MPC vs SUN

Marathon Petroleum Corporation vs Sunoco LP — Valuation Comparison 2026

MPC

Petroleum Refining
Marathon Petroleum Corporation
Quality
7.4
out of 10
Value Trap
24
SAFE
Price
$248.77
Last close
Models
12/13
Active
VS

SUN

Petroleum Refining
Sunoco LP
Quality
7.7
out of 10
Value Trap
12
SAFE
Price
$64.94
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType MPC Fair ValueMPC Upside SUN Fair ValueSUN Upside
Bayesian DCF Intrinsic $307.16 +23.5% $6.54 -90.7%
Earnings Power Value Intrinsic $56.64 -77.2%
EROIC Spread Intrinsic $66.80 -73.1% $8.57 -86.8%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 MPC vs SUN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MPC vs SUN — Which Stock Is More Undervalued?

SUN scores higher with a 7.7/10 quality rating vs MPC's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Marathon Petroleum Corporation (MPC) and Sunoco LP (SUN) 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.

MPC currently trades at $248.77 with a QOC of 7.4/10, while SUN trades at $64.94 with a QOC of 7.7/10.

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).