VNOM vs WTI

Viper Energy, Inc. vs W&T Offshore, Inc. — Valuation Comparison 2026

VNOM

Crude Petroleum & Natural Gas
Viper Energy, Inc.
Quality
6.6
out of 10
Value Trap
Price
$45.50
Last close
Models
12/13
Active
VS

WTI

Crude Petroleum & Natural Gas
W&T Offshore, Inc.
Quality
6.0
out of 10
Value Trap
18
SAFE
Price
$3.68
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType VNOM Fair ValueVNOM Upside WTI Fair ValueWTI Upside
Bayesian DCF Intrinsic $25.34 -44.3% $9.77 +165.6%
Earnings Power Value Intrinsic $6.77 +64.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $16.28 -64.2% $4.38 +19.1%
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 VNOM vs WTI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

VNOM vs WTI — Which Stock Is More Undervalued?

VNOM scores higher with a 6.6/10 quality rating vs WTI's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Viper Energy, Inc. (VNOM) and W&T Offshore, Inc. (WTI) 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.

VNOM currently trades at $45.50 with a QOC of 6.6/10, while WTI trades at $3.68 with a QOC of 6.0/10.

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).