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BillionToOne, Inc (BLLN) Fair Value & Analysis

Healthcare · US · Market cap $4.5B

Price$104.28
Fair Value$14.37
Upside-86.2%
Quality87/100
Evidence: Medium Range $12.86 – $15.87

Analysis

BillionToOne, Inc (BLLN) currently trades at $104.28, while our model-based Fair Value estimate is $14.37 — implying the stock looks roughly 86.2% overvalued today. We read business quality at 87/100 (high quality), in the Healthcare sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).

About the company

BillionToOne, Inc., a precision diagnostics company, quantifies biology to create molecular diagnostics. The company provides molecular counting platform, which is designed to detect and measure DNA molecules at the single-count level to help enhance disease detection. It also offers UNITY Complete, a non-invasive prenatal screen that assesses fetal risk for aneuploidies, recessive conditions, and fetal antigens from a maternal blood draw; Northstar Select, a liquid biopsy test that provides a list of mutations that are present in the tumor and types of mutations detected that include deletions, insertions, and point mutations; and Northstar Response, a methylation-based assay that quantifies the amount of cancer at the single molecule level without requiring a tissue biopsy, enabling real-time monitoring of patient response to therapy with unprecedented precision. BillionToOne, Inc. was incorporated in 2016 and is headquartered in Menlo Park, California.

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How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.