Main highlights
- ChainOpera announced a collaboration with Princeton AI Launching the cryptocurrency industry's first benchmark
- The project, named 'CryptoBench', was developed with machine learning expert Professor Mengdi Wang and PhD student Jiacheng Gu.
- This benchmark uses more sophisticated agents used by major DeFi platforms to improve the predictive accuracy of AI tools in volatile markets.
On December 10, ChainOpera AI revealed its latest collaboration with Princeton AI Lab to launch CryptoBench, the crypto industry's first expert-level dynamic benchmark.
The first benchmark for agents in the cryptocurrency industry.
@Princeton In collaboration with Princeton AI Lab (professor @MengdiWang10 and PhD student @JiachengGu50887) we built CryptoBench, the world's first expert-level dynamic benchmark for evaluating LLM agents… pic.twitter.com/g9tvKNYCZ9
— ChainOpera AI (@ChainOpera_AI) December 10, 2025
It is known as the world's first expert-level dynamic benchmark built specifically to test AI agents in the cryptocurrency industry.
The tool is designed to solve key issues, including the lack of a standard method for evaluating large-scale language models that are increasingly used for digital asset trading, analysis, and risk assessment.
The project was developed with machine learning expert Professor Mengdi Wang and PhD student Jiacheng Gu. Unlike traditional benchmarks that use old static data, CryptoBench works in real time.
Challenge your AI agents by getting live information from the blockchain. These tests focus on four key areas essential to navigating the cryptocurrency market.
The first is real-time data acquisition from sources such as block explorers. The second is to predict future market trends amid high volatility. Another point is to analyze on-chain data to identify unusual transaction patterns.
Point out critical gaps in safer AI tools
CryptoBench's goal is to separate truly capable AI from ineffective or dangerous hype. Common AI models are
Existing agent benchmarks overlook the need to integrate on-chain intelligence, market data, DEX flows, and MEV alerts. CryptoBench provides 50 domain verification questions per month categorized into simple/complex searches and simple/complex predictions, reflecting the workload of professional analysts.
“We are introducing CryptoBench, a live benchmark that stress-tests LLM agents in time-sensitive adversarial crypto workflows. Existing agent benchmarks incorporate on-chain intelligence, market data, DEX flows, and Overlooking the need to integrate MEV alerts, CryptoBench provides 50 domain validation questions per month categorized into simple/complex acquisition and simple/complex prediction workloads,” the official website states.
“Evaluating 10 state-of-the-art LLMs (with and without the SmolAgent framework) reveals a significant imbalance between retrieval and prediction. Models that excel at fact retrieval often break down in predictive inference. Orchestration with agents can swap positions on the leaderboard, proving that raw model IQ is not equivalent to field performance.”
How CryptoBench can help the crypto sector
The cryptocurrency industry lost $2.1 billion to hacks and fraud in 2025 alone. Avoiding these scams is critical to growing the cryptocurrency industry and ensuring the safety of users.
CryptoBench’s DeFi Risk Assessment provides the power of an AI agent that can identify smart contract exploits and suspicious on-chain activity in real-time.
This means that benchmark-qualified AI agents can be integrated into exchanges to automatically alert users to potential phishing contracts or lag pulls before they interact.
This type of development could help decentralized finance bring much-needed trust and encourage adoption by institutional investors, as seen in markets like Singapore, where AI-based security has helped attract $150 billion in decentralized finance investments.
Separately, ChainOpera's system incentivizes contributions through its Proof-of-Intelligence model by rewarding those who improve the ecosystem with COAI tokens.
CryptoBench is also expected to bring the predictive accuracy of AI tools in volatile markets. That trend will help users develop more sophisticated agents used by major DeFi platforms.
For example, AI-optimized yield farming has already shown results in reducing transaction gas fees by 30% through predictive liquidity management.
CryptoBench provides a clear path to regulatory compliance. New regulations such as EU AI laws and anticipated US SEC guidelines are expected to mandate risk audits of AI agents in the financial industry.
