TNXP vs TRVI

Tonix Pharmaceuticals Holding C vs Trevi Therapeutics, Inc. — Valuation Comparison 2026

TNXP

Biotechnology
Tonix Pharmaceuticals Holding C
Quality
6.1
out of 10
Value Trap
27
LOW
Price
$12.64
Last close
Models
12/13
Active
VS

TRVI

Biotechnology
Trevi Therapeutics, Inc.
Quality
4.2
out of 10
Value Trap
30
LOW
Price
$14.45
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType TNXP Fair ValueTNXP Upside TRVI Fair ValueTRVI Upside
Bayesian DCF Intrinsic $9.52 -24.7% $4.39 -69.7%
Earnings Power Value Intrinsic $20.65 +51.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $3.17 -74.9% $0.40 -97.3%
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 TNXP vs TRVI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TNXP vs TRVI — Which Stock Is More Undervalued?

TNXP scores higher with a 6.1/10 quality rating vs TRVI's 4.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Tonix Pharmaceuticals Holding C (TNXP) and Trevi Therapeutics, Inc. (TRVI) 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.

TNXP currently trades at $12.64 with a QOC of 6.1/10, while TRVI trades at $14.45 with a QOC of 4.2/10.

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).