LLM-Based Translation of Ethereum Solidity Contracts to Algorand Python

Blockchain platforms have revolutionized decentralized computing, with smart contracts enabling trustless and autonomous applications. However, the diversity of blockchain ecosystems - such as Bitcoin, Ethereum, Solana and Algorand creates significant challenges for developers seeking cross-platform compatibility. Algorand, known for its scalability and security, transaction finality and transaction costs has a distinct smart contract model that differs from Ethereum's Solidity-based approach, making migration and adoption difficult. This study explores the potential of Large Language Models (LLMs) in addressing this challenge by assisting in the translation of Solidity smart contracts into Algorand's Python-based smart contract language. We evaluate four LLMs - Claude, ChatGPT, Qwen, and DeepSeek - using various prompting strategies across 10 contracts. Our findings reveal that while LLMs can accelerate the learning process and provide useful code suggestions, they introduce translation errors and require careful validation. These insights contribute to understanding how AI-powered tools can support blockchain development, reduce manual effort, and lower barriers to entry for developers transitioning to Algorand.