MTD vs PACB

Mettler-Toledo International, I vs Pacific Biosciences of Californ — Valuation Comparison 2026

MTD

Laboratory Analytical Instruments
Mettler-Toledo International, I
Quality
9.3
out of 10
Value Trap
6
SAFE
Price
$1180.58
Last close
Models
12/13
Active
VS

PACB

Laboratory Analytical Instruments
Pacific Biosciences of Californ
Quality
5.7
out of 10
Value Trap
50
WARN
Price
$1.49
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType MTD Fair ValueMTD Upside PACB Fair ValuePACB Upside
Bayesian DCF Intrinsic $702.65 -40.5% $2.54 +48.4%
Earnings Power Value Intrinsic $217.59 -81.6% $1.49 -6.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 MTD vs PACB — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MTD vs PACB — Which Stock Is More Undervalued?

MTD scores higher with a 9.3/10 quality rating vs PACB's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Mettler-Toledo International, I (MTD) and Pacific Biosciences of Californ (PACB) 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.

MTD currently trades at $1180.58 with a QOC of 9.3/10, while PACB trades at $1.49 with a QOC of 5.7/10.

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).