SCLX vs SNY

Scilex Holding Company vs Sanofi — Valuation Comparison 2026

SCLX

Drug Manufacturers - General
Scilex Holding Company
Quality
4.7
out of 10
Value Trap
44
WARN
Price
$7.21
Last close
Models
5/13
Active
VS

SNY

Drug Manufacturers - General
Sanofi
Quality
7.3
out of 10
Value Trap
6
SAFE
Price
$44.29
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType SCLX Fair ValueSCLX Upside SNY Fair ValueSNY Upside
Bayesian DCF Intrinsic $33.36 +463.5% $81.01 +82.9%
Earnings Power Value Intrinsic $30.08 -32.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $13.06 +81.2% $84.09 +89.9%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

SCLX vs SNY — Which Stock Is More Undervalued?

SNY scores higher with a 7.3/10 quality rating vs SCLX's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Scilex Holding Company (SCLX) and Sanofi (SNY) 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.

SCLX currently trades at $7.21 with a QOC of 4.7/10, while SNY trades at $44.29 with a QOC of 7.3/10.

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).