PLUR vs SNTI

Pluri Inc. vs Senti Biosciences Holdings, Inc — Valuation Comparison 2026

PLUR

Biological Products, (No Diagnostic Substances)
Pluri Inc.
Quality
5.6
out of 10
Value Trap
29
LOW
Price
$2.35
Last close
Models
10/13
Active
VS

SNTI

Biological Products, (No Diagnostic Substances)
Senti Biosciences Holdings, Inc
Quality
4.1
out of 10
Value Trap
39
LOW
Price
$1.00
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType PLUR Fair ValuePLUR Upside SNTI Fair ValueSNTI Upside
Bayesian DCF Intrinsic $0.28 -88.1% $0.52 -48.4%
Earnings Power Value Intrinsic $0.82 -75.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $3.95 +68.3% $0.06 -93.6%
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 PLUR vs SNTI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PLUR vs SNTI — Which Stock Is More Undervalued?

PLUR scores higher with a 5.6/10 quality rating vs SNTI's 4.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Pluri Inc. (PLUR) and Senti Biosciences Holdings, Inc (SNTI) 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.

PLUR currently trades at $2.35 with a QOC of 5.6/10, while SNTI trades at $1.00 with a QOC of 4.1/10.

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).