GRFS vs LLY

Grifols, S.A. vs Eli Lilly and Company — Valuation Comparison 2026

GRFS

Drug Manufacturers - General
Grifols, S.A.
Quality
1.7
out of 10
Value Trap
Price
$7.98
Last close
Models
13/13
Active
VS

LLY

Drug Manufacturers - General
Eli Lilly and Company
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$1126.80
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType GRFS Fair ValueGRFS Upside LLY Fair ValueLLY Upside
Bayesian DCF Intrinsic $2.36 -70.5% $118.75 -89.5%
Earnings Power Value Intrinsic $3.00 -62.8% $192.34 -82.9%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

GRFS vs LLY — Which Stock Is More Undervalued?

LLY scores higher with a 10.0/10 quality rating vs GRFS's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Grifols, S.A. (GRFS) and Eli Lilly and Company (LLY) 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.

GRFS currently trades at $7.98 with a QOC of 1.7/10, while LLY trades at $1126.80 with a QOC of 10.0/10.

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).