Parallel- [Parallel Search MCP vs. Exa MCP: a 2026 comparison](https://parallel.ai/articles/parallel-search-mcp-vs-exa-mcp)
Tags:Comparison
October 29, 2026
# Linkup vs. Parallel: comparing two accuracy-first search APIs
Linkup and Parallel are unusually similar. Both run their own index, sell search, extraction, and asynchronous deep research, and lead with benchmark accuracy. They split on price per request, deployment options, EU data processing, and which benchmark you read.
Tags:Comparison
## **Linkup or Parallel: which should you choose?**
Choose Parallel when cost per request, the range of research depths, or the breadth of APIs decides it. Its Search API starts at $1 per 1,000 requests against $5 for Linkup’s standard search. Task API runs start at $5 per 1,000 for quick enrichment, and its multi-minute tiers (pro at $100, ultra at $300) sit near Linkup Research’s $250 to $2,500. Choose Linkup when you want one search endpoint that returns results, a sourced answer, or structured JSON, a batch endpoint for mixed calls, pay-per-request access with no account, a private index over your own data, a documented Bring Your Own Cloud deployment, ISO 27001 and HIPAA compliance, or a larger free allowance, topped back up to $20 a month against Parallel’s $5. Parallel’s standard terms also let it train its models on customer inputs and outputs, not linked to any individual, which its zero-data-retention enterprise plans exclude. On the two independent multi-step benchmarks that test both, Parallel’s Advanced and Basic modes score above every Linkup depth.
Prices, limits, and benchmark figures in this comparison were checked against each vendor’s pages and the benchmark sites on October 27, 2026.
## **What does each product suite include?**
Linkup[Linkup] exposes five endpoints:
- - Search (synchronous web search, with flash, fast, standard, and deep depths)
- - Fetch (the content of a web page, with optional JavaScript rendering and a pro mode)
- - Research (asynchronous multi-step research that returns a precise answer or a report with citations)
- - Tasks (up to 100 Search, Fetch, or Research calls batched into one asynchronous job)
- - Extract (a seed URL turned into repeated structured rows, such as products or job listings, in closed beta)
Parallel[Parallel] exposes nine:
- - Search API (synchronous, four modes from about 200ms to about 3s)
- - Extract API (URLs to clean markdown, including JavaScript-heavy pages and PDFs)
- - Image Search API (images from across the web)
- - Decisions API (typed answers grounded in web evidence, up to 100 questions per request)
- - Task API (asynchronous deep research and enrichment against a typed schema, nine processor tiers)
- - Responses API (OpenAI-compatible research with cited answers)
- - FindAll API (verified entity list building)
- - Entity Search API (real-time people and company search)
- - Monitor API (scheduled change tracking)
Linkup’s Search, Fetch, and Research map closely to Parallel’s Search, Extract, and Task APIs. Parallel goes wider into entity discovery, monitoring, image search, and typed decisions. Linkup’s Tasks endpoint has no direct Parallel equivalent: it batches up to 100 mixed calls into one asynchronous job, which fits scheduled or overnight work.
## **How do the two Search APIs differ?**
Linkup’s Search takes a query and a depth. Four depths[Four depths] set the trade. Flash, in alpha, returns ranked sources from the index in under 200ms with no LLM. Fast is a one-shot retrieval at about 1s. Standard is a single-iteration agentic search at 1 to 3s, and deep runs multi-iteration search and scraping in 5 to 30s. You can restrict results with includeDomains or excludeDomains (up to 100 domains), bound them with fromDate and toDate, and ask for images alongside text.
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. Four modes[Four modes] set the trade: Turbo at about 200ms and $1 per 1,000 requests, Fast at about 700ms and the same $1, Basic at about 1s and $5 per 1,000, and Advanced at about 3s and $5 per 1,000, the default. Turbo supports English and Japanese only. A source policy handles domains and freshness, and a fetch policy chooses between the index and a live crawl.
## **What output shapes do you get?**
This is the cleanest functional difference at the search layer. Linkup’s outputType parameter gives you three shapes from the same endpoint. The searchResults option returns raw entries with URLs and content, sourcedAnswer returns a written answer with its sources, and structured returns data that conforms to a JSON schema you supply.
Parallel keeps those separate. Search returns results and excerpts only. Synthesized answers, including JSON that conforms to a schema, come from the Responses API[Responses API]. Typed research output comes from the Task API, where every field arrives with a Basis[Basis]: the citations behind it, the reasoning that produced it, and a confidence rating.
Which you prefer depends on how you 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.
## **Which is more accurate, Linkup or Parallel?**
Both companies lead with benchmark numbers. The vendor-run figures don’t line up cleanly, so the independent results carry more weight.
Linkup’s benchmarks page[benchmarks page] reports Linkup Fast at a 94% F1 score on Verified SimpleQA, ahead of the other sub-second search APIs it tested, and Linkup Research at 61% on SealQA-0, ahead of Parallel Ultra 8X at 56%. The same page reports Linkup ahead of Parallel on its own GTM and FinSearchComp boards, for example 82.4% against Parallel ultra’s 73.1% on FinSearchComp T2. Linkup published its original SimpleQA evaluation script[its original SimpleQA evaluation script] as open source, so you can rerun its accuracy claim yourself.
Parallel’s Search results on parallel.ai/benchmarks[parallel.ai/benchmarks] (evals run September 9, 2026) come from a multi-step agentic setup: a GPT-5.6 Sol agent with Advanced and Basic, a cheaper GPT-5.6 Luna agent with Fast and Turbo, search and extract tools, and an LLM judge on a 100-question sample of SimpleQA Verified. There, Advanced and Basic score 97%, Fast 94%, and Turbo 91%. Linkup isn’t in those runs, and Parallel hasn’t published its own SealQA result, so Linkup’s chart is the only SealQA-0 figure for Parallel.
Those aren’t comparable measurements. Linkup’s 94% is an F1 score from a single search setup, and Parallel’s figures are an agent’s accuracy on a sample of the same dataset, with room to search more than once. Every one of these figures is vendor-produced on a vendor-chosen harness.
### **What do the independent benchmarks show?**
The Artificial Analysis Search Index[Artificial Analysis Search Index] (latest data October 19, 2026) holds the answer model fixed at GPT-5.6 Luna and changes only the search API, across DeepSearchQA, BrowseComp, and AA-Omniscience. Parallel Search scores 75 in advanced mode, 73 in basic, 73 in fast, and 67 in turbo. Linkup Search scores 68 in standard and in fast, and 47 in flash, which Linkup labels alpha. Search cost per 1,000 benchmark tasks measured $8.41 for Parallel fast, $47.93 for Parallel advanced, and $61.15 for Linkup standard. Adding the answer model’s token cost, the totals are about $27.37, $59.22, and $81.67. Neither vendor leads that board. Exa (auto, dynamic highlights) and Perplexity Search (medium) score 80. Linkup’s rows were measured in September 2026 and Parallel’s in August, and the Linkup rows sit in the page’s full provider data rather than its default 12-row view.
Openbenchmarks[Openbenchmarks] publishes its harness and scoring code, and its multi-turn company search board (last updated October 2, 2026) includes two Linkup depths and all four Parallel modes. A fixed research agent answers 45 hand-labelled questions, each combining three or four constraints such as investor backing, headquarters, and founding period, with its search tool swapped for each vendor’s API. Every configuration ran each question three times, and a deterministic judge scores answers against a frozen gold set of companies, so precision and recall are measured directly.
| Configuration (search only) | F1 | Precision | Recall | Median cost per agent run (model plus search) |
|---|---|---|---|---|
| Parallel Basic | 46.5 | 88.7 | 34.4 | $1.12 |
| Parallel Advanced | 44.2 | 87.6 | 32.0 | $0.63 |
| Linkup Fast | 41.1 | 82.8 | 30.3 | $0.92 |
| Linkup Standard | 40.6 | 84.0 | 29.7 | $0.94 |
| Parallel Fast | 38.0 | 79.9 | 27.5 | $0.46 |
| Parallel Turbo | 34.7 | 80.0 | 24.8 | $0.42 |
On the search-only board, ranked by F1, Parallel Basic finishes first of the 20 configurations tested, 5.4 points ahead of Linkup Fast, which is wider than the trial-to-trial spread of about 2 points. Parallel Advanced, in third, leads Linkup by about 3 points. Linkup Fast (tied for fifth) and Standard (seventh) finish 2.6 to 3.1 points ahead of Parallel Fast (tenth) and about 6 points ahead of Turbo. Fast and Turbo cost about half as much per agent run as either Linkup depth. With page fetching turned on, Linkup Standard’s precision rises to 90.7, the highest on that board, and its F1 of 42.0 sits just below Parallel Basic (42.3) and Advanced (42.2), within noise.
The Openbenchmarks speed board[speed board] sends 300 company-news questions to each API. Its latest run (October 21, 2026) has Linkup fast at 96.7% accuracy and 1.57s mean latency, and Linkup standard at 92.0% and 2.55s. Parallel isn’t in that run. Openbenchmarks now flags a timing bias in its company-news question set[company-news question set] and says it’s re-running every vendor. On the same page’s 100-question developer board, Parallel turbo completes 64.7% of tasks and Parallel fast 66.7%, against 43.3% for Linkup fast.
Put together, Parallel’s higher modes score above Linkup on both multi-step boards, and Parallel Fast scores above both Linkup depths on Artificial Analysis while trailing them on Openbenchmarks. On single company-news lookups, Linkup fast reached 96.7% accuracy at around a second and a half in a run that did not include Parallel, while on the developer board Parallel Turbo and Fast completed more tasks than Linkup fast. Run both against a sample of your own production queries to answer the question for your workload.
### What about finance benchmarks?
Linkup's benchmarks page[benchmarks page] claims Linkup Research leads both FinSearchComp tiers it ran, at 82.4% on T2 and 58.3% on T3, comparing each provider's best research or deep configuration. Its T2 list puts Parallel ultra at 73.1%, Perplexity at 72.3%, Exa research_pro at 42.0%, and Tavily research_pro at 40.3%. Those are research products, each returning a finished answer with its own model and orchestration, measured in Linkup's harness.
Parallel's October 2026 finance results, published with Parallel for Finance[Parallel for Finance], measure the search layer instead: every search API runs inside the same agent loop with the same answer model and judge. In those runs Parallel Advanced was the most accurate search API on FinSearchComp T3 at 57.4%, 6.0 points ahead of the next, at $161.10 per 1,000 questions, and scored 79.9% on T2. Linkup wasn't in those runs. We make Parallel and ran these evaluations; the methodology is on parallel.ai/benchmarks[parallel.ai/benchmarks].
The two kinds of number answer different questions. Linkup's tells you how its research endpoint's finished answers score, and ours tells you how much a retrieval layer helps an agent and model you control. The harnesses, question subsets, and graders differ, so they don't belong on one leaderboard. The FinSearchComp explainer[FinSearchComp explainer] covers both.
## **How do Linkup Research and the Parallel Task API compare for deep research?**
Linkup’s Research endpoint[Research endpoint] runs asynchronously and returns either a precise answer or a report with citations. Four sizes set depth and price: S at $0.25 per call (2 to 5 minutes), M at $0.50 (3 to 7 minutes), L at $1.50 (5 to 10 minutes), and XL at $2.50 (10 to 20 minutes). That’s $250 to $2,500 per 1,000. Linkup’s pricing page gives a shorter range of 1 to 10 minutes, so check the size you’d use.
Parallel’s Task API covers the same job with nine processor tiers[processor tiers] over a much wider range: lite at $5 per 1,000 runs, base at $10, core at $25, core2x at $50, pro at $100, ultra at $300, ultra2x at $600, ultra4x at $1,200, and ultra8x at $2,400. Latency runs[Latency runs] from about 10 seconds on lite to as long as two hours on ultra8x, and you pay only for successful runs. Matched on latency, Parallel pro (2 to 10 minutes, $100 per 1,000) and ultra (5 to 25 minutes, $300) sit around Linkup Research S ($250) and M ($500). Below that, lite and base cover quick enrichment that Linkup Research doesn’t price, which makes per-row enrichment practical at high volume.
Parallel also offers the Responses API for synchronous research. It’s OpenAI-compatible and priced by reasoning effort at $10, $50, or $250 per 1,000 requests, with latency from about 5 seconds to about a minute. Linkup’s Research endpoint is asynchronous only, but its synchronous deep search[deep search] (5 to 30s, $50 to $55 per 1,000) runs multi-iteration search and can return a sourced answer.
## **Where can each one run, and where is data processed?**
Linkup lists two enterprise deployment modes on its homepage[its homepage]. Private Index builds a dedicated, access-controlled index over your own data, so retrieval spans your corpus without exposing it. Bring Your Own Cloud runs the indexing layer inside your infrastructure. Linkup’s security documentation[security documentation] says BYOC is for selected high-tier deployments with strict data sovereignty needs, that it’s scoped case by case, and that queries don’t leave the customer’s environment.
Parallel is a hosted API by default. Its FAQ[FAQ] says private-cloud and on-prem options are available for qualified enterprise customers, and that Parallel focuses on public web information rather than indexing private sources. On private data, Linkup is ahead. Private Index is a listed product, and Parallel says only that it plans tools to point it at your own sources.
Both vendors can keep processing in Europe. Linkup runs infrastructure in the US, EU, Canada, and APAC. By default[By default], queries may be processed across those regions, and enterprise agreements can guarantee local processing in a specified geography.
Parallel’s EU data residency[EU data residency] runs Search and Extract in the EU when you call eu.parallel.ai under an enterprise agreement. It isn’t available on self-serve plans. Search there must use basic mode, returns up to 20 results, answers from the EU search index with no live fetches, and doesn’t retain request or response content. Extract takes up to 20 URLs and looks up each one in a cache Parallel hosts outside the EU, sending only the URL. Live fetch of uncached pages works the same way unless you run cache-only. The Task, Responses, FindAll, and Monitor APIs and the MCP servers aren’t served on the EU endpoint. Billing and metrics data, which carries no request or response content, goes to the US. A wider comparison is in Best web search APIs with EU data residency[Best web search APIs with EU data residency].
## **How much do Linkup and Parallel cost?**
Both charge per request with no plan to commit to, which makes the comparison unusually direct.
Linkup:
| Product | Price |
|---|---|
| Search | $5 per 1,000 for flash, fast, or standard results ($6 with a sourced answer or structured output). Deep: $50 to $55 per 1,000 |
| Fetch | $1 per 1,000 without JavaScript rendering, $5 per 1,000 with it. Pro mode: $5 or $10 per 1,000 |
| Research | $250 to $2,500 per 1,000 |
| Tasks | Each batched call billed at its own endpoint’s rate |
| x402 | $0.01 per Search or Fetch request in USDC, no account needed |
| Free credit | $20 at signup with a professional email, topped back up to $20 each month |
Parallel:
| Product | Price |
|---|---|
| Search | $1 per 1,000 in Turbo and Fast, $5 per 1,000 in Basic and Advanced, 10 results with excerpts included |
| Extract | $1 per 1,000 URLs, JavaScript rendering and PDFs included |
| Image Search | $1 to $5 per 1,000 |
| Decisions | $1 per 1,000 requests, up to 100 questions each |
| Task API | $5 to $2,400 per 1,000 runs |
| Responses API | $10 to $250 per 1,000, by reasoning effort |
| Monitor | $3 to $10 per 1,000 checks |
| Entity Search | $5 per 1,000 |
| FindAll | From $0.10 per run in preview. Base: $0.25 per run plus $0.03 per match. Pro: $10 per run plus $1 per match |
| Free credit | Up to $80 to earn at signup, plus $5 a month for eligible organizations with a card on file |
At the search layer, Parallel’s Turbo and Fast cost a fifth of Linkup’s $5 per 1,000, and Fast keeps most of Advanced’s quality at that price on the Artificial Analysis index (73 against 75), though on Openbenchmarks’ multi-turn board Fast scores 38.0 against Advanced’s 44.2, below both Linkup depths. Basic and Advanced match Linkup’s standard price, and Linkup’s deep search costs $50 per 1,000. Extraction is level at $1 per 1,000 for plain pages, and Parallel doesn’t charge extra for JavaScript rendering, where Linkup charges $5 per 1,000. On deep research, Parallel’s $5 lite tier has no Linkup Research equivalent, and at multi-minute depth the two price in the same range, with comparable ceilings.
Linkup’s $20 monthly top-up is four times Parallel’s $5. It buys about 4,000 Linkup searches, against up to 5,000 Turbo or Fast searches, or 1,000 in Basic or Advanced, on Parallel. Linkup is ahead on one payment option. x402[x402] lets an agent pay per Search or Fetch request in USDC on Base, with no account or API key, at a flat $0.01. That’s twice the API-key price of a standard search.
_Check official documentation for current pricing._
## **What are the rate limits and uptime commitments?**
Linkup commits to 99.9% uptime, SLA-backed, on its homepage[its homepage], and its default rate limit[default rate limit] is 10 queries per second per organization for Search and Fetch. Parallel publishes default limits[default limits] of 600 requests per minute for Search, Image Search, Extract, and Entity Search, 2,000 per minute for Tasks, 300 per minute for Monitor, and 300 per hour for FindAll runs, with GET polling excluded and custom limits on enterprise plans. Ten queries per second is 600 a minute, so the default search ceilings match.
## **How do they compare on compliance and training?**
Linkup holds SOC 2 Type II and ISO 27001 and is HIPAA compliant[holds SOC 2 Type II and ISO 27001 and is HIPAA compliant]. Its pricing page[pricing page] includes SOC 2 Type II on all plans and lists zero data retention with the Enterprise plan. SSO, guaranteed local processing, and IP whitelisting are enterprise options.
Parallel is SOC 2 Type I and Type II certified[SOC 2 Type I and Type II certified] and offers a Data Processing Addendum. Under the standard Customer Terms[Customer Terms], Parallel may use inputs and outputs, not linked to any individual, to train and improve the models behind its services. Enterprise plans with zero data retention exclude that use, and so does Search API traffic sent to the EU endpoint.
Both hold SOC 2 Type II. Linkup adds ISO 27001 and HIPAA compliance, and it runs inside your own cloud for selected deployments. For teams on Parallel’s standard plans, the training clause is the term to review.
## **What is the developer experience like?**
Linkup publishes[publishes] Python and TypeScript SDKs, a CLI, an OpenAI SDK integration, an MCP server, and integrations for LangChain, CrewAI, and the Vercel AI SDK. Parallel ships Python and TypeScript SDKs and an MCP server[MCP server], and its Responses API is OpenAI-compatible, so an existing OpenAI integration usually needs only a new base URL, key, and model name.
1234567891011import os
from parallel import Parallel
client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
search = client.search(
objective="your goal",
search_queries=["your keyword query"],
mode="turbo",
)``` import os from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) search = client.search( objective="your goal", search_queries=["your keyword query"], mode="turbo",)``` ## **When should you use each?**
Choose Linkup when the deployment model or the payment model is the constraint. Private Index is a capability Parallel doesn’t offer, and Linkup documents Bring Your Own Cloud in more detail than Parallel describes its private-cloud option. If your data can’t leave your VPC, little 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. Tasks suits scheduled bulk work, and x402 lets an agent pay its own way.
Choose Parallel when unit economics, research depth, and breadth decide it. Turbo and Fast cost $1 per 1,000 requests, a fifth of Linkup Search, which changes what you can afford inside an agent loop. Advanced and Basic score above every Linkup depth on both independent multi-step boards. Nine research processors run from $5 per 1,000 for quick enrichment to multi-minute tiers priced near Linkup Research, and the Basis[Basis] on every Task field gives per-field citations, reasoning, and confidence. FindAll, Entity Search, Monitor, Decisions, and a dedicated Image Search endpoint cover jobs Linkup doesn’t sell as separate products.
Two questions usually decide it. 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?
## **FAQ**
### **Which is better for AI web research, Linkup or Parallel?**
On the independent multi-step benchmarks, Parallel’s Advanced and Basic modes score above every Linkup depth, and Parallel’s Task API offers nine research tiers from $5 to $2,400 per 1,000 runs. Linkup suits teams that need Bring Your Own Cloud, ISO 27001, or HIPAA compliance.
### **How does Linkup pricing compare with Parallel?**
Linkup standard search costs $5 per 1,000 requests and deep search $50. Parallel Search costs $1 per 1,000 in Turbo and Fast and $5 in Basic and Advanced. Linkup tops eligible accounts back up to $20 a month, and Parallel gives $5 a month to eligible organizations plus up to $80 to earn at signup.
### **Does Parallel offer EU data residency?**
Yes, under an enterprise agreement. Requests to eu.parallel.ai for Search in basic mode and for Extract are processed in the EU, and request and response content isn’t retained. Extract’s cache lookups send only the URL outside the EU.
### **Does Linkup process queries in the EU?**
Linkup runs infrastructure in the EU and other regions. By default, queries may be processed in any of them. Guaranteed local processing in a chosen geography needs an enterprise agreement.
### **Which vendor can run inside my own cloud?**
Linkup documents Bring Your Own Cloud for selected high-tier deployments. Parallel’s FAQ says private-cloud and on-prem options are available for qualified enterprise customers, so ask both for specifics.
**Related reading: **Exa vs. Parallel[Exa vs. Parallel] / You.com vs. Parallel[You.com vs. Parallel] / Parallel vs. Perplexity[Parallel vs. Perplexity].