# Linkup vs. Parallel: comparing two accuracy-first search APIs

## **The product suites**

Linkup exposes five endpoints, each placed at a different point on the latency and quality curve:

• - Search (synchronous web search, with fast, standard, and deep settings)
• - Fetch (retrieve the content of a web page, with optional JavaScript rendering)
• - Research (asynchronous multi-source synthesis returning a report or a precise answer with inline citations; currently in beta)
• - Tasks (batch up to 100 Search, Fetch, or Research calls into one asynchronous job)
• - Extract (seed URL plus a query, returning repeated structured rows such as products or job listings; in closed beta)

Parallel exposes eight:

• - Search API (synchronous, three modes from ~200ms to ~3s)
• - Extract API (URLs to clean markdown, including JS-heavy pages and PDFs)
• - Task API (asynchronous deep research against a typed schema, nine processor tiers)
• - Responses API (OpenAI-compatible agentic research with cited answers)
• - FindAll API (verified entity list building)
• - Entity Search API (real-time people and company search)
• - Monitor API (scheduled change tracking)

The first three lines match almost exactly. Parallel goes wider into entity discovery, monitoring, and web-grounded answers. Linkup's Tasks endpoint has no direct Parallel equivalent: it batches a hundred mixed calls into a single asynchronous job, which is a clean fit for scheduled or overnight work.

## **Search**

Linkup's Search takes a query and a depth. Standard returns faster; deep runs a more thorough multi-step search. You can restrict results with includeDomains (up to 100) or excludeDomains, bound them with fromDate and toDate, and ask for images alongside text. Linkup's documentation describes standard search as sub-second; its pricing page lists the Search endpoint at 1 to 3 seconds, so plan against your own measurements rather than either figure.

Parallel's Search takes a natural-language objective, optionally with explicit search queries, and returns ranked URLs with excerpts sized by max_chars_per_result and max_chars_total. Three modes set the trade: Turbo at ~200ms and $1 per 1,000 requests, Basic at ~1s and $5 per 1,000, Advanced at ~3s and $5 per 1,000 and the default. A Source Policy handles domains and freshness; a Fetch Policy chooses between the index and a live crawl.

## **Output shapes**

This is the cleanest functional difference at the search layer. Linkup's outputType parameter gives you three shapes from the same endpoint: searchResults returns raw entries with URLs and content, sourcedAnswer returns a written answer with the sources behind it and optional inline citations, and structured returns data conforming to a JSON schema you supply.

Parallel keeps those separate. Search returns results and excerpts only. Synthesized answers come from the Responses API; typed structured output comes from the Task API, where every field arrives with a Basis attached: the citations behind it, the reasoning that produced it, the excerpts it was drawn from, and a calibrated confidence score.

Which you prefer depends on how you like to build. One endpoint with a mode switch is fewer moving parts. Separate endpoints give you independent pricing, independent rate limits, and per-field provenance on the structured path.

## **The competing accuracy claims**

Both companies lead with benchmark numbers, and the numbers do not line up cleanly enough to declare a winner.

Linkup reports that Search ranks first among sub-second web search APIs on SimpleQA Verified with a 92% F-score, and that Research ranks first on SEAL-0 with 61% accuracy. Linkup publishes its SimpleQA evaluation harness as open source, which is worth crediting; almost nobody in this category does.

Parallel reports 91% accuracy on SimpleQA for Turbo at a 240ms median search latency, measured in a single-step setup where the raw question goes to search and the model answers from the results alone with roughly 1,000 characters per result. On SealQA, Parallel's published Task API results run from 42.3% on Core at $25 per 1,000 runs to 56.8% on Ultra8x at $2,400 per 1,000 on SEAL-0, and 60.6% to 70.1% on SEAL-HARD.

Read carefully, those are not comparable measurements. SimpleQA Verified is a curated 1,000-prompt subset, not the full 4,326-question SimpleQA set, and an F-score is not the same statistic as accuracy. On SEAL-0, Linkup's 61% is above the 56.8% Parallel published for Ultra8x, but Parallel's SealQA testing ran in October 2025 and the two systems were never run head to head. Every one of these figures is vendor-produced on a vendor-chosen harness.

The honest conclusion is that both are accuracy-first products with credible results, and that a benchmark leaderboard is not going to settle this one. Run both against a sample of your own production queries; it is a day of work and it answers the question properly.

## **Deep research**

Linkup's Research endpoint runs asynchronously over 2 to 20 minutes and returns either a detailed report or a precise answer with inline citations. It is priced at $0.25 to $2.50 per request, which is $250 to $2,500 per 1,000. It is in beta, and Linkup notes that behaviour and parameters may change.

Parallel's Task API covers the same job with nine processor tiers spanning a much wider range: lite at $5 per 1,000 runs and 10 to 60 seconds, through core at $25, pro at $100, ultra at $300, and up to ultra8x at $2,400 per 1,000 and as long as two hours. Fast variants of each trade some depth for tighter latency at the same price. The range matters because a lot of enrichment work does not need a twenty-minute research run; at $5 per 1,000, lite makes per-row enrichment viable at a volume that a $250 floor does not.

Parallel also offers the Responses API for synchronous research, OpenAI-compatible and priced by reasoning effort at $10, $50, or $250 per 1,000 requests with latencies from about 5 seconds to about a minute. Linkup's Research is asynchronous only.

## **Deployment, where Linkup goes further**

Linkup offers two enterprise deployment modes that Parallel does not match. A Private Index builds a dedicated, access-controlled index over your proprietary data, so retrieval spans your own corpus without exposing it. Bring Your Own Cloud runs the full indexing layer inside your own infrastructure, so queries never leave your environment at all.

For a bank, a government department, or a legal AI product working under strict data sovereignty rules, that is a decisive capability, and it shows in Linkup's customer list. Parallel is a hosted API. It offers zero data retention and a contractual commitment not to train on customer data, but the queries do go to Parallel's infrastructure.

## **Pricing**

Both charge per request with no plan to commit to, which makes the comparison unusually direct.

Linkup:

• - Search: **$5 to $6 per 1,000 requests** depending on depth
• - Fetch: **$1 per 1,000** without JavaScript rendering, $5 per 1,000 with it
• - Research: **$250 to $2,500 per 1,000**
• - Free tier: 4,000 queries a month, framed as a $20 monthly credit

Parallel:

• - Search: **$1 per 1,000** in Turbo, $5 per 1,000 in Basic and Advanced, 10 results with excerpts included
• - Extract: **$1 per 1,000 URLs**, JavaScript rendering and PDFs included
• - Task API: **$5 to $2,400 per 1,000 runs**
• - Responses API: $10 to $250 per 1,000, by reasoning effort
• - Monitor: $3 per 1,000 executions; Entity Search: $5 per 1,000; FindAll: fixed cost plus $0.03 to $1.00 per match
• - Free tier: $5 in credits a month, applied automatically, covering up to 5,000 Turbo searches

Three things fall out of that. At the search layer, Parallel Turbo is five to six times cheaper than Linkup Search, though Turbo is the fastest and shallowest of Parallel's modes; against Basic and Advanced the two are at parity. Extraction is level at $1 per 1,000 for plain pages, but Parallel does not charge extra for JavaScript rendering where Linkup charges five times as much for it. And on deep research, Parallel's floor of $5 per 1,000 sits well below Linkup's $250, while the ceilings are comparable.

The free tiers are closer than the headline numbers suggest. Linkup's $20 monthly credit is four times Parallel's $5, but because Turbo costs a fifth of Linkup Search, Parallel's credit buys roughly 5,000 searches against Linkup's 4,000.

_Note: For the latest pricing, always check official documentation._

## **Reliability and rate limits**

Linkup commits to 99.9% uptime backed by an SLA. Parallel publishes default rate limits of 600 requests per minute for Search, Extract, and Entity Search, 300 for Monitor, and 300 per hour for FindAll runs, with GET polling excluded and custom limits on enterprise plans. Linkup does not publish default per-endpoint quotas, so ask during evaluation if throughput matters to your design.

## **Compliance**

Linkup includes SOC 2 Type II on all plans and states that it does not train on user data. Zero data retention is available with the Enterprise plan, alongside IP whitelisting, a dedicated index refresh rate, and a private environment.

Parallel is SOC 2 Type 2 certified, offers a Data Processing Addendum and zero data retention, and commits contractually to not training on customer data, with a public status page and trust center.

The substantive difference is not the certification, which both hold, but the deployment envelope: Linkup will run inside your cloud, Parallel will not.

## **Developer experience**

Both are easy to adopt. Linkup publishes a public OpenAPI spec, ships Python and Node clients, and is wired into LangChain, CrewAI, LiteLLM, Haystack, and an OpenAI SDK wrapper. Parallel ships Python and TypeScript SDKs, an MCP server, and a playground, and its Responses API is OpenAI SDK-compatible, so an existing integration usually needs only a new base URL, key, and model name.

## **When to use each**

Choose Linkup when the deployment model is the constraint. Bring Your Own Cloud and Private Index are genuine capabilities that Parallel has no answer to, and if your data cannot leave your VPC, nothing else in this comparison matters. The outputType switch is a nice piece of design if you want search results, a written answer, and structured JSON from one endpoint. The batch Tasks endpoint suits scheduled bulk work, the open-source evaluation harness suggests a company confident in being checked, and SOC 2 Type II on every plan is a lower bar to clear than an enterprise contract.

Choose Parallel when unit economics and breadth decide it. Turbo runs at 200ms and $1 per 1,000 requests, a fifth to a sixth of Linkup Search, which changes what you can afford to do inside an agent loop. Nine research processors starting at $5 per 1,000 make per-row enrichment practical at volumes where a $250 floor does not, and the Basis on every Task field gives you per-field citations, reasoning, and confidence rather than document-level sources. FindAll, Entity Search, and Monitor cover list building and change tracking that Linkup does not sell, and zero data retention does not require an enterprise plan.

These two are close enough that published benchmarks will not decide it for you. The questions that will: does your data need to stay in your own cloud, and how much of your workload sits at the cheap, high-volume end of the curve rather than the deep-research end?

**Related reading: **Exa vs. Parallel[Exa vs. Parallel](/articles/exa-vs-parallel) · You.com vs. Parallel[You.com vs. Parallel](/articles/you-com-vs-parallel) · Perplexity Search API vs. Parallel Search API[Perplexity Search API vs. Parallel Search API](/articles/perplexity-search-api-vs-parallel-search-api).