LOGI vs MYND

Logitech International S.A. - R vs Mynd.ai, Inc. — Valuation Comparison 2026

LOGI

Computer Peripheral Equipment, NEC
Logitech International S.A. - R
Quality
10.0
out of 10
Value Trap
6
SAFE
Price
$121.87
Last close
Models
13/13
Active
VS

MYND

Computer Peripheral Equipment, NEC
Mynd.ai, Inc.
Quality
4.7
out of 10
Value Trap
57
WARN
Price
$0.61
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType LOGI Fair ValueLOGI Upside MYND Fair ValueMYND Upside
Bayesian DCF Intrinsic $119.87 -1.6%
Earnings Power Value Intrinsic $59.04 -51.6% $0.38 +11.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $89.56 -26.5% $1.62 +165.0%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for LOGI vs MYND — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LOGI vs MYND — Which Stock Is More Undervalued?

LOGI scores higher with a 10.0/10 quality rating vs MYND's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Logitech International S.A. - R (LOGI) and Mynd.ai, Inc. (MYND) 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.

LOGI currently trades at $121.87 with a QOC of 10.0/10, while MYND trades at $0.61 with a QOC of 4.7/10.

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).