BYRN vs DFLI

Byrna Technologies, Inc. vs Dragonfly Energy Holdings Corp — Valuation Comparison 2026

BYRN

Miscellaneous Electrical Machinery, Equipment & Supplies
Byrna Technologies, Inc.
Quality
8.2
out of 10
Value Trap
12
SAFE
Price
$6.24
Last close
Models
12/13
Active
VS

DFLI

Miscellaneous Electrical Machinery, Equipment & Supplies
Dragonfly Energy Holdings Corp
Quality
4.4
out of 10
Value Trap
24
SAFE
Price
$2.11
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType BYRN Fair ValueBYRN Upside DFLI Fair ValueDFLI Upside
Bayesian DCF Intrinsic $1.84 -70.5%
Earnings Power Value Intrinsic $5.36 -14.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $1.98 -68.3% $0.65 -69.0%
PWERM Option-Based $5.31 -14.8% $3.68 +74.6%
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 BYRN vs DFLI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BYRN vs DFLI — Which Stock Is More Undervalued?

BYRN scores higher with a 8.2/10 quality rating vs DFLI's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Byrna Technologies, Inc. (BYRN) and Dragonfly Energy Holdings Corp (DFLI) 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.

BYRN currently trades at $6.24 with a QOC of 8.2/10, while DFLI trades at $2.11 with a QOC of 4.4/10.

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).