Is BHVN or BIVI more undervalued in 2026?

CirclFi compares Biohaven Ltd. (BHVN) and BioVie Inc. (BIVI) using 13 institutional-grade valuation models. BHVN has a Quality of Company score of 3.8/10 while BIVI scores 3.2/10.

Which is a better buy — BHVN or BIVI?

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 BHVN and BIVI valuations compare?

BHVN at $10.24 and BIVI at $1.44 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BHVN vs BIVI Valuation Comparison 2026 — Intrinsic Value Analysis

BHVN

Biotechnology

Biohaven Ltd.

Quality

3.8

out of 10

Value Trap

LOW

Price

$10.24

Last close

Models

6/13

Active

BIVI

Biotechnology

BioVie Inc.

Quality

3.2

out of 10

Value Trap

LOW

Price

$1.44

Last close

Models

7/13

Active

Model-by-Model Comparison

Model	Type	BHVN Fair Value	BHVN Upside	BIVI Fair Value	BIVI Upside
Bayesian DCF	Intrinsic	$2.84	-72.3%	$1.46	+1.2%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
ML-RIV	Intrinsic	—	—	$0.27	-84.1%
Dynamic NAV	Asset-Based	$2.20	-78.6%	$7.21	+401.0%
PWERM	Option-Based	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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BHVN vs BIVI — Which Stock Is More Undervalued?

BHVN scores higher with a 3.8/10 quality rating vs BIVI's 3.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Biohaven Ltd. (BHVN) and BioVie Inc. (BIVI) 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.

BHVN currently trades at $10.24 with a QOC of 3.8/10, while BIVI trades at $1.44 with a QOC of 3.2/10.

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).