ANIP vs ANIX

ANI Pharmaceuticals, Inc. vs Anixa Biosciences, Inc. — Valuation Comparison 2026

ANIP

Pharmaceutical Preparations
ANI Pharmaceuticals, Inc.
Quality
8.4
out of 10
Value Trap
24
SAFE
Price
$78.51
Last close
Models
12/13
Active
VS

ANIX

Pharmaceutical Preparations
Anixa Biosciences, Inc.
Quality
4.4
out of 10
Value Trap
6
SAFE
Price
$2.70
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType ANIP Fair ValueANIP Upside ANIX Fair ValueANIX Upside
Bayesian DCF Intrinsic $46.89 -40.3% $0.77 -71.4%
Earnings Power Value Intrinsic $41.54 -47.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.84 -98.9% $0.79 -70.7%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

ANIP vs ANIX — Which Stock Is More Undervalued?

ANIP scores higher with a 8.4/10 quality rating vs ANIX's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ANI Pharmaceuticals, Inc. (ANIP) and Anixa Biosciences, Inc. (ANIX) 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.

ANIP currently trades at $78.51 with a QOC of 8.4/10, while ANIX trades at $2.70 with a QOC of 4.4/10.

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).