How to make your AI Agent 111x cheaper and 2.5x faster at data aggregation
A recent test compared the performance of Google's Gemini 3.5 Flash model with the Neonia MCP architecture for data aggregation tasks. The Neonia MCP approach proved to be significantly faster and cheaper, completing the task in 15.54 seconds and using only 650 tokens. In contrast, the Gemini model took 38.89 seconds and consumed 72,361 tokens for the same task.
- ▪Google's Gemini 3.5 Flash model was tested for data aggregation tasks.
- ▪The Neonia MCP architecture completed the task 2.5 times faster and was 111 times cheaper than the Gemini model.
- ▪The Gemini model accurately processed 72,286 tokens without errors but at a high cost in terms of time and resources.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 335474) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Olex Tkachuk Posted on May 26 How to make your AI Agent 111x cheaper and 2.5x faster at data aggregation #ai #rust #mcp #agents Google recently released an incredibly fast new model — Gemini 3.5 Flash. As someone building infrastructure for autonomous agents, I decided to put it through a rigorous crash test on a real-world data aggregation task to see how it handles massive context loads. The Benchmark Task The challenge was simple, but computationally heavy.
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