ESM-3
Generative Protein Language Model
EvolutionaryScale (Meta spinoff) released ESM-3 in June 2024 — a 98-billion-parameter protein language model that handles sequence, structure, and function in a unified framework. Unlike AlphaFold, ESM-3 is generative — it can DESIGN new proteins, not just predict the shape of existing ones.
Cost
Free
Open weights — self-host
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Made high-quality generative protein design open. Combined with Baker Lab's RFdiffusion + ProteinMPNN, established the open- source protein-engineering toolkit that competes with closed Google offerings.
Core Capabilities
Science
Built for biology, chemistry, materials, weather, or math research.
Generative
Produces images, video, audio, or other media.
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
Context window not disclosed.
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host What it feels like
- Language model from Meta AI — see the linked sources below for benchmark and review coverage
Best use cases
- General-purpose tasks within Meta AI's deployment footprint
- See the model spec and sources block for benchmarked use cases
Tools to try
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher