KEYS vs MLAB

Keysight Technologies Inc. vs Mesa Laboratories, Inc. — Valuation Comparison 2026

KEYS

Industrial Instruments For Measurement, Display, and Control
Keysight Technologies Inc.
Quality
8.3
out of 10
Value Trap
19
SAFE
Price
$338.33
Last close
Models
13/13
Active
VS

MLAB

Industrial Instruments For Measurement, Display, and Control
Mesa Laboratories, Inc.
Quality
8.6
out of 10
Value Trap
37
LOW
Price
$102.02
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType KEYS Fair ValueKEYS Upside MLAB Fair ValueMLAB Upside
Bayesian DCF Intrinsic $122.27 -63.9% $88.07 -13.7%
Earnings Power Value Intrinsic $54.79 -83.8% $528.65 +418.2%
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 $•••.•• ••.•% $•••.•• ••.•%
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KEYS vs MLAB — Which Stock Is More Undervalued?

MLAB scores higher with a 8.6/10 quality rating vs KEYS's 8.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Keysight Technologies Inc. (KEYS) and Mesa Laboratories, Inc. (MLAB) 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.

KEYS currently trades at $338.33 with a QOC of 8.3/10, while MLAB trades at $102.02 with a QOC of 8.6/10.

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).