AUST vs CGAU

Austin Gold Corp. vs Centerra Gold Inc. — Valuation Comparison 2026

AUST

Gold
Austin Gold Corp.
Quality
2.4
out of 10
Value Trap
6
SAFE
Price
$1.33
Last close
Models
7/13
Active
VS

CGAU

Gold
Centerra Gold Inc.
Quality
6.7
out of 10
Value Trap
Price
$17.05
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType AUST Fair ValueAUST Upside CGAU Fair ValueCGAU Upside
Bayesian DCF Intrinsic $0.35 -73.5% $16.47 -3.4%
Earnings Power Value Intrinsic $7.03 -58.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $1.05 -18.3% $10.81 -36.6%
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 AUST vs CGAU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AUST vs CGAU — Which Stock Is More Undervalued?

CGAU scores higher with a 6.7/10 quality rating vs AUST's 2.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Austin Gold Corp. (AUST) and Centerra Gold Inc. (CGAU) 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.

AUST currently trades at $1.33 with a QOC of 2.4/10, while CGAU trades at $17.05 with a QOC of 6.7/10.

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).