# Parallel vs. Tavily

Parallel scores higher than Tavily on hard, multi-hop research at a fifth to an eighth of the price per search, and adds deep research, entity discovery, and monitoring on one platform.

[Start building for free](https://platform.parallel.ai/) 

[Fast-tier search, per 1,000 requests: $1 vs. $5–$8](#cost) [Artificial Analysis Search Index, basic modes: 73 vs. 66](#accuracy) [BrowseComp, same frontier agent: 74% vs. 66%](#accuracy)

Last updated October 1, 2026

## What does Parallel do?

Both return LLM-ready web results. Tavily bills credits that vary by depth and plan. Parallel bills a flat rate per request, and its research APIs return a citation and confidence score on every field.

* [At a glance](#at-a-glance)
* [Accuracy](#accuracy)
* [Cost](#cost)
* [Research](#research)
* [Features](#features)
* [Which to pick](#decision)

## Parallel vs. Tavily at a glance

Published list prices and documented capabilities as of October 2026.

__Parallel and Tavily compared at a glance__
| Dimension                                                   | Parallel                                                              | Tavily                                                      |
| ----------------------------------------------------------- | --------------------------------------------------------------------- | ----------------------------------------------------------- |
| Search list price, per 1,000 requests                       | $1 (Turbo or Fast); $5 (Basic or Advanced) (Leads)                    | $5–$8 basic, fast, or ultra-fast; $10–$16 advanced, by plan |
| Artificial Analysis Search Index (independent, August 2026) | 73 basic; 75 advanced (Leads)                                         | 66 basic, the only Tavily mode tested                       |
| BrowseComp, Parallel’s September 2026 run                   | 74% (Advanced); 44% (Fast) (Leads)                                    | 66% frontier tier; 32% low-cost tier                        |
| Citations on research output                                | Research Basis per field: URL, excerpt, reasoning, confidence (Leads) | Report-level citations in four styles                       |
| Deep research throughput                                    | 2,000 Task runs per minute (Leads)                                    | 20 research tasks per minute                                |
| Monitoring and entity discovery                             | Monitor, FindAll, and Entity Search APIs (Leads)                      | No dedicated endpoints documented                           |
| Site map and crawl                                          | No crawl endpoint                                                     | Map and Crawl endpoints (Leads)                             |
| Framework ecosystem                                         | Vercel AI SDK, n8n, LangChain, LlamaIndex, and more                   | LangChain, LlamaIndex, CrewAI, and 20+ more (Leads)         |

Sources: [Parallel pricing](https://parallel.ai/pricing), [Artificial Analysis Search Index launch results](https://artificialanalysis.ai/articles/search-api), [Parallel benchmarks, September 2026](https://parallel.ai/benchmarks), [Research Basis docs](https://docs.parallel.ai/task-api/guides/access-research-basis), [Parallel rate limits](https://docs.parallel.ai/getting-started/rate-limits), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api)

## Ahead where the queries get hard

Parallel’s September 2026 runs gave every provider the same agent and the same search and extract tools. With a GPT-5.6 Sol agent, Parallel Advanced scored 74% on BrowseComp against Tavily’s 66%, and 97% on SimpleQA Verified against 92%, at less than half of Tavily’s measured cost on both.

With the cheaper GPT-5.6 Luna agent the results are closer, and we report them as measured: Parallel Fast led BrowseComp 44% to 32%, tied Tavily at 94% on SimpleQA Verified, and trailed on WideSearch, 45.5 to 47.9\. It did all three at an eighth of Tavily’s cost or less.

The independent Artificial Analysis Search Index tested Tavily in its basic mode. In its August 2026 launch table Tavily basic scored 66, against 73 for Parallel basic and 75 for Parallel advanced. On the three components Parallel basic scored DeepSearchQA F1 79 against 74, BrowseComp 73 against 59, and AA-Omniscience 68 against 64, and took 20.4 seconds per task against 36.3.

Tavily publishes its own runs, most recently on SealQA and SimpleQA Verified, scored with a single-pass reader model rather than an agent. Different harnesses rank providers differently, which is why [Harvey](/ai/blog/case-study-harvey) and [Kepler](/ai/blog/case-study-kepler) evaluated on their own professional research before building on Parallel.

Sources: [Parallel benchmarks, September 2026](https://parallel.ai/benchmarks), [Artificial Analysis Search Index launch results](https://artificialanalysis.ai/articles/search-api), [Harvey case study](https://parallel.ai/blog/case-study-harvey), [Kepler case study](https://parallel.ai/blog/case-study-kepler)

+8 points on BrowseComp, same frontier agent

BrowseComp, frontier agent

Higher is better

Parallel: 74%

Tavily: 66%

SimpleQA Verified, frontier agent

Higher is better

Parallel: 97%

Tavily: 92%

Artificial Analysis Search Index (independent)

Higher is better

Parallel: 73 (basic)

Tavily: 66 (basic)

BrowseComp and SimpleQA Verified rows: Parallel-run evaluation, September 9, 2026, a GPT-5.6 Sol agent (reasoning: high) calling each provider’s search and extract tools, graded by an LLM judge, on samples of 50 BrowseComp and 100 SimpleQA Verified questions, Parallel Advanced against Tavily in the same harness. Artificial Analysis row: that firm’s launch table (August 18, 2026), a GPT-5.6 Luna agent over DeepSearchQA, a 200-question BrowseComp subset, and AA-Omniscience, with the search provider as the only variable; basic mode is the only Tavily configuration it tested.

## A fifth to an eighth of the price on the fast tiers

Tavily sells credits. A basic, fast, or ultra-fast search costs 1 credit and an advanced search costs 2, at $0.005 to $0.008 per credit depending on plan, so 1,000 fast, ultra-fast, or basic searches cost $5 to $8 and 1,000 advanced searches $10 to $16\. Parallel bills per request with no plan: $1 per 1,000 on Turbo or Fast, and $5 on Basic or Advanced. Matched tier for tier, that is $1 against $5 to $8 on the fast tiers, $5 against $5 to $8 on basic, and $5 against $10 to $16 on advanced.

Credits also make the bill harder to predict. Tavily’s auto\_parameters setting can promote a request to advanced depth, which doubles its cost, and Tavily Research is priced dynamically at 4 to 250 credits per request. Every Parallel processor has a fixed price, so the cost of a run is known before it starts.

Measured spend points the same way. In Artificial Analysis’s launch table, search spend per 1,000 benchmark tasks was $126 for Tavily basic against $45.14 for Parallel basic and $13.64 for Parallel turbo. In Parallel’s September run, Parallel Fast matched Tavily’s 94% on SimpleQA Verified for $2 per 1,000 questions against $17.40.

Sources: [Parallel pricing](https://parallel.ai/pricing), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api), [Parallel benchmarks, September 2026](https://parallel.ai/benchmarks), [Artificial Analysis Search Index launch results](https://artificialanalysis.ai/articles/search-api)

5–8× lower list price per 1,000 fast-tier searches

Fast tiers, per 1,000 requests

Lower is better

Parallel: $1

Tavily: $5–$8

Basic tier, per 1,000 requests

Lower is better

Parallel: $5

Tavily: $5–$8

Advanced tier, per 1,000 requests

Lower is better

Parallel: $5

Tavily: $10–$16

SimpleQA Verified at equal 94% accuracy, per 1,000

Lower is better

Parallel: $2

Tavily: $17.40

Published list prices, October 2026\. Tavily prices search in credits, 1 for basic, fast, or ultra-fast and 2 for advanced, at $0.008 pay-as-you-go down to $0.005 on the $500-a-month Growth plan; bar lengths use the pay-as-you-go rate, and on Growth, Tavily basic matches Parallel Basic at $5\. Equal-accuracy row: Parallel’s September 9, 2026 run with a GPT-5.6 Luna agent, LLM tokens plus tool calls per 1,000 questions on a 100-question sample, Parallel Fast against Tavily, both at 94%. Enterprise pricing differs on both platforms.

## Deep research that runs at batch scale

Tavily Research is a single endpoint with three model settings (mini, pro, and auto), and new tasks are capped at 20 per minute. Parallel’s Task API accepts 2,000 runs per minute by default across nine fixed-price processors, from Lite at $5 per 1,000 to Ultra8x at $2,400, so enriching a whole dataset is a matter of throughput, not a queue.

Both return structured output against a JSON Schema. Parallel also returns a Research Basis on every field: the source URL, the supporting excerpt, the reasoning, and a calibrated confidence score, so a pipeline can send low-confidence values to review instead of trusting a whole report. Tavily cites sources at the report level, in numbered, MLA, APA, or Chicago style.

Task Groups fan thousands of runs out over a reusable Task Spec and report group status as they complete, and the same platform carries on into FindAll for entity discovery and Monitor for change tracking, neither of which Tavily documents an endpoint for.

Sources: [Parallel rate limits](https://docs.parallel.ai/getting-started/rate-limits), [Parallel Task docs](https://docs.parallel.ai/task-api/task-quickstart), [Research Basis docs](https://docs.parallel.ai/task-api/guides/access-research-basis), [Parallel FindAll docs](https://docs.parallel.ai/findall-api/findall-quickstart), [Parallel Monitor docs](https://docs.parallel.ai/monitor-api/monitor-quickstart)

100× higher default rate limit on research runs

Research runs created per minute, default

Higher is better

Parallel: 2,000

Tavily: 20

Default limits from each provider’s documentation, October 2026\. Parallel counts Task Runs created per minute, and GET polling does not count. Tavily counts research tasks created per minute, on development and production keys alike. Both offer custom limits on enterprise plans.

## Feature by feature

Pricing, capabilities, performance, integrations, and enterprise readiness, side by side.

### Pricing and total cost

Parallel bills per request at the tier you pick. Tavily draws every endpoint from one credit balance, priced by plan, with depth and model settings changing how many credits a call uses.

__Pricing and total cost: Parallel compared with Tavily__
| Feature                       | Parallel                                         | Tavily                                             |
| ----------------------------- | ------------------------------------------------ | -------------------------------------------------- |
| Cheapest search, per 1,000    | $1 Turbo or Fast; $5 Basic or Advanced (Leads)   | $5–$8 basic, fast, or ultra-fast; $10–$16 advanced |
| Results included per search   | 10, with more billed per result                  | Up to 20 at the same credit cost (Leads)           |
| Extraction, per 1,000 URLs    | $1, excerpts or full markdown                    | $1.00–$1.60 basic; $2.00–$3.20 advanced            |
| Deep research, per 1,000 runs | $5–$2,400 across 9 fixed-price processors        | Dynamic, 4–250 credits per request                 |
| Pricing model                 | Pay per request, no plan or credits              | Credits, pay-as-you-go or $30–$500 monthly plans   |
| Free tier                     | Up to $80 at signup, plus $5 every month (Leads) | 1,000 credits every month, no card required        |

Sources: [Parallel pricing](https://parallel.ai/pricing), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api)

### Core capabilities

Both platforms cover search, extraction, and research. Tavily adds site mapping and crawling; Parallel adds per-field evidence, batch orchestration, entity discovery, and monitoring.

__Core capabilities: Parallel compared with Tavily__
| Feature                    | Parallel                                                        | Tavily                                                      |
| -------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------- |
| Search input               | An objective plus up to five keyword queries (Leads)            | One query string                                            |
| Search depths              | Turbo, Fast, Basic, Advanced                                    | Ultra-fast, fast, basic, advanced                           |
| Answers in search          | Not in Search; the Responses API returns cited answers          | Optional LLM answer on any search (Leads)                   |
| Images in search           | Not offered                                                     | Optional image results (Leads)                              |
| Citations on research      | Research Basis per field, with calibrated confidence (Leads)    | Report-level citations in four styles                       |
| Structured research output | Task Specs with input and output schemas                        | JSON Schema output on Research                              |
| Batch orchestration        | Task Groups with group status and bulk retrieval (Leads)        | Not documented                                              |
| Monitoring                 | Monitor API: event stream plus snapshot diffs (Leads)           | No dedicated endpoint documented                            |
| Entity discovery           | Entity Search for candidates; FindAll for verified sets (Leads) | No dedicated endpoint documented                            |
| Site map and crawl         | Crawler is internal to the index, not an endpoint               | Map and Crawl with depth, breadth, and path filters (Leads) |
| Source controls            | Domain include and exclude, freshness date, live fetch          | Domain filters, topic, date range, country, language        |

Sources: [Parallel Search docs](https://docs.parallel.ai/search/search-quickstart), [Parallel Extract docs](https://docs.parallel.ai/extract/extract-quickstart), [Research Basis docs](https://docs.parallel.ai/task-api/guides/access-research-basis), [Parallel FindAll docs](https://docs.parallel.ai/findall-api/findall-quickstart), [Parallel Monitor docs](https://docs.parallel.ai/monitor-api/monitor-quickstart), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api)

### Performance and scale

Per call, the fastest tiers are close: in Parallel’s July 2026 run Tavily Ultra Fast measured 150–357ms p50 against Turbo’s 216–240ms, quicker on two of five suites, while Turbo was more accurate on all five. Tavily allows more search requests per minute by default; Parallel allows far more research runs.

__Performance and scale: Parallel compared with Tavily__
| Feature                                        | Parallel                                | Tavily                                      |
| ---------------------------------------------- | --------------------------------------- | ------------------------------------------- |
| Search latency, advertised                     | \~200ms (Turbo); \~700ms (Fast)         | 180ms p50                                   |
| Search latency, measured p50                   | 216–240ms (Turbo) across five suites    | 150–357ms (Ultra Fast) across the same five |
| BrowseComp at the fastest tier, July 2026 run  | 51% Turbo (Leads)                       | 19.3% Ultra Fast                            |
| Artificial Analysis Search Index, August 2026  | 75 advanced; 73 basic; 67 turbo (Leads) | 66 basic                                    |
| Artificial Analysis time per task, basic modes | 20.4s (Leads)                           | 36.3s                                       |
| Search rate limit                              | 600 RPM                                 | 1,000 RPM on production keys (Leads)        |
| Research rate limit                            | 2,000 RPM (Leads)                       | 20 RPM                                      |

Sources: [Parallel Search Turbo benchmark run](https://parallel.ai/blog/parallel-search-turbo), [Artificial Analysis Search Index launch results](https://artificialanalysis.ai/articles/search-api), [Parallel rate limits](https://docs.parallel.ai/getting-started/rate-limits), [Parallel pricing](https://parallel.ai/pricing)

### Integrations and ecosystem

Tavily has the broader framework footprint, and many agent frameworks ship it as the default search tool. Both offer SDKs, a CLI, and a hosted MCP server with keyless access; Parallel adds an OpenAI-compatible Responses route.

__Integrations and ecosystem: Parallel compared with Tavily__
| Feature                 | Parallel                                                 | Tavily                                                         |
| ----------------------- | -------------------------------------------------------- | -------------------------------------------------------------- |
| SDKs                    | Python and TypeScript                                    | Python and TypeScript                                          |
| CLI                     | Search, extraction, research, enrichment, and monitoring | Search, extraction, crawl, map, and research                   |
| Framework coverage      | Vercel AI SDK, n8n, LangChain, LlamaIndex, and more      | LangChain, LlamaIndex, CrewAI, Vercel AI SDK, 20+ more (Leads) |
| MCP                     | Free keyless hosted Search MCP, plus Task MCP            | Hosted MCP with OAuth, API key, or keyless access              |
| OpenAI-compatible route | Responses API with streaming and follow-ups (Leads)      | Not documented                                                 |

Sources: [Parallel Responses docs](https://docs.parallel.ai/responses-api/responses-quickstart), [Parallel MCP docs](https://docs.parallel.ai/integrations/mcp/quickstart), [Parallel CLI docs](https://docs.parallel.ai/integrations/cli), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api)

### Security, compliance, and support

Both hold SOC 2 Type II and offer zero data retention. They differ on health data: Parallel is HIPAA compliant, while Tavily’s platform terms bar protected health information from requests.

__Security, compliance, and support: Parallel compared with Tavily__
| Feature                  | Parallel                 | Tavily                                             |
| ------------------------ | ------------------------ | -------------------------------------------------- |
| SOC 2 Type II            | Yes                      | Yes                                                |
| HIPAA compliance         | Yes (Leads)              | Terms bar protected health information in requests |
| Zero data retention      | Enterprise               | Stated as the default (Leads)                      |
| Data Processing Addendum | Yes                      | On request                                         |
| Published SLA numbers    | Enterprise contract only | 99.99% uptime advertised; terms on request         |

Sources: [Parallel pricing](https://parallel.ai/pricing), [Parallel Trust Center](https://trust.parallel.ai), [Switching from Tavily to Parallel](https://parallel.ai/articles/tavily-to-parallel-search-api)

## Which one should you pick?

Both platforms are credible. Parallel is built for production agents where accuracy on hard queries, evidence, and unit cost decide. Tavily is a convenient default when your framework already wires it in.

### Pick Parallel when

* You run multi-hop research where accuracy decides, and want the higher score at a lower measured cost.
* You want a flat per-request price you know before the call, not credits that vary by depth and plan.
* You need a citation, reasoning, and a confidence score on every field of a research result.
* You want search, deep research, enrichment, entity discovery, and monitoring from one provider.
* You run batch research at volume and need thousands of task runs per minute.

### Pick Tavily when

* Your agent stack already reaches Tavily through LangChain or another of its framework integrations, and setup time matters most.
* You need to map or crawl a site’s pages through the API rather than query an index.
* You want an LLM-generated answer or image results returned inline with each search.
* You need more than 600 search requests per minute on a default production key.
* You want zero data retention without negotiating an enterprise agreement.

## Switching from Tavily

Tavily’s Search, Extract, and Research endpoints have direct Parallel counterparts. Map and Crawl do not, so keep a crawler for those jobs and port the rest first.

[Start your migration](https://platform.parallel.ai/) [Talk to our team](https://contact.parallel.ai/)

1. 01  
### Map the calls you already make  
Tavily Search becomes Parallel Search, Extract becomes Extract, and Research becomes Task or Responses. Turn the query string into an objective, and pass your tuned keywords alongside it as search queries.
2. 02  
### Translate the parameters  
Basic depth maps to Turbo or Basic and advanced to Advanced. Domain filters move into the source policy, and chunks per source becomes an excerpt character budget. Topic, answers, and images have no direct parameter.
3. 03  
### Validate before you commit  
New accounts earn up to $80 at signup plus $5 in credits every month, which covers a real evaluation on your own query mix before anything moves to production.

## FAQ

* **Is Parallel better than Tavily?**  
For accuracy on hard queries and for cost, yes. In the same harness Parallel scored higher on BrowseComp at both agent tiers, and the independent Artificial Analysis Search Index rates Parallel basic 73 against Tavily basic 66\. Parallel’s fast tiers list at a fifth to an eighth of Tavily’s. Tavily has the edge on framework integrations, site crawling, and default search rate limits.
* **Parallel vs. Tavily: which is cheaper?**  
Parallel, at published list prices. Tier for tier, search is $1 per 1,000 requests on Parallel Turbo or Fast against $5 to $8 for Tavily fast or ultra-fast, $5 against $5 to $8 on basic, and $5 against $10 to $16 on advanced. Tavily Research is priced dynamically per request, while Parallel’s Task processors have fixed prices from $5 to $2,400 per 1,000.
* **Can I migrate from Tavily to Parallel?**  
Yes. Search maps to Search, Extract to Extract, and Research to Task or Responses, with Python and TypeScript SDKs on both sides. Map and Crawl have no Parallel equivalent, and Tavily’s topic, answer, and image options have no direct parameter, so plan for those.
* **Is Tavily faster than Parallel?**  
Per call, it depends on the query. In Parallel’s July 2026 run Tavily Ultra Fast measured 150 to 357ms p50 across five suites against 216 to 240ms for Parallel Turbo, faster on two and slower on three, while Turbo was more accurate on all five. Per benchmark task, Artificial Analysis measured 20.4 seconds for Parallel basic against 36.3 for Tavily basic.
* **Where does Tavily still win?**  
Tavily has the broader framework ecosystem, led by LangChain, and ships Map and Crawl endpoints for whole-site extraction. It can return an LLM answer and images inline with search, allows 1,000 search requests per minute on production keys against Parallel’s 600, and states zero data retention by default.
* **How current is this comparison?**  
Parallel reviewed both providers’ live documentation and published price lists on October 1, 2026\. BrowseComp, SimpleQA Verified, and WideSearch figures come from Parallel’s September 9, 2026 runs, latency from its July 2026 run, and Search Index scores from Artificial Analysis’s August 2026 launch table. Both platforms ship often, so check the linked sources before you buy.

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