8 Best Parallel.ai Alternatives & AI Competitors in 2026: Real Reviews

Key Takeaways

• TinyFish is the best Parallel.ai alternative for builders who need one platform to run the entire web workflow, from agentic search and fetch up to autonomous web agents that run authenticated, multi-step workflows even behind a login.
• Exa and Tavily fit teams that want neural semantic search or RAG-ready retrieval wired straight into an agent stack.
• Perplexity Sonar and Brave Search API suit teams that want grounded, cited answers (Sonar) or an independent, privacy-first index (Brave).
• You.com and Jina AI cover composable AI search with optional full-page content (You.com) and search plus deep research from one token-based key (Jina).
• SerpAPI is the pick when your agent needs real, structured multi-engine SERP data rather than a synthesized answer.

You might be hitting the wall where research isn't enough and your agent needs to act on the web. Or maybe those deep-research calls are have been running for 30 minutes when you needed an answer in 30 seconds. If these scenarios resonate, it's time to evaluate a Parallel.ai alternative.

The best Parallel.ai alternatives in 2026 are TinyFish, Exa, Tavily, Perplexity Sonar, Brave Search API, You.com, Jina AI, and SerpAPI.

This guide breaks down what each tool does well, where it falls short, real pricing, and honest user reviews, so you can match the right one to your stack.

Reasons to consider an alternative to Parallel.ai

Founded by ex-Twitter CEO Parag Agrawal and valued at $2 billion after a Sequoia-led round in early 2026, Parallel is an accuracy-focused agentic search API. It aggregates authoritative sources and returns compressed, token-dense excerpts, and its Basis framework attaches citations, rationale, and confidence scores to every answer, backed by SOC 2 provenance. For research and enrichment work, it earns its place.

Parallel is built for a specific job, though, and a few needs sit outside it. For you, it may be that:

• You need your agent to act on the web: Parallel searches, researches, extracts, and monitors the public web. It doesn't log into a portal, hold a session, fill a form, and complete a multi-step task on your behalf. If your agent's job is to operate behind authentication, TinyFish is the better fit.
• Your costs are hard to forecast: Parallel's Task API runs across nine processor tiers, from Lite at $5 per 1,000 requests up to Ultra8x at $2,400 per 1,000, and the bill climbs fast once you combine search, deep research, and extraction in one workflow. Mapping that to a monthly forecast takes a spreadsheet.
• Deep research is too slow for real time: Task API runs are asynchronous and can take anywhere from 5 seconds to 30 minutes depending on processor and query. An independent AIMultiple benchmark across 100 real-world AI queries clocked Parallel Search Pro at 13.6 seconds, the slowest of the eight providers tested, against 669ms for the fastest.
• You want open source or self-hosting: Parallel is a proprietary, closed index. Jina ships its Reader and models under a permissive open-source license you can run yourself, and TinyFish offers an on-prem option on its Enterprise plan.
• Inference cost is a bottleneck: If you forward fetched pages into an LLM thousands of times a day, TinyFish's token-efficient fetch cuts the downstream bill in a way a search API tuned for its own outputs can't.

So, the call to switch from Parallel usually starts with cost. If you can't forecast the monthly bill across its per-request tiers, or the token cost of every fetched page is adding up, TinyFish's free Search and Fetch change the math, and the case only grows if you also need action, real-time speed, or self-hosting.

8 best Parallel.ai alternatives and AI competitors in 2026

The table below recaps all eight tools at a glance, including the one job each does that Parallel's search-and-research APIs don't. Full breakdowns, real user reviews, pricing, and where each tool falls short follow underneath.

1. TinyFish: Best Parallel Alternative for Multi-step Operation

TinyFish is the best Parallel.ai alternative because it gives AI agents the web operating layer they need to search, read, extract, authenticate, and interact with the live web.

Where Parallel is an agentic search API, TinyFish escalates from reading the web into operating on it. It folds the proxies, anti-bot handling, headless browsers, reasoning, and agentic web interaction including form filling, filtering, and logging in into one platform and one credit pool, so you build workflows instead of juggling separate vendors.

TinyFish is infrastructure for web agents, the web operating layer rather than a chatbot or an agent you talk to. The primitives are the tools a Claude or a ChatGPT would reach for when it needs the live web.

TinyFish raised a $47 million Series A led by ICONIQ Capital in August 2025, with participation from USVP, MongoDB Ventures, and Sheryl Sandberg's Sandberg Bernthal Venture Partners.

Core capabilities

Where most search and research APIs cover the read path, TinyFish's capabilities include Search, Fetch, Browser, and Agent, with Vault and Profiles supporting authenticated workflows.

1. Agent-optimized search

TinyFish Search is agent-optimized web search: fast, structured for AI consumption, and returning live results an agent can act on directly. It runs standalone or as the input layer before a Fetch or an Agent call, so an agent can flow straight into reading or operating on a page rather than stopping at retrieval.

Parallel differs here. Its Search API aggregates authoritative sources into ranked, compressed excerpts from its own index, priced per request and kept to the read path, tuned to feed a model's context window.

Because TinyFish’s Search shares one API key and credit-free access with the rest of the platform, you can:

• Use these results to trigger deeper work, like pulling clean content from a page with Fetch or handing a site to the Web Agent to operate a larger workflow
• Customize searches by location and language to see results exactly as a local user would, region by region

2. Token-efficient Fetch

TinyFish Fetch is structured content extraction from any URL. It renders dynamic, JavaScript-heavy, and single-page-app content in a real browser and returns clean markdown, JSON, or HTML, LLM-ready by default, with the navigation, sidebars, link strings, and image embeds stripped out.

Every token you send downstream to an LLM costs money and latency, and models reason better when they aren't wading through irrelevant context.

In one benchmark, the same article came back at roughly 1,170 tokens through Fetch versus about 41,000 tokens via conventional extraction. At the scale of thousands of page visits per day, that gap can make or break the economics of an agent workflow.

Parallel's Extract API returns full page contents and compressed excerpts, which is useful. TinyFish tunes its output for the content an agent forwards into a model rather than the version a human reads.

3. Managed browser infrastructure

TinyFish Browser is a managed, stealth cloud Chrome session you drive directly with Playwright or the Chrome DevTools Protocol (CDP). It is infrastructure: managed sessions, stealth fingerprinting, proxies, and anti-bot handling that TinyFish runs so you don't stand up and maintain your own browser fleet.

Parallel is different in this regard. It’s built for search, research, and extraction rather than live browser sessions. Browser is the foundation the Web Agent runs on when a job needs a real, controllable page.

Because Browser shares one API key and session model with the rest of the TinyFish platform, you can:

• Drive pages directly with Playwright or CDP, scripting clicks, typing, and navigation that go beyond what retrieval alone can reach
• Reuse saved login state with Profiles, so a run picks up an authenticated session instead of signing in every time
• Run isolated sessions on managed cloud infrastructure, with proxies and anti-bot handling already wired in

4. Authenticated, multi-step action with Web Agent

The Web Agent is TinyFish's differentiated layer. Give it a natural language goal and a URL and it navigates live sites, fills forms, works through filters and pagination, authenticates, and returns structured results. Search APIs like Parallel find and read pages; the TinyFish’s Web Agent operates them and completes the task.

Vault and Profiles support that authenticated work. Vault lets the agent use authorized credentials or saved session context without exposing raw secrets to the model, and Profiles carries identity and reusable login state across runs.

Credentials are handled by the customer and resolved at request time, never exposed in the model's context, which keeps the security posture clean for enterprise use.

With that, an agent can pull quotes from a carrier portal, reconcile inventory in a supplier dashboard, or run recurring checks inside an authenticated account.

Parallel, by design, stays on the public read path and forms content partnerships with publishers for licensed access. It doesn’t log into your third-party portals to act inside them. If your agent's job is to operate on the web rather than find something, that is the dividing line. TinyFish's own piece on why 90% of the internet stays invisible makes the case for operating on the live web, beyond indexing it.

Pricing

Search and Fetch are free on every TinyFish plan and consume no credits. Agent and Browser runs are billed per credit: one credit covers one Agent step or four minutes of Browser time. Credits and steps stay one-to-one until a Browser session enters the workflow, since Browser meters by time rather than by step. Monthly plans cut the per-credit rate. Always confirm current rates on the TinyFish pricing page before committing.

Where TinyFish shines

• Recurring, multi-step agent workflows: Clean Fetch and credit-based pricing make daily and weekly jobs across many pages pay off at volume, the point where per-query search APIs strain. See how this plays out when fetching from a large URL list.
• Live, fresh data: TinyFish hits the live web on every run, so your agent reasons over what a page says right now instead of a cached snapshot or an index that refreshes on its own schedule. For price monitoring, inventory checks, and other time-sensitive work, that freshness is reflected in your results.
• Authenticated and gated sources: TinyFish reaches content behind logins, forms, and anti-bot systems through its Web Agent, with Vault and Profiles handling credentials and session state so your agent can sign in mid-run without exposing secrets to the model. This is the surface retrieval-only tools leave untouched.
• Scale and concurrency: TinyFish runs concurrent execution, bulk submission of up to 100 runs per request, and reusable profiles shared across many agents, so teams reach high volume without building their own orchestration layer.
• Speed with freshness and accuracy: Speed with freshness and accuracy: TinyFish Search returns results at 556 ms p50 latency, 3x faster than Parallel, measured across a 1,200-query production sample, and it posts the highest first-result accuracy on SimpleQA at 49.2% Snippet@1. Getting the answer right on the first pass means the agent finishes instead of stalling and retrying.

Pro Tip: To see how fast TinyFish responds compared with other search APIs, try the live Search Arena.

Explore data enrichment tools for recurring web workflows.

Where TinyFish falls short

• CAPTCHAs can still be a hard stop: TinyFish's stealth, proxy, and human-like execution patterns reduce the likelihood of CAPTCHA challenges, but TinyFish does not automatically solve reCAPTCHA, hCaptcha, or similar challenges today. If a site serves a CAPTCHA, the workflow may need fallback handling, manual intervention, or a different approach.
• Web Agent is overkill for simple public data: If you're scraping static, public, structurally stable pages, or hitting cooperative APIs at very high frequency, a plain HTTP request, Scrapy, BeautifulSoup, or Playwright can be cheaper and faster than a full agent workflow. TinyFish's own web agent vs. automation guide makes this distinction: use simple scrapers for stable public pages, Fetch for JavaScript-rendered pages, Browser for strict access or session requirements, and the Web Agent for authenticated or multi-step workflows.

Customer reviews

The Zebra runs the web workflow behind its insurance quote comparisons on TinyFish agents, in an always-on production deployment that returns quotes in under two minutes. Daniel Herrington, Chief AI Officer, says comparisons that "once took minutes now happen in seconds," and that the gain compounds at scale.

For Google Hotels in Japan, TinyFish agents work booking flows across more than 40,000 small-hotel sites that standard integrations miss, checking live availability and returning structured pricing into Google Hotel Search. The team credits TinyFish with reaching long-tail supply it couldn't access any other way, adding roughly 3 million annual impressions.

Read more stories from TinyFish customers.

Who TinyFish is best for

• AI engineers and agent builders: Anyone wiring live web access into an agent who wants search, clean fetch, browser control, and authenticated action on one platform rather than four vendors. The case for that consolidation is laid out in why agents need unified web infrastructure.
• Ops and data teams with login-gated workflows: Insurance quoting, hotel and travel inventory, and lead extraction that depend on dynamic or authenticated pages a selector script can't reliably reach.
• Teams augmenting other work with the web: Groups using agents to price products, monitor competitors, or enrich records, not only teams whose whole product is an agent.
• Teams that want a hands-on vendor: Builders who'd rather work with a provider that customizes to their workflow and answers directly, with a dedicated support engineer and custom runbooks, instead of filing tickets into a black-box queue.

Ready to give your agents the live web? Start free with Search and Fetch, no card required, and scale into authenticated agents when you need them. Explore TinyFish →

2. Exa

Exa is an AI-native search API built on a neural, embeddings-based index. It retrieves pages by meaning, which makes it strong for "find similar" discovery and research-style retrieval. Per TinyFish's own read, Exa is the closest competitor on pure retrieval quality. Its surface spans Search, Contents, Answer, Deep Search, an async Agent for deep research and enrichment, Monitors, and the Websets product for building structured entity lists.

Core capabilities

• Neural semantic search: Exa encodes indexed pages as vector embeddings, so a vague natural-language query surfaces conceptually relevant pages that keyword search misses. Exa Instant returns results in under 200 milliseconds for real-time agent use, and the company runs its own index rather than reselling another engine.
• Deep research and Websets: Deep Search and the async Agent run multi-step retrieval and synthesis, returning structured outputs with citations, and Exa reports around 94.9% on the SimpleQA factuality benchmark for its research runs. Websets sits on the same index and verifies each result row against your criteria, which suits B2B lead research and market mapping.

Pricing

Usage-based with a free tier. Content for the first 10 results is bundled into search. Confirm current rates before committing.

Where Exa shines

• Semantic discovery and research: Neural ranking beats keyword matching for "find me pages like this" work, and Deep Search handles multi-hop questions in one call.
• Framework-native integration: First-class LangChain, LlamaIndex, and MCP support means most teams drop it in with little glue code.

Where Exa falls short

• Read-path only: Like Parallel, Exa finds, ranks, and synthesizes. It doesn't drive a browser, authenticate into a portal, or act on a page. TinyFish's own Exa comparison lays out where that line sits.
• Pricing creeps up at volume: Per-query economics that vary by endpoint and result count make spend hard to predict for high-traffic production workloads.

For a closer look at what Parallel costs at different usage levels, see Parallel pricing.

Customer reviews

A go-to-market engineer on G2 praised Exa for ease of use through both the UI and the API, crediting it with prospecting and lead-list enrichment at a lower cost than competing tools.

On the critical side, a developer on Hacker News questioned Exa's pricing trajectory, noting it had crept up toward what Perplexity and Google charge for search-grounded queries.

Who Exa is best for

• AI engineers building research agents, RAG pipelines, and lead-gen features where semantic relevance beats raw keyword recall, especially teams already on LangChain or LlamaIndex.

3. Tavily

Tavily is an AI-native search API built for retrieval-augmented generation (RAG) pipelines and agent workflows, returning clean, LLM-ready results rather than raw HTML. Its endpoints (Search, Extract, Map, Crawl, and a Research endpoint) plus native LangChain integration make it the fastest path from zero to a working agent search loop. Tavily was acquired by AI cloud company Nebius in February 2026.

Core capabilities

• RAG-ready search with framework integrations: Tavily returns pre-cleaned, LLM-ready snippets and ships native integrations with LangChain, LlamaIndex, CrewAI, and AutoGen. Basic search costs one credit, advanced costs two, and fast and ultra-fast depth options serve latency-sensitive jobs like voice assistants.
• Managed research pipeline: The Research endpoint runs a multi-step search-and-synthesize loop that returns a structured report with cited sources, essentially a managed research agent you call in one request.

Pricing

Transparent credit-based pricing with a free tier. Confirm current rates before committing.

Where Tavily shines

• Fastest path to a working RAG loop: If you're already in LangChain or LlamaIndex, Tavily is the path of least resistance, with transparent per-credit pricing that's easy to forecast.
• Latency options for real-time agents: Fast and ultra-fast search depths suit voice and trading agents where a long deep-research run would be unusable.

Where Tavily falls short

• Research costs are less predictable: The Research endpoint can consume anywhere from 4 to 250 credits per request depending on complexity, which erodes the otherwise clean cost story.
• Read-path only: Tavily retrieves, extracts, and crawls public content. It doesn't authenticate into a portal or complete a multi-step action behind a login.

Customer reviews

On Reddit's r/Rag, developers reach for Tavily first when prototyping, with one calling it "easy to implement and super accurate retrieval." The LangChain-native experience and transparent credit pricing are the usual reasons cited.

The recurring criticism in that same thread is retrieval quality on harder queries: one developer flagged that Tavily ignores the time-range filter and returns stale results. A GitHub community discussion echoes the point, noting Tavily "doesn't always guarantee live or high-quality links."

Who Tavily is best for

• RAG and agent developers already building on LangChain or LlamaIndex who want predictable search pricing and a managed research endpoint without standing up their own retrieval stack.

4. Perplexity Sonar

Perplexity Sonar is Perplexity's developer API. Sonar is a search-grounded LLM: you send a question and get back a synthesized answer with citations included as metadata, the synthesis handled for you. It offers reasoning tiers (Sonar, Sonar Pro, and deeper research modes) so you can scale depth per query, plus a raw Search API when you want results rather than an answer.

Core capabilities

• Grounded, cited answers: Sonar runs the retrieval and synthesis together and returns a written answer with source citations attached as metadata, so your agent gets a finished, attributable response without you assembling a retrieval-plus-synthesis stack.
• Reasoning tiers and raw search: Sonar and Sonar Pro trade cost for depth, and deeper research modes handle multi-step questions. The standalone Search API returns ranked results priced per request when you want to run your own synthesis.

Pricing

Per-token for the Sonar models, plus a per-request search fee, or per-request for the raw Search API. Confirm current rates before committing.

Where Perplexity Sonar shines

• Cited answers with no assembly: For teams that want a finished, attributable answer rather than raw results to synthesize, Sonar is the shortest path, with a recognizable brand behind the synthesis quality.
• Depth on demand: Reasoning tiers let you route simple queries cheaply and reserve Sonar Pro or research modes for harder, multi-part questions.

Where Perplexity Sonar falls short

• Less control over retrieval and synthesis: Because Sonar returns a generated answer, you have less control over retrieval and synthesis than with a search-only API you pair with your own model.
• Read-path only: Sonar answers questions about the web. It doesn't drive a browser, authenticate, or complete an action on a page.

Customer reviews

Perplexity's cited-answer format earns broadly positive G2 reviews for answer quality and ease of use, and developers value getting an attributable response without wiring up their own retrieval and synthesis.

For the Sonar API specifically, the sharpest complaint is cost. In a widely read r/perplexity_ai thread, a developer found the API bills retrieved citation documents as input tokens, which pushed simple queries toward $0.10 each and some test runs to about $15. In that same thread, developers also questioned accuracy and output consistency, with one clocking Sonar Pro below 70% on a batch of math questions where rival models scored higher.

Who Perplexity Sonar is best for

• Teams that want fast, cited answers baked into the response without building their own retrieval-and-synthesis stack, especially chat and research features where attribution matters.

5. Brave Search API

Brave Search API runs its own independent index of more than 30 billion pages, refreshed by over 100 million page updates a day, rather than reselling Google or Bing. That independence, plus a privacy-first posture and Zero Data Retention (ZDR) options, gives you an alternative to Parallel's proprietary index without routing through a third-party reseller.

Core capabilities

• Self-owned independent index: Brave crawls the web directly, so you aren't dependent on a reseller that could be throttled. The index is refreshed continuously, and ZDR plus privacy controls suit compliance-sensitive apps.
• LLM Context endpoint: The LLM Context endpoint returns pre-extracted, ranked content chunks in a model-ready format, so your pipeline gets grounding material without extra parsing. Goggles let you re-rank results with custom rules.

Pricing

Metered per request, with a small monthly free credit. Brave moved off its older flat free tier in early 2026, so new accounts bill with a card on file. Confirm current rates before committing.

Where Brave Search API shines

• Independent, privacy-first retrieval: A self-owned, trackless index with ZDR suits compliance-sensitive apps that can't route through a reseller.
• Model-ready grounding at search price: The LLM Context endpoint hands your pipeline clean, ranked chunks ready to drop into a grounding context.

Where Brave Search API falls short

• No traditional free tier: New accounts get a small monthly credit and metered billing with a card on file rather than a flat free allowance.
• Read-path only: Brave returns search results and grounding chunks. It doesn't operate a page or complete an authenticated task.

Customer reviews

On Reddit's r/Rag, developers who found Exa too expensive report switching to Brave, pairing it with their own filtering and calling the results solid at a better cost. The independent index and the newer LLM Context endpoint draw the most praise.

The loudest complaint is the free-tier removal in early 2026. New accounts now get a $5 monthly credit they can cap rather than a flat free plan, and a card is required up front, which several developers pushed back on. Existing free-plan subscribers were grandfathered in on their 2,000 free requests a month, so the change mainly hits new customers.

Who Brave Search API is best for

• Privacy- and compliance-sensitive teams and composability-minded engineers who want an independent, trackless index and prefer to own ranking and synthesis rather than buy a finished answer.

6. You.com

You.com is a composable web search API for AI, spanning web, news, and research with content extraction. Against Parallel, its angle is composability: you assemble the search, extraction, and research pieces you need, and one call can return up to 100 results.

The Search API returns up to 100 results per call at $5 per 1,000 calls. Full page content through livecrawl is a separate add-on at $1 per 1,000 pages on top of that base rate, the same price as the dedicated Contents API that pulls page content for URLs you already have. The Research API runs multi-step research at $12 per 1,000 calls. The free tier covers 100 calls a day, and Zero Data Retention is available for privacy-sensitive teams.

The watch-outs reviewers flag are premium per-query pricing above budget SERP APIs and a thinner public developer community, so you lean on official SDKs rather than grassroots support.

You.com is best for RAG and research-app builders who want composable search with the option to pull full page content and cited research from one provider.

7. Jina AI

Jina AI, acquired by Elastic in October 2025, is a search-foundation platform that covers the read path under one token-based key. Its Reader (r.jina.ai), Search (s.jina.ai), and DeepSearch endpoints handle converting any URL to clean markdown, finding pages, and running iterative deep research, alongside embeddings and rerankers for retrieval.

DeepSearch keeps searching, reading, and reasoning until it answers or hits its token budget, and it returns footnote-style citations, which puts it head to head with Parallel's deep research. Every new key includes 10 million free tokens, and paid usage runs on token top-ups at roughly $0.05 per million ($50 per billion) with no monthly commitment.

The Reader and Jina's models are open source under a permissive license, which answers the self-hosting case Parallel can't. The trade-off is that a complex DeepSearch run can use around 70,000 tokens, so cost climbs without budget controls, and like Parallel it stays on the read path with no browser, authentication, or action.

Best for RAG and deep-research developers who want clean page reading, search, and iterative research from one token-based key, with an open-source option.

8. SerpAPI

SerpAPI returns real, structured search-results data, in the space since 2017. Where Parallel aggregates authoritative sources into curated excerpts, SerpAPI returns structured JSON scraped from Google and 15 or more other engines, parsing organic results, ads, People Also Ask, news, local packs, shopping, and AI Overview content with citations. It manages proxies, CAPTCHA solving, and parsing maintenance, and it takes on the legal exposure of scraping.

Pricing is subscription by search volume: a free tier of about 100 searches a month, then Starter at $25 for 1,000, Developer at $75 for 5,000, Production at $150 for 15,000, and Big Data at $275 for 30,000. The trade-off against Parallel is that SerpAPI returns SERP metadata, titles, URLs, and snippets, so an agent that needs the actual page text still adds a fetch step, and per-search costs run several times what Google-only APIs charge.

Best for agents and SEO tools that need real, structured multi-engine SERP data with the deepest element coverage.

What's next?

The best Parallel.ai alternative comes down to what your agent does with the web. For high-accuracy deep research over a proprietary index, Parallel is hard to beat.

Choose TinyFish when your AI agent needs to:

• Maintain persistent login state between requests
• Pull live, real-time data on every run
• Reach authenticated endpoints behind forms and anti-bot walls

A single TinyFish API key gives you Search, Fetch, Browser, and Agent, with Vault and Profiles supporting authenticated workflows. Search and Fetch are free, with no credits used. See how teams like Google Hotels reach supply behind fragmented sites, then point an agent at your own target site and test the token savings before you scale into authenticated workflows.

Start free on TinyFish →

FAQs

1. What is the best Parallel.ai alternative in 2026?

TinyFish is the best Parallel.ai alternative for builders who need the full web workflow on one platform. Search and Fetch handle the reading. The Web Agent handles authenticated, multi-step action behind a login. For semantic search and RAG, Exa and Tavily fit. For cited answers, Perplexity Sonar. For an independent index, Brave; for composable AI search, You.com. For deep research from one key, Jina AI. For raw SERP data, SerpAPI

2. Which Parallel.ai alternative is best for RAG pipelines?

Tavily, for LangChain-native retrieval, or Exa for semantic relevance. Jina AI fits when you also need clean page reading and deep research. TinyFish fits when your RAG context sits behind a login.

3. Can Parallel.ai alternatives be self-hosted?

Partly. Jina's Reader and models are open source, and TinyFish offers an on-prem Enterprise option. Exa, Tavily, Perplexity Sonar, Brave, and You.com are cloud-only, like Parallel.

4. What is the main difference between Parallel and its competitors?

Parallel aggregates authoritative sources into cited research on the read path. TinyFish adds authenticated, multi-step action. The other search APIs differ mainly in index, synthesis style, and pricing.

5. Which Parallel.ai alternative is best for reducing LLM token costs?

TinyFish, because its fetch strips page chrome and returns only the meaningful content, roughly 1,170 tokens versus 41,000 on the same page. Parallel and Sonar return compact excerpts too, while Jina's DeepSearch runs token-heavy.