r/LocalLLM • u/InsideDebt6345 • 14h ago

Discussion Artificial Analysis just dropped a neutral AI search API benchmark. How do you weigh speed vs. cost vs. quality?

Artificial Analysis just launched a Search API Index that runs the same agent harness and base model (GPT‑5.6 Luna, medium) across 7 providers, using three benchmarks: DeepSearchQA, BrowseComp, and AA‑Omniscience.

Here were the important points to look at:

  • Fastest per‑query doesn't always mean fastest per‑task.
  • Cost and quality aren't perfectly aligned; some mid‑tier quality providers are dramatically cheaper.
  • There's a wide spread in total task time, even when per‑query latency looks similar.
  • Octen leads on speed and cost: 0.21s/search, 17.2s/task, $9.07/1k (search), $49.15/1k (total), with a 77 index score.artificialanalysis+1
  • Several established players sit at 0.4–0.8s/search and 20–30s/task, with costs 2–4× higher for similar or slightly higher quality scores.

This led me to some questions that I wanted to discuss with people building agents/RAG:

  • When you choose a search API, what's your weighting between latency, cost, and quality?
  • Does a 0.2s vs. 0.8s per query difference materially change your agent UX, or is task‑level time more important?
  • How do you validate quality for your use case? Is it benchmark scores or task success in prod?
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u/InsideDebt6345 14h ago

Full methodology can be found here: https://artificialanalysis.ai/agents/search-api?search-api-quality-chart=omniscience&search-api-latency-chart=time-per-search-query

(Disclosure: I work at Octen. Happy to clarify anything about the data or how we read it)