Perplexity搜索在基准测试中击败竞争对手，成本更低，三种上下文设置表现均佳。
Perplexity Search在Artificial Analysis Search Index基准测试中表现最佳，三种上下文设置均位居排行榜前列。其中中等上下文设置得分80，高于Parallel(advanced)和Brave Search(LLM context)的75分。Perplexity的搜索结果负载更小，模型推理成本最低，每任务成本在0.028至0.034美元之间。
perplexity is the best at search at any level of compute, agnostic of how much compute the competito...
perplexity is the best at search at any level of compute, agnostic of how much compute the competitors use. Artificial Analysis @ArtificialAnlys Perplexity Search debuts on the Artificial Analysis Search Index, with all three context size variants taking top positions on the leaderboard The @perplexity_ai Search API comes with three context settings (low, medium, and high) that control how much extracted content each search result carries. We tested all three variants using our standardized methodology: the same model (GPT-5.6 Luna at medium reasoning), running inside Stirrup, our open-source agent harness, with tools for searching and fetching pages from the web. Only the provider behind the search tool changes. Key results: ➤ Perplexity Search (medium) scores 80 on the Artificial Analysis Search Index, ahead of the previous leaders, Parallel (advanced) and Brave Search (LLM context), at 75. The high and low variants score 79 and 77 respectively. Its lead is concentrated in BrowseComp results, with AA-Omniscience and DeepSearchQA scoring comparably to other leading providers ➤ Efficient search payloads: smaller overall search results mean the model reads less per task, so Perplexity has the lowest model inference cost per task of providers we’ve tested so far, ranging from $0.028 to $0.034 across the three variants vs $0.036 for the next lowest provider ➤ Total cost per task is ~$0.091 for the medium and high context variants, at mid-pack latency. For comparison, Parallel (advanced) costs $0.084 per task and Brave (LLM context) costs $0.13 per task 🔗 View Quoted Tweet 💬 5 🔄 7 ❤️ 53 👀 6763 📊 8 ⚡