VNET vs WYFI

VNET Group, Inc. vs WhiteFiber, Inc. — Valuation Comparison 2026

VNET

Information Technology Services
VNET Group, Inc.
Quality
3.8
out of 10
Value Trap
18
SAFE
Price
$10.62
Last close
Models
11/13
Active
VS

WYFI

Information Technology Services
WhiteFiber, Inc.
Quality
5.3
out of 10
Value Trap
Price
$32.18
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType VNET Fair ValueVNET Upside WYFI Fair ValueWYFI Upside
Bayesian DCF Intrinsic $7.11 -33.1% $9.36 -70.9%
Earnings Power Value Intrinsic $5.41 -49.1% $6.83 -59.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

VNET vs WYFI — Which Stock Is More Undervalued?

WYFI scores higher with a 5.3/10 quality rating vs VNET's 3.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing VNET Group, Inc. (VNET) and WhiteFiber, Inc. (WYFI) 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.

VNET currently trades at $10.62 with a QOC of 3.8/10, while WYFI trades at $32.18 with a QOC of 5.3/10.

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).