GFR vs GTE

Greenfire Resources Ltd. vs Gran Tierra Energy Inc. — Valuation Comparison 2026

GFR

Crude Petroleum & Natural Gas
Greenfire Resources Ltd.
Quality
6.6
out of 10
Value Trap
9
SAFE
Price
$5.79
Last close
Models
12/13
Active
VS

GTE

Crude Petroleum & Natural Gas
Gran Tierra Energy Inc.
Quality
6.0
out of 10
Value Trap
18
SAFE
Price
$8.04
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType GFR Fair ValueGFR Upside GTE Fair ValueGTE Upside
Bayesian DCF Intrinsic $1.12 -80.7%
Earnings Power Value Intrinsic $1.32 -77.2% $1.27 -86.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $4.10 -29.2% $11.81 +46.9%
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 GFR vs GTE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GFR vs GTE — Which Stock Is More Undervalued?

GFR scores higher with a 6.6/10 quality rating vs GTE's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Greenfire Resources Ltd. (GFR) and Gran Tierra Energy Inc. (GTE) 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.

GFR currently trades at $5.79 with a QOC of 6.6/10, while GTE trades at $8.04 with a QOC of 6.0/10.

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).