ChainOpera AI to Launch 1st Crypto Benchmark ‘CryptoBench’

Key Highlights

• ChainOpera has announced a collaboration with Princeton AI  to launch the first benchmark for the cryptocurrency industry 
• The project named ‘CryptoBench’ was developed with a machine learning expert, Professor Mengdi Wang, and PhD student Jiacheng Gu
• This benchmark will provide a better predictive accuracy of AI tools in a volatile market with better refined agents used on major DeFi platforms

On December 10, ChainOpera AI revealed its latest collaboration with the Princeton AI Lab to launch CryptoBench, which is the first expert-level dynamic benchmark for the crypto industry. 

It is known as the world’s first expert-level dynamic benchmark built specifically for testing AI agents in the cryptocurrency industry. 

This tool is designed to solve major problems, including the lack of a standard way to evaluate the large language models that are increasingly used for trading, analysis, and risk assessment in digital assets. 

The project was developed with Professor Mengdi Wang, a machine learning expert, and PhD student Jiacheng Gu. Unlike traditional benchmarks that use old, static data, CryptoBench operates in real time.

It fetches live information from blockchains to challenge AI agents. These tests focus on four critical areas essential for navigating crypto markets. 

First is real-time data retrieval from sources like block explorers. Second is predicting future market trends amidst high volatility. Another point is analyzing on-chain data to spot unusual transaction patterns. 

Filing a Critical Gap of Safer AI Tools

The purpose of CryptoBench is to separate truly capable AI from ineffective or even dangerous hype. General AI models are 

Existing agent benchmarks overlook the need to synthesize on-chain intelligence, market data, DEX flows, and MEV alerts. CryptoBench delivers 50 domain-authentic questions per month, categorized into Simple/Complex Retrieval and Simple/Complex Prediction, mirroring professional analyst workloads.

“We introduce CryptoBench, a live benchmark that stress-tests LLM agents in time-sensitive, adversarial crypto workflows. Existing agent benchmarks overlook the need to synthesize on-chain intelligence, market data, DEX flows, and MEV alerts. CryptoBench delivers 50 domain-authentic questions per month, categorized into Simple/Complex Retrieval and Simple/Complex Prediction, mirroring professional analyst workloads,” stated on the official website.

“Evaluating ten state-of-the-art LLMs (with and without the SmolAgent framework) reveals a pronounced retrieval–prediction imbalance: models that excel at factual lookup frequently collapse on predictive reasoning. Agentic orchestration can reshuffle leaderboard positions, proving that raw model IQ does not equal field performance,” it stated.

How CryptoBench will Help the Crypto Sector

The crypto industry lost $2.1 billion to hacks and scams in 2025 alone. It is very important to avoid these scams in order to grow the crypto industry and ensure users’ safety.

CryptoBench’s DeFi risk assessment will provide AI Agent’s capability, which will be able to locate smart contract exploits and suspicious on-chain activity in real time. 

It means that an AI Agent that passes the benchmark’s criteria could be integrated into an exchange to automatically raise an alarm on a phishing contract or a possibility of rug pull before a user interacts with it. 

This kind of development will help decentralized finance to bring much-needed trust, which could boost institutional adoption, as seen in markets like Singapore, where AI-based security has helped attract $150 billion in decentralized finance investments. 

Apart from this, ChainOpera’s system also incentivizes contribution through its proof-of-intelligence model by rewarding those who improve the ecosystem with COAI tokens. 

CryptoBench is also expected to bring predictive accuracy of AI tools in a volatile market. Its trend will help users to develop more refined agents that are used on major DeFi platforms. 

For example, AI-optimized yield farming has already shown results to reduce transaction gas fees by 30% through predictive liquidity management. 

CryptoBench will provide a clear path to regulatory compliance. New regulations, such as the EU’s AI Act and anticipated U.S. SEC guidelines, are expected to require risk audits for AI agents in finance. 

Also Read: US OCC Permits Banks to Hold Crypto For Blockchain Fees