Multilogin vs GoLogin: Antidetect Browser Comparison

An antidetect browser gives every profile its own device identity: canvas and WebGL hashes, AudioContext output, font list, screen metrics, timezone, locale, plus a matching User-Agent and client hints set. Each profile keeps its own cookie jar, storage and cache. Two profiles on one laptop should read as two computers to a tracking script, not one computer in two windows.

Multilogin and GoLogin both do this. Multilogin is the older of the two and the reference implementation competitors get benchmarked against. GoLogin arrived later as the accessible option, with broader platform coverage including a web version and an Android app.

Neither changes the network layer. The fingerprint says "Windows 11 desktop in Berlin" and the IP says whatever your proxy says. When those disagree, the profile is more suspicious than a plain Chrome window would have been. That mismatch is the most common self-inflicted wound in this category, and it is a proxy configuration problem, not a browser problem. Our primer on browser fingerprinting covers the signals both tools try to control.

One compliance note, stated once: many platforms prohibit multi-accounting in their terms of service, and some jurisdictions regulate automated account creation. Read the terms for the platform you operate on.

Multilogin fits when the cost of a single failure is high and you need process controls: a four-tier role hierarchy, a support relationship, a published core-update policy, the maturity of a long-lived codebase. Agencies handling client ad accounts and regulated teams land here.

GoLogin fits when you need many profiles across many machines at a lower entry cost, and when opening a profile from a browser or a phone matters more than deep governance. Small teams, freelancers and distributed contractors land here.

Choosing on fingerprint quality alone optimizes the wrong variable. Academic testing of ten antidetect browsers found nine detectable in production (Browser Polygraph, ACM IMC 2024). What changes your outcome is operational: whether profiles pair consistently with the right network path, how the tool fits your automation stack, and how fast the engine tracks upstream releases.

This table describes product shape, not prices. Plans, profile limits and feature tiers change often here, so confirm current details on the Multilogin pricing page and the GoLogin pricing page before committing budget.

Both vendors have repriced significantly since 2024, so any article quoting hard figures is stale within a quarter. Pull the numbers yourself on the day you buy.

This used to be the biggest architectural difference between the two products. As of August 2026 it is mostly gone, and almost every competing article still reports it as live.

What changed, with the citation

Multilogin's release notes entry dated 27 January 2026 states that Stealthfox, its Firefox-derived core, is now legacy, that no new cores or fixes will be released, and that customers should export their data and switch to Mimic (Multilogin release notes). Its own core-update article confirms the freeze: Stealthfox no longer reports a latest version because it is no longer updated, with the final core pinned at 146 (core updates). The same release notes list Mimic core 151 shipping on 11 August 2026. Both vendors therefore ship exactly one actively maintained Chromium-derived lineage today: Mimic on one side, Orbita on the other.

If you were about to pick Multilogin because it could present Firefox-shaped traffic, re-read that page before you pay. Existing Stealthfox profiles still open, but a browser frozen at core 146 while Firefox stable keeps moving is a widening version gap, which is the exact detection surface described below.

Why the second lineage was expensive

Every engine has to be rebased on upstream security and feature releases. Chrome ships stable roughly every four weeks and Firefox runs a similar cadence, so a second lineage doubles that work permanently for a fraction of the user base. A vendor that priced that trade-off and dropped the Firefox core is a stronger data point than any argument we could make.

Patch lag, and which vendor gives you a number

Patch lag is itself a detection surface. A browser reporting a version whose real feature set no longer matches gets caught by feature probing, however good its canvas spoofing is. Multilogin publishes its policy: cores roll out one to two weeks after the official upstream release, deliberately, so that frameworks like Puppeteer and Selenium can catch up (core updates). GoLogin's Orbita documentation says the browser tracks Chrome's version releases and that updates ship regularly, but we could not find a published interval on its docs or release notes during this pass, so treat that lag as unknown rather than assuming it is shorter or longer.

That reverses the usual framing. Multilogin's advantage on engines is no longer breadth. It is a stated number you can plan around.

The check that settles it

Open a profile, visit a version-reporting page, and compare the reported major version against current Chrome or Firefox stable on the day you test. Then feature-probe: if the profile claims version N, test an API that landed in N and one that landed in N+1. A profile claiming a version but failing its own feature set is precisely the inconsistency anti-bot vendors hunt for.

Generated fingerprints are assembled from rules: pick an OS, pick a GPU string that plausibly matches, pick a screen size from a distribution, derive the font list. Cheap and effectively infinite. The weakness is statistical, because rule-based generators produce combinations at frequencies no real population produces, and a model trained on millions of real sessions learns that distribution.

Harvested fingerprints are captured from real devices and replayed, so they are internally consistent by construction. The weakness is supply and staleness: the pool is finite, and a fingerprint from a device configuration that has aged out of the population becomes its own anomaly.

Neither vendor publishes a full methodology, so be skeptical of marketing language in either direction and test the output instead. Step 2 of the test plan below shows how: create ten profiles per tool targeting the same OS, dump the values from a public fingerprint reporting page, and read the variance.

The metric people get wrong is uniqueness. Being unique is fine, since most real browsers are close to unique. Being implausible is the problem. A fingerprint no real device would produce hurts far more than a common one, and a fingerprint that shifts between sessions for a profile representing one person's laptop is worse still. Stability across sessions is a feature.

Both tools sync profiles through the vendor's cloud, so a profile opened in Lisbon carries the cookies it had when it closed in Warsaw. That convenience raises two questions.

Who can open which profile. Irrelevant in a team of one. In an agency with contractors it is the whole ballgame, because a profile is a live logged-in session to a client's ad account. Both vendors publish a role matrix, which is worth saying plainly because most comparisons imply only one of them does. Multilogin documents four roles, owner, manager, operator and starter, scoped to folders and profiles (roles and permissions). GoLogin documents four access levels, can run, can edit, folder full access and workspace full access, and notes that granular control comes from folder-level assignment rather than a workspace-wide role (team members).

So the differentiator is not whether roles exist. It is whether they are enforced. Test that: create a limited-role user, then try to export a profile, read a saved cookie, or move a profile between workspaces from that account. The gap between a permissions matrix on a docs page and what the product enforces is where audit findings come from.

What happens on exit. Portability decides your switching cost. Establish before buying what you can export: cookies as JSON, local storage, the fingerprint definition, proxy assignments. Cookies are usually exportable from both. Fingerprint definitions frequently are not, in either tool, because they are the vendor's product. Multilogin's Stealthfox deprecation notice, which tells users to export and switch cores, is a preview of what migration feels like when the vendor sets the deadline. Plan for the realistic outcome, which is that migrating means recreating profiles and re-importing cookies. Keep your own map of profile to account, proxy and locale outside the vendor, because it is the only artifact that lets you rebuild.

Both products expose the same pattern. A local agent runs on the machine, you call it over HTTP to start a profile, and it returns a debugging endpoint you attach a standard automation library to. After that you write ordinary Playwright, Puppeteer or Selenium. Multilogin publishes worked examples for all three (Puppeteer, Selenium and Playwright). GoLogin publishes the same plus a REST API for profile, workspace and proxy management and maintained Node and Python SDKs, which matters if you provision profiles from CI.

Ports, routes and payloads change between releases, so take them from current vendor documentation rather than any blog post, this one included. What to evaluate is the stability record, and here is a finding neither vendor advertises. Reading both public changelogs on 18 August 2026, Multilogin's release notes and GoLogin's release notes log features and fixes, but neither documents breaking API changes against a version number. Assume you are responsible for detecting contract drift yourself. Wrap the vendor call in one adapter so a change is a ten-line fix:

import requests
from playwright.sync_api import sync_playwright

# One adapter per vendor. Take the route and payload from your vendor's
# CURRENT local-API docs, not from this snippet.
LOCAL_AGENT = "http://127.0.0.1:PORT"

def open_profile(profile_id: str) -> str:
    r = requests.post(f"{LOCAL_AGENT}/profile/start",
                      json={"profile_id": profile_id}, timeout=60)
    r.raise_for_status()
    return r.json()["ws_endpoint"]   # field name varies by vendor and version

def run_check(profile_id: str, url: str) -> str:
    with sync_playwright() as p:
        browser = p.chromium.connect_over_cdp(open_profile(profile_id))
        page = browser.contexts[0].pages[0]
        page.goto(url, wait_until="networkidle")
        return page.title()

The pattern is identical for both, which is the finding: automation compatibility does not separate these two, because both land you in the same Playwright or Selenium code. Pin your client version and run a contract test after every vendor auto-update.

Both vendors sell proxy bandwidth alongside the browser, and both accept any third-party HTTP or SOCKS5 proxy. Bundled is convenient for a first profile and awkward at scale: you cannot audit the pool, metered bandwidth makes cost per profile unpredictable, and you lose direct control of the profile-to-exit pairing that decides whether the profile holds up.

The pairing rules are simple and almost universally violated:

• One profile, one stable exit. A profile representing a person's laptop should not appear in Frankfurt on Monday and Sao Paulo on Tuesday. Use a sticky session or a static IP per profile, as covered in our explainer on sticky session proxies.
• Timezone and locale follow the IP. A German exit means Europe/Berlin and an Accept-Language plausible for that region. Both tools let you set this. Both let you forget to.
• The proxy type should match the story. A profile presenting as a home user on a datacenter ASN is a contradiction the first-pass ASN lookup catches before any fingerprint script runs. That is what residential proxies exist for.
• Close WebRTC leakage. A WebRTC candidate exposing the real local or public address undoes everything above. Both tools have a setting. Verify it rather than trusting it, and see WebRTC leaks for what to look for.

Verify the exit before the profile does anything meaningful. Open a plain IP-reporting endpoint as the profile's first navigation, log the result next to the profile ID, and alert when the country changes unexpectedly. That catches a rotating-proxy misconfiguration on day one instead of after an account review.

We are deliberately not quoting figures. Both vendors have restructured pricing more than once recently, so any number printed here misleads within a quarter. Check the Multilogin and GoLogin pages for current plans. What is stable enough to compare is the shape of the models.

Both sell tiers defined mainly by profile count, with team seats and add-ons on top. Broad positioning as of August 2026 is that Multilogin sits higher and sells on reliability, controls and support, while GoLogin sits lower and sells on profiles per dollar. That gap has narrowed since 2024, so re-check rather than assume. Build the total for your actual workload instead of reading the headline tier:

Run that for both vendors with your own numbers. The winner is often not the one with the cheaper headline tier.

Both vendors offer trials. Use them for a controlled comparison, running the same script against each.

1. Build a neutral reference capture. Record what your target serves an ordinary automated client first, so you can tell whether a difference came from the antidetect browser or from the site changing. SparkProxy's Scraping API gives you that control group:

import requests

API = "https://scrape.sparkproxy.io/api/v1"
KEY = "YOUR_API_KEY"   # from app.sparkproxy.io

r = requests.get(API,
    headers={"X-API-Key": KEY},
    params={
        "url": "https://example.com/pricing",
        "render_js": "true",
        "country_code": "DE",     # match the exit geo you test in the profile
        "json_response": "true",
    }, timeout=120)
data = r.json()
print(data["status_code"], data["duration_ms"], data["credits_used"])

Swap in "format": "screenshot" with window_width and window_height for a visual diff instead of HTML. Divergence from what the profile sees usually means geo or consent-state differences rather than detection. If the work never needs a logged-in account, note that render_js=false plus batch URLs is faster and cheaper than any browser profile, and you may not need either product.

2. Dump fingerprint values from each profile and diff them. Open the same public fingerprint reporting page in ten profiles per vendor and compare distributions:

import json, collections

for vendor in ("multilogin", "gologin"):
    profiles = json.load(open(f"{vendor}-profiles.json"))
    for field in ("webgl_renderer", "screen", "timezone", "fonts_hash"):
        c = collections.Counter(p[field] for p in profiles)
        print(f"{vendor:10} {field:16} distinct={len(c):2} top={c.most_common(2)}")

Three distinct WebGL renderers across ten profiles is a thin pool. Ten distinct values that all pair a mobile GPU string with a 2560x1440 desktop resolution is a naive generator.

3. Check header and TLS consistency. Fingerprint spoofing happens in JavaScript; the TLS handshake happens below it and cannot be patched from page context. A profile claiming Firefox while presenting a Chromium TLS signature is detectable before a single script runs, and with both vendors now on Chromium-derived cores that mismatch is the likely outcome if you set a Firefox User-Agent by hand. Our piece on TLS fingerprinting explains the JA3 and JA4 signals involved.

4. Run a five-day soak, not a five-minute demo. Open the profiles daily, do ordinary work in them, and see what still works at week's end. New profiles almost always work. Week-old profiles are the real test.

Pick Multilogin when the cost of one bad session is high, and GoLogin when the cost of one more profile is what you feel. Nobody should decide on which vendor claims better fingerprint spoofing, because neither claim is independently verifiable and the published research says both are detectable under determined testing. Everything above was read off the vendors' own pages on 18 August 2026, and both change plans and features often, so re-verify the rows that decide your purchase.

Whichever you pick, the work that moves your success rate sits on the network side: one stable exit per profile, geo and locale that agree with that exit, WebRTC closed, and a log that tells you when a profile's IP moved. Get that right and either tool is adequate. Get it wrong and neither saves you. For the wider field, see our roundup of the best antidetect browsers.