PACB vs RVTY

Pacific Biosciences of Californ vs Revvity, Inc. — Valuation Comparison 2026

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
VS

RVTY

Laboratory Analytical Instruments
Revvity, Inc.
Quality
8.2
out of 10
Value Trap
25
LOW
Price
$104.55
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PACB Fair ValuePACB Upside RVTY Fair ValueRVTY Upside
Bayesian DCF Intrinsic $2.54 +48.4% $94.06 -10.0%
Earnings Power Value Intrinsic $1.49 -6.6% $56.11 -46.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 PACB vs RVTY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PACB vs RVTY — Which Stock Is More Undervalued?

RVTY scores higher with a 8.2/10 quality rating vs PACB's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Pacific Biosciences of Californ (PACB) and Revvity, Inc. (RVTY) 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.

PACB currently trades at $1.49 with a QOC of 5.7/10, while RVTY trades at $104.55 with a QOC of 8.2/10.

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).