NNDM vs SANM

Nano Dimension Ltd. vs Sanmina Corporation — Valuation Comparison 2026

NNDM

Printed Circuit Boards
Nano Dimension Ltd.
Quality
2.0
out of 10
Value Trap
Price
$1.75
Last close
Models
12/13
Active
VS

SANM

Printed Circuit Boards
Sanmina Corporation
Quality
7.9
out of 10
Value Trap
12
SAFE
Price
$259.73
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NNDM Fair ValueNNDM Upside SANM Fair ValueSANM Upside
Bayesian DCF Intrinsic $0.41 -76.5% $93.57 -64.0%
Earnings Power Value Intrinsic $4.91 +158.3% $41.71 -83.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 NNDM vs SANM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NNDM vs SANM — Which Stock Is More Undervalued?

SANM scores higher with a 7.9/10 quality rating vs NNDM's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nano Dimension Ltd. (NNDM) and Sanmina Corporation (SANM) 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.

NNDM currently trades at $1.75 with a QOC of 2.0/10, while SANM trades at $259.73 with a QOC of 7.9/10.

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).