TKNO vs UPC

Alpha Teknova, Inc. vs Universe Pharmaceuticals Inc — Valuation Comparison 2026

TKNO

Drug Manufacturers - Specialty & Generic
Alpha Teknova, Inc.
Quality
5.3
out of 10
Value Trap
18
SAFE
Price
$4.66
Last close
Models
11/13
Active
VS

UPC

Drug Manufacturers - Specialty & Generic
Universe Pharmaceuticals Inc
Quality
1.5
out of 10
Value Trap
15
SAFE
Price
$2.85
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType TKNO Fair ValueTKNO Upside UPC Fair ValueUPC Upside
Bayesian DCF Intrinsic $1.09 -76.7% $0.56 -80.2%
Earnings Power Value Intrinsic $1.50 -56.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $1.25 -73.3% $9.06 +235.7%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for TKNO vs UPC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TKNO vs UPC — Which Stock Is More Undervalued?

TKNO scores higher with a 5.3/10 quality rating vs UPC's 1.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Alpha Teknova, Inc. (TKNO) and Universe Pharmaceuticals Inc (UPC) 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.

TKNO currently trades at $4.66 with a QOC of 5.3/10, while UPC trades at $2.85 with a QOC of 1.5/10.

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).