RVMD vs SANA

Revolution Medicines, Inc. vs Sana Biotechnology, Inc. — Valuation Comparison 2026

RVMD

Biotechnology
Revolution Medicines, Inc.
Quality
5.1
out of 10
Value Trap
35
LOW
Price
$154.63
Last close
Models
12/13
Active
VS

SANA

Biotechnology
Sana Biotechnology, Inc.
Quality
3.8
out of 10
Value Trap
37
LOW
Price
$3.13
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType RVMD Fair ValueRVMD Upside SANA Fair ValueSANA Upside
Bayesian DCF Intrinsic $53.27 -65.5% $0.89 -71.4%
Earnings Power Value Intrinsic $64.04 -52.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $3.00 -98.1% $0.85 -72.9%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for RVMD vs SANA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RVMD vs SANA — Which Stock Is More Undervalued?

RVMD scores higher with a 5.1/10 quality rating vs SANA's 3.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Revolution Medicines, Inc. (RVMD) and Sana Biotechnology, Inc. (SANA) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

RVMD currently trades at $154.63 with a QOC of 5.1/10, while SANA trades at $3.13 with a QOC of 3.8/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).