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The best scraping API for an AI agent is the one that retrieves the evidence your workload needs and lets the agent finish its task within your quality, latency, and cost limits. A high request-success rate is useful, but it does not show whether the agent can extract the right facts, cite them, and produce a correct final answer.
Benchmark both layers: first, whether the API returns usable page content; then, whether the complete agent workflow solves representative tasks. The steps below give you a repeatable way to compare providers without mistaking a published leaderboard for a universal ranking.
What a scraping API benchmark can—and cannot—tell you
A benchmark score is conditional on its target URLs, success rule, provider configurations, timeout, and run date. Change any of those and the result may change. A score is evidence about the test that was run, not a guarantee for every domain or agent workload.
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For example, the Web Data Frontier README describes a 100-target suite that counts a pass when a 2xx response contains expected text. That is a concrete retrieval check, but it does not establish that an agent found every required field, interpreted the page correctly, or supplied valid evidence. Treat request-level results as one part of an evaluation.
#1 Best Overall
Before comparing published results, look for the details that make them interpretable:
- The target URLs, page categories, and date of the run.
- The exact pass rule, including how partial, stale, malformed, and blocked content is handled.
- Rendering, proxy, geography, session, retry, and timeout settings.
- Per-attempt outcomes, latency treatment, and raw results where available.
- Who conducted or published the evaluation and any commercial relationship relevant to interpreting it.
Define the agent task before choosing metrics
Start from the user-visible result, not the API feature list. Specify what information the agent must obtain, how fresh it must be, what format it must return, and what evidence is required. Define whether partial completion counts and how you will check correctness.
A useful task specification is concrete enough to evaluate deterministically. For example:
Find 3 round-trip flight options for 1 adult from New York City to San Francisco, departing 2026-06-16 and returning 2026-06-19. Report a total round-trip price in USD for each of the 3 options and at least one source URL per option.
For that task, an evaluator could check that there are three distinct options, each has a round-trip total in USD, and each includes a source URL. It should also verify the values against the retrieved evidence and record partial results separately from a fully correct answer. A page fetch that succeeds but omits a fare or source URL is not a completed task.
Build a representative, fixed URL set
Use URLs from the domains and page types your agent will actually encounter. Split the set into meaningful categories so a good result on easy pages cannot conceal failure on important ones.
- Static HTML pages.
- JavaScript-rendered pages and single-page applications.
- Pages with access protections relevant to your workload.
- Commerce pages, product catalogs, or other vertical-specific sources.
- News and media pages, if the agent must use them.
Keep one fixed set for final comparisons and a separate development set for configuration changes. Record geography, authentication state, session requirements, and time-sensitive conditions for every target. A broad public test suite can help with initial screening, but it cannot replace a URL set shaped around your own use case.
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Category results matter. In a June 2026 benchmark published by Anakin and authored by Tosh Kothari of Anakin Team, six APIs were tested against 24 URLs for 144 requests. The report’s categories included Cloudflare-protected pages, Akamai-protected retail, JavaScript-heavy SPAs, e-commerce, news and media, and static HTML. It reported overall success rates from 42% to 75%; the highest overall result was 18 of 24 URLs. Results varied by category: several providers passed all five JavaScript-heavy SPA URLs, while no provider passed more than one of the three Akamai-category URLs. Those figures describe that specific run, not the market as a whole.
Make provider configurations comparable
Decide what question the comparison should answer. If you want to know what each provider can deliver in production, test its strongest supported production configuration. If you want to isolate a particular capability, constrain providers to a common configuration. State which approach you used; the results answer different questions.
For every provider and run, record:
- JavaScript rendering and extraction mode.
- Proxy, access-bypass, and geographic settings.
- Session or authentication state.
- Output format and any schema constraints.
- Timeout and retry policy.
Do not treat JavaScript rendering as synonymous with anti-bot bypass. The Web Data Frontier README describes those as separate configuration axes and notes that adapters can differ in how settings are applied. Document provider-specific differences rather than presenting configurations as identical when they are not.
Rank #3
Define retrieval success beyond an HTTP status
An HTTP 200 only says that a request received a successful status code; it does not prove that the response contains usable evidence. Use a retrieval pass rule that checks for expected text or fields, confirms required values are non-empty, and applies basic validity checks. For structured responses, validate the schema as well as the content.
Keep failures distinct instead of folding them into a single undifferentiated miss:
- Blocked or denied response.
- Timeout.
- Malformed response or invalid schema.
- Expected content missing or incomplete.
- Stale content when freshness is required.
- Successful, validated retrieval.
The Web Data Frontier rule—2xx plus expected text in the body—is a reproducible API-level check. For an agent workload, add checks for field correctness, completeness, and source traceability. Report partial retrieval separately from a full pass.
Measure the agent’s completed task separately
Evaluate the whole workflow after measuring retrieval. An API can return a page while the agent misses required facts, extracts an incorrect value, or fails to provide source evidence. Agent-level scoring should reflect the user’s requested outcome, not just the transport layer.
For each task, record whether the final answer is correct and complete, whether required source URLs are present and valid, and whether the agent met freshness and formatting requirements. Also capture operational measures such as tool calls or turns, token use, wall-clock time, and total cost when these affect deployment.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A 2026 experiment from agent-data.dev illustrates why these layers should not be conflated. In five runs of one flight-search task, a structured API completed 5/5 tasks, while web search plus extraction and browser automation each completed 0/5. The experiment reported median per-run figures, and its browser runs hit an 80-turn limit. This is a narrow comparison of tool types and implementations on one task, not a general ranking of scraping APIs.
OpenAI’s BrowseComp benchmark provides broader background on agent-level web research: it comprises 1,266 challenging problems designed to measure an agent’s ability to find difficult, entangled information online. It evaluates browsing-agent capability, not scraping API performance, so it should not be used as a substitute for a provider benchmark.
Report latency and cost in workload terms
Save per-attempt wall time. At minimum, report median and a tail percentile such as p95 for successful calls, plus the timeout rate. Show successful-call latency separately from all-attempt latency and explain how misses enter each calculation. Without that disclosure, a provider with many slow or failed calls can appear faster than it is.
String’s Web Data Frontier page describes how its methodology assigns latency to content responses and complete misses. That kind of explicit rule is important when reading a reported latency figure or designing your own.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchCompare cost per successful validated response and, for end-to-end tests, cost per completed task. Include retries, rendering or proxy surcharges, and downstream agent or model usage if they are part of the actual deployment. The cheapest basic request may not be the least expensive way to produce a correct, evidenced answer.
Repeat runs and preserve the evidence
A single run can reflect temporary site or provider conditions. Repeat requests at several times or on multiple days when appropriate, randomize provider order, and compare providers on the same URLs. Keep timestamps, request IDs, response artifacts, and errors so a result can be investigated or reproduced.
If you refresh the suite, preserve the prior raw results and label each run by date. Published results demonstrate why dates and protocols belong beside every score: String’s page reports a Web Data Frontier run based on data generated September 16, 2026, while Anakin’s comparison used June 2026 data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use published comparisons as screening evidence
Published benchmarks can help identify questions to test, but their scores are not interchangeable when targets, configurations, and pass rules differ.
| Published result | What it reports | How to interpret it |
|---|---|---|
| Anakin, June 2026 | Six APIs, 24 URLs, 144 requests; overall reported success from 42% to 75%, with 18/24 the highest overall result in that test. | A category-rich, vendor-published run. Use its variation across page types to inform your own test set; do not treat its top figure as a universal winner. |
| String Web Data Frontier page, September 16, 2026 | String reports 97.0% success in the open-source benchmark run, using 100 hard targets and 500 requests per provider. | The page is published by a participating provider, so this is not an independent third-party ranking. |
| NativePort dataset snapshot, August 5, 2026 | 22 commercial web-access APIs, 67 provider-capability scorecards across 13 capabilities, and 297 metric rows. | NativePort identifies the dataset as a first-party benchmark, not an independent third-party evaluation, and says it operates a commercial gateway routing to many providers it scores. |
Commercial involvement does not automatically make a benchmark useless; it does affect how you should read its claims. Look for methods, target-level results, and ownership disclosure, then validate promising options on your own workload.
Compare options on the dimensions that affect your deployment
| Dimension | What to measure | Why it matters |
|---|---|---|
| Target coverage | Success by domain, page type, and protection class. | An overall average can hide failures on workload-critical sources. |
| Content quality | Required text and fields, schema validity, freshness, and source traceability. | A successful response code does not prove the returned data is usable. |
| Agent task success | Correct final answers, required fields and evidence, and completion rate. | This measures the outcome the user actually needs. |
| Latency | Successful-call distribution, tail latency, timeout rate, and miss treatment. | Slow calls and retries affect agent responsiveness. |
| Total cost | Cost per validated page and completed task, including retries and advanced modes. | Nominal request cost may not reflect the cost of a usable result. |
| Operational fit | API and SDK behavior, output options, session and geography support, and diagnostics. | Integration and failure handling influence production effort. |
| Evidence quality | Raw data, fixed corpus, repeated runs, and ownership disclosure. | Transparent evidence is easier to reproduce and qualify. |
A practical benchmark runbook
- Write the task contract. Specify required facts, formats, freshness, evidence, and what constitutes full versus partial completion.
- Assemble the URL corpus. Stratify by the page classes and domains that occur in your workload; separate tuning URLs from held-out comparison URLs.
- Freeze test settings. Document rendering, proxies, geography, sessions, extraction, output, timeout, and retries for each tested configuration.
- Run paired, repeated requests. Use the same URLs for each option, randomize order, and retain artifacts, timestamps, request IDs, and errors.
- Score retrieval outcomes. Validate content and schema; label blocks, timeouts, malformed, missing, stale, partial, and full-success responses separately.
- Run the agent tasks. Evaluate correctness, completeness, evidence, freshness, latency, turns or tool calls, tokens, and total spend.
- Publish the method with the result. Include target count and categories, date, pass criteria, configuration, latency and miss treatment, and the raw results or a reproducible summary.
- Choose with workload-specific trade-offs. Compare the options against your quality, tail-latency, and cost limits rather than selecting by one aggregate success score.
What a defensible result looks like
A useful report lets another engineer understand what was tested and why a result matters: fixed target URLs, clear page categories, explicit configurations and pass criteria, repeated outcomes, latency distributions, and cost measured at the level of validated content and completed tasks. It distinguishes API retrieval from agent success and discloses who produced the comparison.
Published leaderboards can narrow the field. Only a small test using your target URLs and your actual agent tasks can show whether a provider meets your requirements.
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