Gnosis Chain vs Algorand Scalability
Real-time TPS
Gnosis Chain TPS is 57% lower than Algorand TPS
Max TPS (100 blocks)
Gnosis Chain max TPS is 98.58% lower than Algorand max TPS
Max Theoretical TPS
Gnosis Chain max theoretical TPS is 98.27% lower than Algorand max theoretical TPS
Transaction Volume
Gnosis Chain transaction volume is 57% lower than Algorand transaction volume
Block Time
Gnosis Chain block time is 1.83X longer than Algorand block time
Finality
Gnosis Chain finality is 100% longer than Algorand finality
Type
Gnosis Chain is a sidechain, while Algorand is a layer 1 blockchain
Launch Date
Gnosis Chain was launched 8 months before Algorand
Gnosis Chain vs Algorand Decentralization New
Nakamoto Coefficient
Gnosis Chain has no data, while Algorand Nakamoto Coefficient is 12
Validators
Gnosis Chain has 173X more validators than Algorand
Stake
Gnosis Chain stake is 90.03% lower than Algorand stake
Consensus Mechanism
Gnosis Chain is PoS, while Algorand is Pure Proof of Stake
Governance
Gnosis Chain multisig governance is worse than Algorand on-chain governance
Gnosis Chain vs Algorand Real-Time TPS Chart
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Other Comparisons
Gnosis Chain Comparisons
About Blockchains
About Gnosis Chain
Gnosis Chain operates as an execution-layer Ethereum Virtual Machine (EVM) chain, utilizing the xDAI stablecoin for transactions and fee payments. Its security is ensured by the Gnosis Beacon Chain (GBC), which employs a Proof-of-Stake consensus mechanism akin to Cardano and Solana. Users stake GNO tokens to participate in transaction validation, earning additional GNO rewards for contributing to network security.
About Algorand
Algorand emerges as a blockchain platform committed to fostering transparency and enabling the growth of decentralized projects and applications. Operating as a public, decentralized blockchain, it leverages a Pure Proof-of-Stake (PPoS) consensus mechanism to uphold network security, efficiency, and decentralization. Powered by the Algorand Consensus Algorithm, the network employs a combination of cryptographic techniques and random selection to attain consensus, effectively addressing the constraints of traditional consensus mechanisms.