BFRG vs BHST

Bullfrog AI Holdings, Inc. vs BioHarvest Sciences Inc. — Valuation Comparison 2026

BFRG

Pharmaceutical Preparations
Bullfrog AI Holdings, Inc.
Quality
5.5
out of 10
Value Trap
12
SAFE
Price
$0.76
Last close
Models
11/13
Active
VS

BHST

Pharmaceutical Preparations
BioHarvest Sciences Inc.
Quality
6.1
out of 10
Value Trap
Price
$3.86
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BFRG Fair ValueBFRG Upside BHST Fair ValueBHST Upside
Bayesian DCF Intrinsic $0.23 -69.1% $1.53 -60.5%
Earnings Power Value Intrinsic $0.11 -84.5% $3.34 -20.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 BFRG vs BHST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BFRG vs BHST — Which Stock Is More Undervalued?

BHST scores higher with a 6.1/10 quality rating vs BFRG's 5.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Bullfrog AI Holdings, Inc. (BFRG) and BioHarvest Sciences Inc. (BHST) 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.

BFRG currently trades at $0.76 with a QOC of 5.5/10, while BHST trades at $3.86 with a QOC of 6.1/10.

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).