Monad Testnet Throughput Versus Sei and Aptos Scaling Realities

Monad’s 10,000 TPS Benchmark and Sei’s Production Gap

Monad’s November 2026 testnet benchmarks sustain over 10,000 transactions per second (TPS) through its parallel execution engine and asynchronous input/output for state access. This performance exceeds Sei V2’s current mainnet throughput, which sits between 2,500 and 3,500 TPS under real dApp loads from Astroport, White Whale, and Seiyans NFT activity. While Sei targets a 12,500 TPS theoretical ceiling, real-world contention from DEX pools and storage IOPS limits current production performance. Monad rebuilt its EVM from scratch in C++ to avoid the compatibility-performance trade-off Sei accepted by importing Geth. Sei’s strategy ensures that unmodified Solidity contracts work natively, but Monad bets on raw speed. Sei’s Giga devnet in 2025 reached 5.2 gigagas per second and 211 millisecond time-to-finality using a 20-validator set across the U.S., Europe, and Asia Pacific. Monad’s November 2025 ICO, hosted by Coinbase with 85,820 participants, provided $269 million in capital to support its $3 billion valuation. You know the drill: testnet numbers often exceed mainnet realities. Will Monad maintain these speeds when mainnet users launch heavy contention attacks?

Storage Bottlenecks in Aptos and Sei

Aptos developers identified the interaction between the execution layer and the storage layer as the primary bottleneck in their engine, which prompted the development of MonoMove to improve smart contract processing speed. Aptos tested MonoMove on transactions replayed from the Aptos mainnet, specifically on the Decibel exchange where withdrawing collateral was up to 55 times faster and placing orders was up to 22 times faster. These tests showed the engine’s end-to-end sequential throughput increased by up to 11 times. This storage issue mirrors the constraints seen in Sei, where sustained write throughput above 10,000 TPS pushes commodity NVMe drives into high latency territory despite SeiDB’s optimizations. SeiDB replaces the default Cosmos SDK IAVL+ tree with a custom backend that splits state into a write-optimized active layer and a read-optimized archive to reduce disk IOPS by 10x. Monad’s asynchronous state access allows the processor to continue scheduling other transactions while it waits for disk or memory responses, a mechanism that prevents a single slow state lookup from stalling the entire execution pipeline. Aptos relies on the Move programming language to make conflicts statically analyzable, whereas Monad uses a rollback mechanism to resolve state conflicts after parallel execution. Sei uses Twin Turbo consensus to compress block times to under 400 milliseconds.

The L1 Throughput Competition

The competition between parallel-EVM chains hinges on whether architectural improvements can overcome physical hardware limits like storage IOPS and validator network heterogeneity that degrade performance in live environments. Monad utilizes its $269 million capital pool to fund development. Sei pursues the Giga upgrade to reach 200,000 to 250,000 TPS, targeting 5 gigagas per second of execution. Aptos continues to test MonoMove to address the gap between execution and storage, with full feature readiness expected by the end of 2026. Monad’s performance remains unproven under adversarial conditions, unlike Sei which already handles $560 million in DeFi TVL. Monad’s BFT consensus layer agrees on the block order before it hands finalized blocks to a separate execution layer, creating a distinction from Solana, which bundles consensus and execution into a tightly coupled Proof-of-History system.

Sei’s Giga roadmap aims to rebuild three subsystems including Autobahn consensus and asynchronous execution to reach 200,000 to 250,000 TPS. Monad’s performance depends on whether its custom EVM and asynchronous I/O can sustain high volume once incentives for testnet participants fade. Monad aims for 400ms block times and 800ms finality. Aptos expects MonoMove to provide the fastest smart contract execution the company has measured. The 2026 throughput race belongs to whoever manages the storage-to-execution bottleneck most effectively.