BioMap vs Earendil Labs (2026)

AI Health Index verifiedOctober 9, 2026
Verdict

Both are AI native biologics companies that signed major collaborations with Sanofi, and they sit at different points on the path to a drug. BioMap builds xTrimo, a large protein focused language model, and uses it to find targets and design biologics, co developing AI modules with partners rather than carrying its own named pipeline. Earendil Labs uses a foundational model to design antibodies and then develops them through its own pipeline, licensing individual programs out. Its lead, HXN-1001, is described as ready for Phase 2, and its Sanofi agreements run into the billions in potential value. BioMap is the one for a partner that wants model driven target finding and biologics design built into its own work. Earendil is the one for a partner that wants to license antibody programs a platform has already advanced.

The case for BioMap
  • A large protein focused language model, xTrimo, used to find targets and design biologics.
  • A co development model that builds AI modules into a partner's drug discovery.
  • A Sanofi collaboration worth more than a billion dollars in potential payments.
The case for Earendil Labs
  • A foundational model that designs antibodies carried forward through an in house pipeline.
  • Named programs, including HXN-1001 described as ready for Phase 2, available to license.
  • Sanofi agreements worth up to $1.8 billion and a broader deal worth up to $2.56 billion.

This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. BioMap and Earendil Labs are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded

At a Glance

Plain facts

Fact BioMap Earendil Labs
Primary category Drug Discovery AI Drug Discovery AI
Founded 2020 Not recorded
Headquarters Menlo Park, California, United States Wilmington, Delaware, United States
Website biomap.com earendil.bio
Attribute Matrix

Side by Side

Axis
B
BioMap
E
Earendil Labs
AI Centrality
Autonomy and Oversight Model
Model and Technology Transparency
Model Supply Chain Disclosure
Clinical and Operational Evidence
AI Safety and PHI Stewardship
HIPAA and BAA Posture
Security Certifications and Trust Center
FDA and Regulatory Status
AI Governance and Bias Disclosure
AI Liability and Recourse
EHR and Interoperability Depth
Deployment Model and Data Residency
Commercial Transparency
Setting and Specialty Coverage
Citable Summaries

Each record in one paragraph

Written to be quoted whole. Each paragraph states what the AI Health Index verified about the vendor, with the caveats attached. Generated from this pair’s live capability grades, so it moves when a grade moves.

BioMap

The AI Health Index awards BioMap its top capability grade on AI Centrality. Set against Earendil Labs, BioMap grades higher on Model and Technology Transparency. Its thinnest published disclosure sits on several axes, including AI Safety and PHI Stewardship, HIPAA and BAA Posture and Security Certifications and Trust Center. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.

Source: AI Health Index, October 2026

Earendil Labs

The AI Health Index awards Earendil Labs its top capability grade on AI Centrality. Set against BioMap, Earendil Labs grades higher on Setting and Specialty Coverage. Its thinnest published disclosure sits on several axes, including AI Safety and PHI Stewardship, HIPAA and BAA Posture and Security Certifications and Trust Center. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.

Source: AI Health Index, October 2026

FAQ

Questions buyers ask

Should we choose BioMap or Earendil Labs?

On the axes where the AI Health Index separates them, BioMap grades higher on Model and Technology Transparency, and Earendil Labs grades higher on Setting and Specialty Coverage. Neither leads on the greater share of scored axes, so the decision turns on which constraint is binding rather than on an overall winner.

Where do BioMap and Earendil Labs differ most?

The widest separation the AI Health Index records between BioMap and Earendil Labs is on Model and Technology Transparency, where BioMap grades B and Earendil Labs grades C. That axis sits in the AI Capability group, so it should carry the most weight for a buyer whose binding constraint is how much of the work the model itself is trusted to do.

Where do BioMap and Earendil Labs grade the same?

The AI Health Index grades BioMap and Earendil Labs the same on several axes, including AI Centrality, Autonomy and Oversight Model and Model Supply Chain Disclosure. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.

What have BioMap and Earendil Labs not disclosed?

At the last review, at least one of BioMap and Earendil Labs published thin or absent detail on several axes, including AI Safety and PHI Stewardship, HIPAA and BAA Posture and Security Certifications and Trust Center. The AI Health Index treats an absent disclosure as a gap in the public record rather than a failure of the product, so these are the axes to get in writing during diligence instead of inferring from the grade.

Keep Comparing

Related comparisons

Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Drug Discovery AI page.

Disclosure

Both records were reviewed in October 2026. Neither publishes a price, and both earn through partnerships, with Earendil also licensing its own programs. Both publish deal announcements rather than methods.

Ask both for evidence of how their models perform on a target like yours, and which programs have advanced and how far.