Is BRR or BTM more undervalued in 2026?

CirclFi compares ProCap Financial, Inc. (BRR) and Bitcoin Depot Inc. (BTM) using 13 institutional-grade valuation models. BRR has a Quality of Company score of 4.6/10 while BTM scores 7.7/10.

Which is a better buy — BRR or BTM?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do BRR and BTM valuations compare?

BRR at $2.28 and BTM at $0.49 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BRR vs BTM Valuation Comparison 2026 — Intrinsic Value Analysis

BRR

Capital Markets

ProCap Financial, Inc.

Quality

4.6

out of 10

Value Trap

—

Price

$2.28

Last close

Models

6/13

Active

BTM

Capital Markets

Bitcoin Depot Inc.

Quality

7.7

out of 10

Value Trap

SAFE

Price

$0.49

Last close

Models

2/13

Active

Model-by-Model Comparison

Model	Type	BRR Fair Value	BRR Upside	BTM Fair Value	BTM Upside
Bayesian DCF	Intrinsic	$0.10	-95.0%	—	—
Markov DDM	Intrinsic	$0.27	-88.0%	$1.53	+210.1%
ML-RIV	Intrinsic	$1.29	-25.2%	$2.04	-69.6%
Dynamic NAV	Asset-Based	$0.48	-79.1%	—	—
PWERM	Option-Based	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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BRR vs BTM — Which Stock Is More Undervalued?

BTM scores higher with a 7.7/10 quality rating vs BRR's 4.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ProCap Financial, Inc. (BRR) and Bitcoin Depot Inc. (BTM) 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.

BRR currently trades at $2.28 with a QOC of 4.6/10, while BTM trades at $0.49 with a QOC of 7.7/10.

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).