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Search API

# The best web search for your AI

## The highest accuracy web search API, built from the ground up for AIs

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Search Background
Agent

Why Parallel

## An agent is only as good as its context

Parallel returns the best context from the web

## Declare semantic objectives, not just keywords

AI tells Parallel Search exactly what it's looking for

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Agent called parallel_search
{
"objective": "Find technical guides or open source repos for implementing a transformer from scratch"
}{
"objective": ""
}

Parallel Search

vs Human

## Get back URLs ranked for token relevancy

Parallel surfaces the most information-dense pages for the agent's next action

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1
Best available EV under 50K : r/electriccars - Reddit
2
New Electric Crossovers and SUVs Under $50k - J.D. Power
3
AWD EVs Under $50,000 - MotorWeek
4
Cheapest Electric Cars of 2025 - Kelley Blue Book
5
AWD EV's Under $50,000 - YouTube

Parallel Search

Sponsored Results
2026 Subaru Solterra EV
The 2026 Toyota bZ - A Modern Masterpiece
Results
Used Electric Cars for Sale

vs Human

## Reason on compressed token efficient excerpts

Each URL is distilled into the highest-value tokens for optimal context windows

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[1706.03762] Attention Is All You Needhttps://arxiv.org/abs/1706.03762

Parallel Search

[1706.03762] Attention Is All You Needhttps://arxiv.org/abs/1706.03762

vs Human

We optimize every web token in the context window

This means agent responses are more accurate and lower cost

Search Playground
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[Search Playground](https://platform.parallel.ai/)
WISER-Search

CPM: USD per 1000 requests. Cost is shown on a Linear scale.

Parallel
Native
Exa
BrowseComp benchmark analysis: CPM: USD per 1000 requests. Cost is shown on a Linear scale. . Evaluation shows Parallel's enterprise deep research API for AI agents achieving up to 48% accuracy, outperforming GPT-4 browsing (1%), Claude search (6%), Exa (14%), and Perplexity (8%). Enterprise-grade structured deep research performance across Cost (CPM) and Accuracy (%). State-of-the-art enterprise deep research API with structured data extraction built for ChatGPT deep research and complex multi-hop AI agent workflows.

### About the benchmark

This benchmark, created by Parallel, blends WISER-Fresh and WISER-Atomic. WISER-Fresh is a set of 76 queries requiring the freshest data from the web, generated by Parallel with o3 pro. WISER-Atomic is a set of 120 hard real-world business queries, based on use cases from Parallel customers. Read our blog here[here]($https://parallel.ai/blog/search-api-benchmark).

### Distribution

40% WISER-Fresh
60% WISER-Atomic

### Evaluation

The Parallel Search API was evaluated by comparing three different web search solutions (Parallel MCP server, Exa MCP server/tool calling, LLM native web search) across four different LLMs (GPT 4.1, o4-mini, o3, Claude Sonnet 4).

## We optimize every web token in the context window

This means agent responses are more accurate and lower cost

Search Playground
P
[Search Playground](https://platform.parallel.ai/)

### Search MCP Benchmark (LP) 

| Series    | Model                        | Cost (CPM) | Accuracy (%) |
| --------- | ---------------------------- | ---------- | ------------ |
| Parallel  | GPT 4.1 w/ Prll Search MCP   | 21         | 74.9         |
| Parallel  | o4 mini w / Prll Search MCP  | 90         | 82.14        |
| Parallel  | o3 w / Prll Search MCP       | 192        | 80.61        |
| Parallel  | sonnet 4 w / Prll Search MCP | 92         | 78.57        |
| Native    | GPT 4.1 w / Native Search    | 27         | 70           |
| Native    | o4 mini w / Native Search    | 190        | 77           |
| Native    | o3 w / Native Search         | 351        | 79.08        |
| Native    | sonnet 4 w / Native Search   | 122        | 68.83        |
| Exa       | GPT 4.1 w/ Exa Search MCP    | 40         | 58.67        |
| Exa       | o4 mini w/ Exa Search MCP    | 199        | 61.73        |
| Exa       | o3 w/ Exa Search MCP         | 342        | 56.12        |
| Exa       | sonnet 4 w/ Exa Search MCP   | 140        | 67.13        |

CPM: USD per 1000 requests. Cost is shown on a Linear scale.

### About the benchmark

This benchmark, created by Parallel, blends WISER-Fresh and WISER-Atomic. WISER-Fresh is a set of 76 queries requiring the freshest data from the web, generated by Parallel with o3 pro. WISER-Atomic is a set of 120 hard real-world business queries, based on use cases from Parallel customers. Read our blog here[here]($https://parallel.ai/blog/search-api-benchmark).

### Distribution

40% WISER-Fresh
60% WISER-Atomic

### Evaluation

The Parallel Search API was evaluated by comparing three different web search solutions (Parallel MCP server, Exa MCP server/tool calling, LLM native web search) across four different LLMs (GPT 4.1, o4-mini, o3, Claude Sonnet 4).

# Powered by our own proprietary web scale index

With innovations in retrieval, crawling, indexing, and reasoning

  • - Billions of pages covering the full depth and breadth of the public web
  • - Millions of pages added daily
  • - Intelligently recrawled to keep data fresh

# The knowledge of the entire public web

in a single tool call

Integrated directly, or add our MCP Server[MCP Server]($https://docs.parallel.ai/integrations/mcp/programmatic-use)

Search Playground
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[Search Playground](https://platform.parallel.ai/)Docs
D
[Docs](https://docs.parallel.ai/search/search-quickstart)
Parallel Web Systems products
Parallel Web Systems announcements
[Copy]
curl https://api.parallel.ai/v1beta/search \
  -H "Content-Type: application/json" \
  -H "x-api-key: $PARALLEL_API_KEY" \
  -H "parallel-beta: search-extract-2025-10-10" \
  -d '{
    "objective": "Find latest information about Parallel Web Systems. Focus on new product releases, benchmarks, or company announcements.",
    "search_queries": ["Parallel Web Systems products","Parallel Web Systems announcements"],
    "max_results": 10,
    "max_chars_per_result": 10000
  }'

## Scale with unmatched price-performance

Get started with 20K free search requests

$.004 per request + $.001 per page extracted

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500
11,000
5
120
Pricing

$2.000

## Every control you need

across any web page

<script></script>
<script src="/recaptcha/api.js"></script>
<script></script>
<script></script>
<embed type="application/pdf">
<script></script>
<embed type="application/pdf">
<script src="/recaptcha/api.js"></script>
<script></script>
Extracting...

### Premium content extraction

Fetch content from PDFs and sites that are JS heavy or have CAPTCHAs

Live fetch
Off
On
max_age (hours)
24
fetch_timeout (seconds)
90

### Freshness policies

Set page age triggers for live crawls, with timeout thresholds to gaurantee latency

{
"title": "Nvidia Becomes First $5 Trillion Company - WSJ",
"excerpts": [
"Last updated: 2 days ago The tech giant owes much of its $4.89 trillion market capitalization to the use of its systems to train AI models. Now it's pushing deeper into ..."
]
}

### LLM friendly outputs

Choose between dense snippets or full page contents, in markdown LLMs understand

Policy
INCLUDE
NIST.GOV

### Source control

Pick which domains are included or excluded from your web search results

## Secure and trusted

0
### Zero data retention
### Soc 2 Type 2
### No training

## FAQ

Parallel Search (API) is the highest accuracy search engine for AIs. It allows developers to build AI apps, agents, and workflows that can search for and retrieve data from the web. It can be integrated into agent workflows for deep research across multiple steps, or for more basic single-hop queries.

Declarative semantic search lets agents express intent in natural language rather than construct keyword queries. Instead of "Columbus" AND "corporate law" AND "disability", an agent specifies: "Columbus-based corporate law firms specializing in disability care." The Search API interprets meaning and context, not just keywords, making it natural to integrate into agent workflows where you already have rich context from previous reasoning steps.

Parallel is the only Search API built from the ground up for AI agents. This means that agents can specify declarative semantic objectives and Parallel returns URLs and compressed excerpts based on token relevancy. The result is extremely dense web tokens optimized to engineer your agent’s context for better reasoning at the next turn. Agents using Parallel search produce answers with higher accuracy, fewer round trips, and lower cost.

We maintain a large web index containing billions of pages. Our crawling, retrieval, and ranking systems add and update millions of pages daily to keep the index fresh.

Yes, Parallel operates a web crawler to support the quality and coverage of the index. Our crawler respects _robots.txt_ and related crawling directives. Learn more about Parallel’s crawler here[Learn more about Parallel’s crawler here]($https://docs.parallel.ai/resources/crawler).

Dense excerpts are the most query relevant content from a webpage, compressed to be extremely token efficient for an agent. These compressed excerpts reduce noise by engineering an agent’s context window to only have the most relevant tokens to reason on - leading to higher accuracy, fewer round trips, and less token use.

End-to-end latency measures total time from agent input to final output, not single-search latency. Our semantic search architecture and dense snippets reduce the number of searches required to reach quality outputs. Two high-precision searches with Parallel beat three lower-quality attempts elsewhere—saving both time and tokens.