Is PEPG or PHGE more undervalued in 2026?

CirclFi compares PepGen Inc. (PEPG) and BiomX Inc. (PHGE) using 13 institutional-grade valuation models. PEPG has a Quality of Company score of 4.3/10 while PHGE scores 3.3/10.

Which is a better buy — PEPG or PHGE?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do PEPG and PHGE valuations compare?

PEPG at $1.50 and PHGE at $1.56 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

PEPG vs PHGE Valuation Comparison 2026 — Intrinsic Value Analysis

PEPG

Biotechnology

PepGen Inc.

Quality

4.3

out of 10

Value Trap

SAFE

Price

$1.50

Last close

Models

7/13

Active

PHGE

Biotechnology

BiomX Inc.

Quality

3.3

out of 10

Value Trap

WARN

Price

$1.56

Last close

Models

7/13

Active

Model-by-Model Comparison

Model	Type	PEPG Fair Value	PEPG Upside	PHGE Fair Value	PHGE Upside
Bayesian DCF	Intrinsic	$0.64	-57.4%	$0.48	-56.9%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Dynamic NAV	Asset-Based	$2.41	+60.5%	$2.32	+49.0%
PWERM	Option-Based	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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PEPG vs PHGE — Which Stock Is More Undervalued?

PEPG scores higher with a 4.3/10 quality rating vs PHGE's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PepGen Inc. (PEPG) and BiomX Inc. (PHGE) 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.

PEPG currently trades at $1.50 with a QOC of 4.3/10, while PHGE trades at $1.56 with a QOC of 3.3/10.

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).