OKLO vs OPTT

Oklo Inc. vs Ocean Power Technologies, Inc. — Valuation Comparison 2026

OKLO

Electric Services
Oklo Inc.
Quality
5.0
out of 10
Value Trap
6
SAFE
Price
$66.88
Last close
Models
10/13
Active
VS

OPTT

Electric Services
Ocean Power Technologies, Inc.
Quality
4.6
out of 10
Value Trap
41
WARN
Price
$0.38
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType OKLO Fair ValueOKLO Upside OPTT Fair ValueOPTT Upside
Bayesian DCF Intrinsic $26.20 -60.8% $0.06 -84.8%
Earnings Power Value Intrinsic $48.11 -32.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $12.34 -81.6% $0.11 -70.5%
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 OKLO vs OPTT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OKLO vs OPTT — Which Stock Is More Undervalued?

OKLO scores higher with a 5.0/10 quality rating vs OPTT's 4.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Oklo Inc. (OKLO) and Ocean Power Technologies, Inc. (OPTT) 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.

OKLO currently trades at $66.88 with a QOC of 5.0/10, while OPTT trades at $0.38 with a QOC of 4.6/10.

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).