LI vs LOT

Li Auto Inc. vs Lotus Technology Inc. — Valuation Comparison 2026

LI

Auto Manufacturers
Li Auto Inc.
Quality
8.3
out of 10
Value Trap
12
SAFE
Price
$15.54
Last close
Models
13/13
Active
VS

LOT

Auto Manufacturers
Lotus Technology Inc.
Quality
2.6
out of 10
Value Trap
Price
$1.28
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType LI Fair ValueLI Upside LOT Fair ValueLOT Upside
Bayesian DCF Intrinsic $12.45 -19.9% $0.34 -73.5%
Earnings Power Value Intrinsic $9.78 -37.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $23.65 +52.2% $3.88 +198.3%
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 LI vs LOT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LI vs LOT — Which Stock Is More Undervalued?

LI scores higher with a 8.3/10 quality rating vs LOT's 2.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Li Auto Inc. (LI) and Lotus Technology Inc. (LOT) 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.

LI currently trades at $15.54 with a QOC of 8.3/10, while LOT trades at $1.28 with a QOC of 2.6/10.

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).