RIGL vs RNAC

Rigel Pharmaceuticals, Inc. vs Cartesian Therapeutics, Inc. — Valuation Comparison 2026

RIGL

Biotechnology
Rigel Pharmaceuticals, Inc.
Quality
9.1
out of 10
Value Trap
24
SAFE
Price
$30.05
Last close
Models
12/13
Active
VS

RNAC

Biotechnology
Cartesian Therapeutics, Inc.
Quality
4.4
out of 10
Value Trap
39
LOW
Price
$7.21
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType RIGL Fair ValueRIGL Upside RNAC Fair ValueRNAC Upside
Bayesian DCF Intrinsic $21.03 -30.0% $5.09 -28.0%
Earnings Power Value Intrinsic $50.82 +69.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $32.03 +6.6% $10.81 +49.9%
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 RIGL vs RNAC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RIGL vs RNAC — Which Stock Is More Undervalued?

RIGL scores higher with a 9.1/10 quality rating vs RNAC's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Rigel Pharmaceuticals, Inc. (RIGL) and Cartesian Therapeutics, Inc. (RNAC) 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.

RIGL currently trades at $30.05 with a QOC of 9.1/10, while RNAC trades at $7.21 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).