SEZL vs SOFI

Sezzle Inc. vs SoFi Technologies, Inc. — Valuation Comparison 2026

SEZL

Credit Services
Sezzle Inc.
Quality
9.7
out of 10
Value Trap
18
SAFE
Price
$119.00
Last close
Models
13/13
Active
VS

SOFI

Credit Services
SoFi Technologies, Inc.
Quality
6.8
out of 10
Value Trap
36
LOW
Price
$16.97
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType SEZL Fair ValueSEZL Upside SOFI Fair ValueSOFI Upside
Bayesian DCF Intrinsic $37.95 -68.1% $2.46 -85.5%
Earnings Power Value Intrinsic $34.43 -71.1% $10.60 -37.5%
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 SEZL vs SOFI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SEZL vs SOFI — Which Stock Is More Undervalued?

SEZL scores higher with a 9.7/10 quality rating vs SOFI's 6.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Sezzle Inc. (SEZL) and SoFi Technologies, Inc. (SOFI) 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.

SEZL currently trades at $119.00 with a QOC of 9.7/10, while SOFI trades at $16.97 with a QOC of 6.8/10.

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).