Perplexity Search API Sweeps Artificial Analysis Test, but Medium Context Wins

Perplexity’s Search API occupied the top three positions in Artificial Analysis’ Search Index, with its medium-context setting scoring 80. The notable detail is not just the sweep: the medium setting outperformed both the larger high-context payload and the smaller low-context option in the same controlled agent test.

Search APIs give an AI agent web results and extracted page material to reason over. In this evaluation, the context setting determined how much content Perplexity returned from each result page: low supplied short, query-relevant passages, medium a balanced amount, and high detailed content.

That design choice produced an inverted result. Perplexity’s medium setting scored 80, versus 79 for high and 77 for low. Parallel Search’s advanced setting and Brave Search’s LLM context mode were next at 75, leaving a five-point gap between the leader and the nearest competing results.

80Medium context

Medium context led Perplexity’s three settings in the index.

79High context

High context placed second overall.

77Low context

Low context placed third overall.

The payload is part of the product

The ranking suggests that a retrieval system’s value is not simply how much source text it can deliver. For this agent and task mix, the middle setting gave the model the strongest material to work with. More extracted content can increase downstream model usage or make the relevant passage harder for an agent to isolate.

The price structure makes that distinction operational. Perplexity charges $5 per 1,000 Search API requests regardless of context setting, but the total task bill can still change with the number of searches an agent makes and the tokens its answer model processes.

Quality came with a timing trade-off

Artificial Analysis estimated roughly $0.091 in total cost per task for Perplexity’s medium and high settings, including search charges and agent-model inference. The low setting cost about $0.105 per task. Parallel Search’s fast setting was cheaper at about $0.068 per task, but scored 73.

Perplexity did not lead on end-to-end task time. Its medium and high settings took 28.3 and 29.1 seconds per task, while low took 36.6 seconds. The metric includes both search calls and model processing, so it does not isolate the time for an individual search request.

A controlled result, not a universal verdict

Artificial Analysis held the agent layer constant by pairing every provider with GPT-5.6 Luna at medium reasoning effort in its open-source Stirrup harness. The agent had web-search and page-fetching tools, a maximum of 25 turns, and up to 10 results per search.

What the index tested

• 900 DeepSearchQA tasks.
• 600 held-out factual questions from AA-Omniscience.
• A 200-question BrowseComp subset.

The setup makes the result useful for teams choosing retrieval infrastructure: it compares providers under the same model and agent rules. It does not establish that the ordering will persist with another model, another context strategy, or workloads centered on coding, commerce, or local search. The immediate decision is therefore narrower: test whether Perplexity’s medium payload improves the specific agent workload being built.