Coral Protocol Beats Microsoft-Backed Rival By 34% On Key AI Benchmark

Coral Protocol's multi-agent AI system has achieved a 34% performance lead over Microsoft MSFT-backed Magnetic-UI on the GAIA Benchmark, a widely used test to measure AI’s ability to solve complex, real-world problems.

The result signals a noteworthy departure from the prevailing industry trend of scaling through ever-larger models.

The London-based open protocol secured the highest verified score for mini-model agents, providing practical validation of NVIDIA's thesis that small, orchestrated models can match or exceed the performance of their larger counterparts.

Coral's horizontal scaling approach layers specialized agents instead of expanding single large models, increasing overall effectiveness in reasoning, planning, and problem-solving.

“This breakthrough marks a turning point in AI infrastructure,” said Caelum Forder, CTO of Coral Protocol. "It's proof that horizontal scaling isn't just possible – it's practical."

The GAIA Benchmark comprises 450 high-difficulty questions designed to test AI systems across various areas, including research, image analysis, and planning.

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Coral's agent system, inspired by CAMEL's OWL, uses a graph-based architecture to deploy specialized mini-agents that coordinate via its MCP communication protocol.

Each agent handles tasks such as critique, search, video processing, and problem-solving.

While the AI industry continues to focus on scaling model size, Coral's benchmark-topping result adds weight to emerging views that performance gains can also come from better orchestration of smaller systems.

The GAIA results suggest these models can deliver faster performance, stronger interconnectivity, and reduced computational overhead.

Forder added, "We have proven that such models can scale beyond their previously known limits and outcompete the incumbents."

Coral Protocol aims to become the infrastructure layer for The Internet of Agents, promoting safe agent collaboration and verifiable interactions in decentralized AI ecosystems.

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