Best CAPTCHA Solver Services: Price and Solve Rate

If you want the decision without the reasoning, take this table.

Two things follow from that table, and most buying guides skip both. If your trigger rate is low, the price gap between the most and least expensive service here is worth pennies per 1,000 pages, so pick on coverage and reliability instead. If your trigger rate is high, no solver is the right purchase, because you are paying per unit for a problem you can remove upstream.

A CAPTCHA solving service is an outsourced worker pool behind an HTTP API. You submit a task describing the challenge, usually a site key plus the page URL for token challenges, or a base64 image for classic ones. The service returns a solution token. You inject that token into the form or the JavaScript callback on the target page, and the target decides whether to accept it.

That last clause is the one that costs people money. You are not buying a bypass, you are buying a token, and the target still gets a vote. A service can report a 99 percent internal solve rate while your end-to-end success rate sits far lower, because tokens submitted from a flagged IP get rejected anyway.

Two worker models exist:

• Human-backed. Real people look at the challenge in a queue. Slower and more expensive per solve, but far more tolerant of challenge designs nobody has seen before.
• Automated. Computer vision and token generation, no human in the loop. Faster and cheaper, but only on the challenge types the vendor has built for.

Most services now run both and route internally. What differs is which types they cover, what they charge, and how quickly they adapt when a challenge vendor ships a change.

Open the target in a browser, trigger the block, and look at what actually appears. The answer determines whether a solver is the right purchase at all.

The bottom three rows are where budgets go to die. Teams see "blocked" in their logs, buy a solver, and the block rate does not move, because those systems evaluate IP reputation, TLS fingerprint and request timing before any challenge renders. The fix for that is in how to bypass Cloudflare for web scraping and how to detect when your scraper is blocked, not on a solver's pricing page.

These are the services most production scrapers end up on, plus the bundled alternative that replaces the category.

2Captcha. The default. Widest challenge coverage of any single account, human workers behind the harder tasks, and an API dialect (in.php to submit, res.php to poll) that most competitors clone deliberately. That cloning is the real feature: write against the 2Captcha dialect and you can switch vendors by changing a base URL. Pricing on premium token types sits at the top of the range.

Anti-Captcha. The other original. A task-based JSON API (createTask and getTaskResult) that is the second de-facto standard dialect. Coverage is close to 2Captcha's, with per-type pricing that generally undercuts it on reCAPTCHA. A solid second provider in a failover pair, precisely because the dialect differs.

CapSolver. Automated-first, aimed at high-volume reCAPTCHA, hCaptcha and Turnstile traffic. Publishes rates well under a dollar per 1,000 on those types and supports both major API dialects, which is unusual and useful. Weaker fit if your workload includes odd one-off image challenges.

CapMonster Cloud. From ZennoLab, also automated-first, and the direct price rival to CapSolver on the same token types. Notable for a separate self-hosted desktop product sold on a licence rather than per solve, which changes the economics if your volume is steady enough to keep hardware busy.

Death By Captcha. One of the oldest human-backed services, still operating, with an API that predates the current conventions. Buy it for tolerance of unusual image work, and because it is a genuinely independent third option when your first two providers are both having a bad day.

Bundled unblocker and scraping APIs. Not a solver, a replacement for owning one. You send a URL, the vendor handles proxies, browser, fingerprint and any challenge, and bills per request or per credit. It costs more per successful page than a solver plus your own proxies, and removes an entire subsystem from your stack. The trade-off is laid out in web scraping API vs self-managed proxies.

Every figure below is the vendor's own published list rate as of September 2026, quoted as an approximate band because these services price per challenge type and revise rates often. Check each vendor's current pricing page before you budget. None of these numbers were measured or benchmarked by SparkProxy.

Premium types cost more everywhere. FunCaptcha, GeeTest v4 and enterprise hCaptcha are billed above the reCAPTCHA v2 line at every vendor, and some quote them only on request. If your target serves one of those, price the whole shortlist on that type alone, because it will dominate the bill. Our guides on bypassing Arkose Labs FunCaptcha and bypassing GeeTest cover what those challenges check.

Per-solve pricing is the wrong unit. You do not buy solves, you buy pages, and the conversion factor is your trigger rate: the share of requests that hit a challenge at all.

Effective cost per 1,000 pages = price per 1,000 solves x trigger rate.

Read the first column carefully. At a 2 percent trigger rate, moving from the most expensive service on the shortlist to the cheapest saves under five cents per 1,000 pages. Across a million pages a month that is about $48. Not nothing, but nowhere near enough to justify a migration, narrower challenge coverage, or a vendor with a thinner track record. At low trigger rates, buy on coverage and uptime and stop optimising the rate card.

Now read the last column. At a 40 percent trigger rate you are paying $0.24 to $1.20 per 1,000 pages in solver fees alone, plus the latency cost of all 400 challenges, plus a success rate that is now hostage to a third party. That workload does not need a cheaper solver. It needs the trigger rate brought down, which is a proxy and fingerprint problem.

Here is the same arithmetic as a script you can point at your own logs:

def solver_cost_per_1k_pages(price_per_1k_solves: float, trigger_rate: float) -> float:
    """trigger_rate = challenges_seen / requests_sent, taken from your own logs."""
    return price_per_1k_solves * trigger_rate

for trigger in (0.02, 0.10, 0.40):
    for price in (3.00, 1.50, 0.60):
        cost = solver_cost_per_1k_pages(price, trigger)
        print(f"trigger {trigger:>5.0%}  rate ${price:.2f}  ->  ${cost:.3f} per 1k pages")

One more term belongs in that model. A failed solve is still billed on some plans, and at minimum it costs the retry, so budget on solves attempted, not tokens accepted. At an 80 percent acceptance rate, your real cost is the table value divided by 0.8.

Every vendor advertises an accuracy figure, and none of them measures the thing you care about. Their number is internal: of the tasks their workers completed, how many were judged correct. Yours should be end to end.

Accepted token rate = tokens the target accepted / challenges you submitted.

The gap between those two comes from things the solver does not control. A reCAPTCHA v3 token comes back with a score attached, and a low score fails at the target even though the solve technically succeeded. A token generated for one IP and submitted from another can be rejected outright. Session cookies, user agent and TLS fingerprint all have to stay consistent between the page that issued the challenge and the request that submits the token.

Latency is the second unpublished number, and it sets your infrastructure cost. Human-backed solves on hard types are commonly quoted in the tens of seconds, automated solves on common token types in single-digit seconds. Whatever the real figure is for your target, size your worker pool from it:

Concurrent workers needed = challenges per second x average solve seconds.

At 5 challenges per second and a 15 second average, you need roughly 75 in-flight tasks to avoid a queue, and your solver plan has to permit that concurrency. Plans with low concurrency caps quietly turn into throughput ceilings, the same failure mode as an undersized thread count on a proxy plan.

Three more things to check before you commit:

• Failure billing. Does a task that returns ERROR_CAPTCHA_UNSOLVABLE cost you anything?
• Refund and expiry policy. Prepaid balances at several vendors expire. Ask in writing before topping up a large amount.
• Type deprecation. When a challenge vendor ships a change, how long has this service historically taken to restore support? Their status page and changelog answer that better than their sales page.

Two API dialects dominate: the 2Captcha in.php and res.php submit-and-poll pattern, and the Anti-Captcha createTask and getTaskResult JSON pattern. Most newer services implement one or both on purpose, so switching is cheap if, and only if, you wrote an adapter instead of scattering vendor calls through your codebase.

import time, requests

class SolverClient:
    """2Captcha-dialect client. Point base_url at any compatible vendor."""

    def __init__(self, api_key: str, base_url: str = "https://2captcha.com"):
        self.key, self.base = api_key, base_url.rstrip("/")

    def solve_recaptcha_v2(self, site_key: str, page_url: str, timeout: int = 180) -> str:
        r = requests.get(f"{self.base}/in.php", params={
            "key": self.key, "method": "userrecaptcha",
            "googlekey": site_key, "pageurl": page_url, "json": 1,
        }, timeout=30).json()
        if r.get("status") != 1:
            raise RuntimeError(f"submit failed: {r.get('request')}")
        task_id, deadline = r["request"], time.time() + timeout
        while time.time() < deadline:
            time.sleep(5)
            res = requests.get(f"{self.base}/res.php", params={
                "key": self.key, "action": "get", "id": task_id, "json": 1,
            }, timeout=30).json()
            if res.get("status") == 1:
                return res["request"]
            if res.get("request") != "CAPCHA_NOT_READY":
                raise RuntimeError(f"solve failed: {res.get('request')}")
        raise TimeoutError("solver did not return in time")

Keep two providers configured and route by challenge type: the cheap automated service for your bulk token type, the human-backed one for everything unusual. Failover is not paranoia here. These services get rate limited, run out of workers at peak, and occasionally suspend accounts with little warning. A single-vendor scraper stops dead when that happens.

The trigger rate that drives the whole cost model is mostly a function of what your requests look like before any challenge appears. Datacenter ranges with a bad history, a TLS fingerprint that does not match the user agent you claim, and a request cadence no human would produce all push it up. Fix those and the solver becomes a small line item.

Practical order of work, cheapest first:

1. Move off free or heavily shared IPs onto a pool where you control concurrency. Details in how to avoid getting your proxy blocked.
2. Match your TLS and HTTP/2 fingerprint to the browser you claim to be. See how to bypass IP fingerprinting with clean datacenter subnets.
3. Keep sessions coherent. One IP per session for multi-step flows, so cookies and tokens line up.
4. Only then buy solves for whatever still gets challenged.

SparkProxy sells the layer underneath the solver rather than the solver itself. The datacenter network runs 1M+ IPs across 80+ countries, including 50,000+ US addresses, behind one gateway at gateway.sparkproxy.io: port 11000 for rotating HTTP and HTTPS, 11002 for sticky sessions, 13000 for SOCKS5. Every plan includes unlimited bandwidth and 30 days of validity, which matters here because retrying a challenged page costs you nothing in traffic.

Those speed figures are ceilings under the Fair Usage Policy, not guaranteed throughput. If you would rather not assemble proxies, fingerprints and solving yourself, the SparkProxy Scraping API bundles them per request: a plain fetch costs 1 credit, JS render 5, screenshot or PDF 10, and there are 1,000 free credits with no card to test on.

import requests

API = "https://scrape.sparkproxy.io/api/v1"
HEADERS = {"X-API-Key": "YOUR_API_KEY"}

resp = requests.get(API, headers=HEADERS, params={
    "url": "https://example.com/product/123",
    "render_js": "true",
    "premium_proxy": "true",
    "country_code": "us",
}, timeout=90)
print(resp.status_code, len(resp.text))

For the tactics that lower trigger rate without spending anything, start with how to avoid CAPTCHAs when web scraping.

Solving a challenge is rarely a criminal act by itself, but it is almost always a breach of the target's terms of service, and the surrounding conduct is what creates real exposure. Circumventing protection on login pages, payment flows, account creation, ticketing, or anything holding personal data moves the question into computer-misuse and fraud territory in most jurisdictions, and no vendor's marketing page is a defence.

Before procurement signs anything, get answers on paper:

• What is being collected, and is it public, unauthenticated data?
• Does the workflow create accounts, submit forms, or transact? If yes, escalate to counsel before writing code.
• Where are the solving workers located, and does that raise a data transfer issue for the images you send them?
• What does the solver vendor log, and for how long? You may be shipping fragments of target pages to a third party.

Answering those in a security review is the difference between a tool your company keeps and one it removes after the first audit.

Run this before you commit to a plan. It costs a few dollars in credits and settles the question with your own data.

1. Measure your trigger rate. Push 1,000 real requests through your existing proxies, count how many return a challenge, and record the type on every hit.
2. Convert it to a budget. Multiply each vendor's published rate for that type by your trigger rate. If two land within a few cents per 1,000 pages, price is not the deciding factor.
3. Buy the smallest top-up at two vendors. One automated, one human-backed, both behind the same adapter.
4. Submit 200 real challenges to each. Real site key, real page URL, from the same IP that will submit the token.
5. Score end to end. Count tokens the target accepted, not solves the vendor reported. Record the 95th percentile time to accepted token, not just the average.
6. Test a failure. Kill the primary vendor's credentials mid-run and confirm your code fails over instead of stalling.
7. Re-run at your real concurrency. Solve times stretch under load, and cheap plans' concurrency caps are where that shows up.

Whatever wins that test is your answer, and it will be specific to your target: a service that is excellent on hCaptcha at one site can be mediocre on the same type at another, because the target's own scoring differs. That is also why published accuracy claims are close to useless as a purchasing input. Related reading on the challenge types: bypassing reCAPTCHA, bypassing hCaptcha and bypassing Cloudflare Turnstile.