Is BRBI or BTM more undervalued in 2026?

CirclFi compares BRBI BR Partners S.A. (BRBI) and Bitcoin Depot Inc. (BTM) using 13 institutional-grade valuation models. BRBI has a Quality of Company score of 1.9/10 while BTM scores 7.7/10.

Which is a better buy — BRBI 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 BRBI and BTM valuations compare?

BRBI at $12.77 and BTM at $0.49 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BRBI vs BTM Valuation Comparison 2026 — Intrinsic Value Analysis

BRBI

Capital Markets

BRBI BR Partners S.A.

Quality

1.9

out of 10

Value Trap

—

Price

$12.77

Last close

Models

5/13

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	BRBI Fair Value	BRBI Upside	BTM Fair Value	BTM Upside
Bayesian DCF	Intrinsic	$3.38	-73.5%	—	—
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	—	—	$1.53	+210.1%
ML-RIV	Intrinsic	$13.96	+9.3%	$2.04	-69.6%
Dynamic NAV	Asset-Based	$2.99	-76.6%	—	—
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%

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BRBI vs BTM — Which Stock Is More Undervalued?

BTM scores higher with a 7.7/10 quality rating vs BRBI's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BRBI BR Partners S.A. (BRBI) 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.

BRBI currently trades at $12.77 with a QOC of 1.9/10, 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).