Indie Dev Hands-on: SERPdive vs Tavily
Patient-Injury-1327 · reddit · 2026-07-20
A developer found that using the Tavily Search API often returned content that was too thin or too noisy, causing Claude to trigger multiple search rounds and consume massive amounts of tokens. To fix this, they developed **SERPdive** and its `mako` model to provide cleaner content, enabling the model to answer in a single pass. In a blind benchmark of 1,000 questions, `mako` won about 60% of the valid matchups. Compared to Tavily Basic, it returned about 20% fewer tokens (helping reduce LLM context costs) and had an average latency of 1.7 seconds (slightly slower than Tavily's 1.4 seconds), all at a lower price ($6 vs $8 / 1k searches). All test questions, raw responses, judging reasoning, and reproduction code have been open-sourced.
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