Is BSOL or CLNK more undervalued in 2026?

CirclFi compares Bitwise Solana Staking ETF (BSOL) and BITWISE CHAINLINK ETF (CLNK) using 13 institutional-grade valuation models. BSOL has a Quality of Company score of 2.5/10 while CLNK scores 3.1/10.

Which is a better buy — BSOL or CLNK?

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 BSOL and CLNK valuations compare?

BSOL at $11.08 and CLNK at $16.39 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BSOL vs CLNK Valuation Comparison 2026 — Intrinsic Value Analysis

BSOL

Commodity Contracts Brokers & Dealers

Bitwise Solana Staking ETF

Quality

2.5

out of 10

Value Trap

—

Price

$11.08

Last close

Models

2/13

Active

CLNK

Commodity Contracts Brokers & Dealers

BITWISE CHAINLINK ETF

Quality

3.1

out of 10

Value Trap

—

Price

$16.39

Last close

Models

2/13

Active

Model-by-Model Comparison

Model	Type	BSOL Fair Value	BSOL Upside	CLNK Fair Value	CLNK Upside
Dynamic NAV	Asset-Based	$5.48	-50.6%	$7.98	-51.3%
PWERM	Option-Based	$10.60	-4.3%	$15.04	-8.3%

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BSOL vs CLNK — Which Stock Is More Undervalued?

CLNK scores higher with a 3.1/10 quality rating vs BSOL's 2.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Bitwise Solana Staking ETF (BSOL) and BITWISE CHAINLINK ETF (CLNK) 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.

BSOL currently trades at $11.08 with a QOC of 2.5/10, while CLNK trades at $16.39 with a QOC of 3.1/10.

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).