VOR vs XFOR

Vor Biopharma Inc. vs X4 Pharmaceuticals, Inc. — Valuation Comparison 2026

VOR

Biological Products, (No Diagnostic Substances)
Vor Biopharma Inc.
Quality
3.5
out of 10
Value Trap
30
LOW
Price
$14.97
Last close
Models
7/13
Active
VS

XFOR

Biological Products, (No Diagnostic Substances)
X4 Pharmaceuticals, Inc.
Quality
5.1
out of 10
Value Trap
49
WARN
Price
$4.31
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType VOR Fair ValueVOR Upside XFOR Fair ValueXFOR Upside
Bayesian DCF Intrinsic $6.33 -57.7% $2.12 -50.7%
Earnings Power Value Intrinsic $3.32 -20.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $27.94 +86.6% $4.72 +9.4%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

VOR vs XFOR — Which Stock Is More Undervalued?

XFOR scores higher with a 5.1/10 quality rating vs VOR's 3.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Vor Biopharma Inc. (VOR) and X4 Pharmaceuticals, Inc. (XFOR) 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.

VOR currently trades at $14.97 with a QOC of 3.5/10, while XFOR trades at $4.31 with a QOC of 5.1/10.

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).