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## MIT Boltz-2 Key Advantages
- 4,255+ GitHub stars under a 100% permissive MIT license (both code and model weights), enabling proprietary commercial drug discovery without DeepMind terms-of-use barriers
- Boltz-2 jointly predicts 3D all-atom protein-ligand co-folding AND quantitative binding affinity (Ki/Kd/IC50) in 18 seconds on a single A100 GPU
- Supports custom MSA servers (ColabFold MMseqs2), covalent ligand modifications, and pocket-conditioned distance constraints out of the box

## Google DeepMind AlphaFold 3 Key Advantages
- 8,649+ GitHub stars as Google DeepMind and Isomorphic Labs' Nature reference architecture replacing Evoformer with the streamlined Pairformer + direct coordinate diffusion
- High zero-shot generalization across complex multi-chain RNA, DNA, covalent glycosylations, and metal ion coordination
- Backed by DeepMind's curated PDB cropping and distillation training methodology