CRNX vs CRSP

Crinetics Pharmaceuticals, Inc. vs CRISPR Therapeutics AG — Valuation Comparison 2026

CRNX

Biotechnology
Crinetics Pharmaceuticals, Inc.
Quality
6.1
out of 10
Value Trap
24
SAFE
Price
$36.01
Last close
Models
12/13
Active
VS

CRSP

Biotechnology
CRISPR Therapeutics AG
Quality
6.2
out of 10
Value Trap
14
SAFE
Price
$56.38
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CRNX Fair ValueCRNX Upside CRSP Fair ValueCRSP Upside
Bayesian DCF Intrinsic $11.27 -68.7% $10.94 -80.6%
Earnings Power Value Intrinsic $1.27 -96.7% $27.66 -43.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 CRNX vs CRSP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CRNX vs CRSP — Which Stock Is More Undervalued?

CRSP scores higher with a 6.2/10 quality rating vs CRNX's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Crinetics Pharmaceuticals, Inc. (CRNX) and CRISPR Therapeutics AG (CRSP) 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.

CRNX currently trades at $36.01 with a QOC of 6.1/10, while CRSP trades at $56.38 with a QOC of 6.2/10.

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).