# Best AI search for agents: 6 web search APIs benchmarked on BrowseComp (2026)

On BrowseComp[BrowseComp](https://openai.com/index/browsecomp/), measured agentic-search accuracy ran from about 19% to about 58% across the tools we tested. That spread often decides whether an agent finishes its task or stalls halfway through. BrowseComp is a benchmark of 1,266 questions that require persistent, multi-step browsing to locate hard-to-find, entangled information on the web, so it maps closely to how autonomous agents behave.

An agentic web search API is a tool your AI agents[AI agents](/articles/what-is-an-ai-agent) call mid-reasoning to fetch live information from the web. The agent issues a query, reads the results, and decides its next move, sometimes across a dozen or more calls for one hard question. When the returned context is thin or noisy, the agent burns tokens, adds round trips, and answers worse. When the context is dense and relevant, the agent resolves the question faster and cheaper.

This guide evaluates each vendor's web search API as a single tool in an agent's loop: the call an agent makes mid-reasoning to fetch live web context, measured on exactly that job.

## **What a benchmark can and cannot settle**

A spec sheet tells you what a vendor claims. A benchmark table, including the BrowseComp figures in this article, tells you how a fixed test scored on a fixed day. Neither settles which API is right for you. Your results depend on your own workloads, your domains, and the query patterns your agents generate. A tool that leads on one benchmark can trail on your traffic.

The only test that settles the question is running your real production queries head-to-head and measuring whether each agent finished the task. Retrieval scores and marketing claims are a starting point. End-task success on your workload is the verdict.

We should be direct about who we are. We are Parallel, we build one of the six APIs below, and we are biased toward it. So read our figures with that in mind, and check them against your own. Teams move to Parallel by running this test themselves, structuring their search objectives, and watching the numbers on their own queries. Our benchmark figures are sourced and dated, but sourced data from us is still data from us. Trust your own run over ours.

## **The six web search APIs, compared**

We tested six APIs on BrowseComp between July 10 and 12, 2026. A GPT-5.4 agent ran each one with up to 20 tool calls per question, and an LLM judge graded the answers. Cost per thousand requests (CPM) includes both LLM token costs and tool-call costs, so it reflects the full price of running the agent rather than a list price. We report each tool below with a short overview, its BrowseComp figures, a best-fit line, and an honest tradeoff.

### 1. Parallel Search API

We built Parallel Search[Parallel Search](/products/search) from the ground up for AI agents[for AI agents](/blog/search-api-benchmark) rather than for human readers. Instead of keyword queries, your agent declares a natural-language objective, and we return URLs ranked by token relevance plus compressed, information-dense excerpts tuned for the context window. We run this on our own proprietary web-scale index of billions of pages, with millions added daily, rather than reselling a third-party index. That lets us reach PDFs, JavaScript-rendered pages, and content behind forms that generalist indexes often miss, and it lets an agent resolve a complex query in fewer round trips. You can also call us over MCP[over MCP](/blog/search-mcp-server) through the Parallel Search MCP server, using the Model Context Protocol (MCP) to plug directly into your agent framework.

On BrowseComp, we scored 51% accuracy at 350 CPM. That's the highest accuracy of any tool in the table except OpenAI, at roughly a third of the per-request cost of Exa or Tavily. We won't claim the top line. On the hardest expert-knowledge set, Humanity's Last Exam (HLE), OpenAI's built-in tool scored higher than we did.

**Best for:** agent web-search tool calls where context quality, token efficiency, and multi-hop accuracy at predictable cost all matter.

**Tradeoffs:** we're a newer platform with a smaller third-party ecosystem than the incumbents, we don't run browser automation, and we don't reason for your model. We ground it, and it still has to think.

### 2. OpenAI Web Search

OpenAI Web Search is built into the OpenAI API as a tool the model invokes on its own. When the model decides it needs the web, it runs a search and injects the results into its context, tightly coupled to its reasoning loop. Billing combines a per-call charge with the search-context tokens at the model's input rate, and the model can trigger several searches per request, so your effective cost varies from question to question.

On BrowseComp, OpenAI Web Search led the table at 57.7% accuracy, and it also carried the second-highest cost at 912 CPM. It topped HLE too, so on the hardest sets it's the accuracy leader. If you're weighing a move, our guide on switching from OpenAI web search[switching from OpenAI web search](/articles/openai-to-parallel-search-api) walks through the tradeoffs.

**Best for:** teams already fully on the OpenAI stack that want maximum accuracy and will accept variable, higher cost.

**Tradeoffs:** you pay the highest or near-highest cost per successful task, you don't control how many searches run or how many context tokens you're billed for, and you're tied to one model vendor.

### 3. Brave Search API

Brave Search offers a developer API over its own independent web index, reported at more than 30 billion pages, served without routing your queries through Google or Bing. In this benchmark it ran search-only, with no paired extract step, and it holds a SOC 2 Type II attestation with a zero-data-retention option.

On BrowseComp, Brave scored 38.3% accuracy at 336 CPM. That's the best accuracy-per-dollar of any tool here outside Parallel and OpenAI, and its cost sits near the bottom of the table.

**Best for:** privacy-sensitive apps and cost-conscious teams that want a truly independent index and will handle extraction themselves.

**Tradeoffs:** it returns human-oriented results rather than token-dense agent excerpts, so your agent pairs it with a separate fetch step for multi-hop work, and its index is smaller than the web giants'.

### 4. Exa

Exa is a neural, embeddings-based search API that matches on meaning rather than exact keywords, with a fast Exa Instant tier for latency-sensitive calls. Only its Search endpoint is in scope here. It's popular across AI developer tooling for semantic discovery.

On BrowseComp, Exa scored 33.7% accuracy at 966 CPM, the highest cost in the table. The picture flips on simpler workloads. On the single-hop SimpleQA set Exa reached 89.3%, and on FRAMES it led the field at 88.3%, so its ranking depends heavily on the kind of question you throw at it.

**Best for:** semantic discovery and single-hop lookups where meaning-based matching shines.

**Tradeoffs:** on this multi-hop agentic set it's the most expensive per request while trailing Parallel and OpenAI on accuracy, and its strength is workload-dependent.

### 5. SerpAPI

SerpAPI scrapes Google and around 100 other engines, then returns structured search-engine-results-page (SERP) JSON with blue-link results, features, and metadata. It's search-only, with no agent-native excerpts, and it's well established for rank tracking and SERP monitoring. Its plans sell monthly search quotas, and some cap usage per hour.

On BrowseComp, tested July 10 to 12, 2026, SerpAPI scored 23.3% accuracy at 296 CPM, the lowest cost in the table.

**Best for:** SEO rank tracking and structured SERP data collection, more than agent reasoning.

**Tradeoffs:** it returns human SERP snippets rather than reasoning-ready excerpts, so your agent does more parsing and takes more hops, it posted the second-lowest accuracy on the agentic set, and quota caps can bite at scale.

### 6. Tavily

Tavily is a search API built for LLMs and retrieval-augmented generation (RAG), with optional AI answer summaries, domain filtering, and content extraction. We tested its Ultra Fast tier. It's widely adopted across agent frameworks and offers a developer-friendly free tier.

On BrowseComp, Tavily scored 19.3% accuracy at 822 CPM, the lowest accuracy and second-highest cost in the table.

**Best for:** quick RAG grounding and straightforward single-hop retrieval where ease of setup matters most.

**Tradeoffs:** on this multi-hop agentic set it trailed the field on accuracy while costing more per request, and community reports note that result quality can vary and fresh, high-quality links aren't always guaranteed.

## Cost and accuracy at a glance

The table below pairs the BrowseComp cost and accuracy figures with a few qualitative columns. CPM reflects LLM plus tool cost during the eval and is not any vendor's list price, so treat it as the cost of running the test. Parallel's own list price, as of July 24, 2026, is $5 per 1,000 requests, which is a separate number from the 350 CPM below.

Tested on BrowseComp, July 10 to 12, 2026.

## Run the test on your own queries

You can settle this for your own agents in about a day. This methodology measures what matters, which is whether the agent finished the job. Each step is expanded in our guide to benchmarking web search APIs[guide to benchmarking web search APIs](/articles/how-to-benchmark-web-search-apis).

1. Sample 50 to 100 real production queries from your logs. Pick the questions your agents receive in production, so the test reflects your traffic.
2. Run each API head-to-head with its default configuration. Keep the agent, the prompts, and the query set identical across tools so the search API is the only variable. Keep the model constant and swap only the tool.
3. Judge end-task success. A high recall score means little if the agent still gave the wrong final answer, so grade the completed task.
4. Measure cost and latency per successful task. A cheap request that fails and forces three more calls costs more than one accurate call, so divide total spend by tasks that succeeded.
5. Rerun the test periodically. Providers change indexes, pricing, and models constantly, so any benchmark, including this one, ages. A quarterly rerun keeps your choice honest.

If you want a starting scaffold, our cookbook shows how to build a search agent[build a search agent](/blog/cookbook-search-agent) end to end, and our notes on how to structure your search objectives[structure your search objectives](/articles/openclaw-best-practices-web-search) help you get comparable results across tools.

## Common questions about AI search for agents

**What is an agentic web search API?** It's a web search endpoint your agent calls as a tool during its reasoning loop. The agent sends a query, reads the returned URLs and excerpts, and decides its next step, often chaining many calls to answer one hard question. Our explainer covers what a web search API does[what a web search API does](/articles/what-is-a-web-search-api) in more depth.

**How is it different from a SERP API?** A SERP API returns the structured contents of a search-engine results page, built for humans and rank tracking. An agentic search API returns results shaped for a model's context window, favoring dense, relevant excerpts over blue-link snippets, so your agent parses less and reasons more.

**Why not just use my model's built-in web search?** Built-in search is convenient and can score well, as OpenAI's BrowseComp result shows. The tradeoff is control and cost. You often can't cap how many searches run or how many context tokens you pay for, and you're tied to one model vendor. A standalone API lets you swap search independently of your model.

**What should I optimize for, accuracy or cost?** Optimize for cost per successful task, which folds both together. The cheapest request that fails and triggers retries can cost more than one accurate call. Measure spend against completed tasks on your own workload rather than sticker price per request.

**How often should I re-evaluate?** At least quarterly, and after any provider announces a major model or index change. Rankings move as vendors ship updates, so a choice that was right last quarter deserves a fresh look this quarter.

## Get started

You don't have to trust our numbers. Run the head-to-head test on your own queries and let the results decide. When you're ready to include Parallel in that comparison, our free tier gives you up to 16,000 searches with no credit card required, enough to run a full evaluation and see the figures on your own workload.