MiniMax Agent has quietly made a decision that reveals something important about how agentic AI systems actually work in production: search quality is not a nice-to-have, it is the core cost driver. The team benchmarked three AI-native search providers across more than 700 agent tasks, and the results were decisive enough to switch their default search provider from Serper to Perplexity.
The Numbers That Made the Call
The benchmark compared MiniMax Agent's previous default, Serper, against Perplexity Search across real agent workflows. Serper is a classic SERP API -- it returns raw Google search results cheaply and quickly. Serper is one of the cheapest options for raw Google SERP data at $1 per 1K requests on the Starter plan, or $0.30 per 1K on Ultimate. But cheap per-query pricing can be deceptive when agents loop.
The MiniMax team's results across 700+ tasks told a clear story:
- Tool calls per task: 17.8 (Perplexity) vs. 32.6 (Serper) -- a 45% reduction
- Token usage: 94.6M vs. 162.3M -- a 42% reduction
- Pass rate: +2% improvement with Perplexity
- Total cost: 27% decrease end-to-end
The key insight: fewer, better searches meant the agent needed less context to arrive at a correct answer. One good search replaced nearly two dozen bad ones.
Why Search Is Different Inside an Agent
The distinction between a search API built for humans and one built for agents is fundamental. The fundamental question is: when your agent calls this API, what does it get? A synthesized answer with citations, or raw links it has to process itself? Full page content, or just snippets? The difference determines your entire agent architecture.
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