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GoldieBench Blog · 7 min read

White Label Link Building + Which AI Models Build The Best Linkable Pages, With Real GoldieBench Scores

GoldieBench has no link building test, but its Page tasks show which models draft the strongest resource pages. Here are the real scores and where people still matter.

Illustrated benchmark leaderboard beside a web page wireframe linked by gold chains to smaller website cards

White label link building lives or dies on the asset behind the pitch, so the AI model you use to draft resource pages and client reports matters more than most agencies think.

Publishers link to pages that help their readers, and models are now writing the first draft of many of those pages.

GoldieBench has no link building test, but its three Page tasks are the closest match to building a linkable resource in one shot.

Every score below was read on live goldiebench.com pages on 11 November 2026.

When you want people to run the outreach itself, book a free strategy session with Julian's team.

What The Bench Can Tell A Link Builder

A white label programme needs three kinds of writing: resource pages worth linking to, outreach drafts and branded client reports.

Only the first one maps onto anything GoldieBench measures.

The Page tasks ask a model to build a full web page from one prompt, which is close to producing a guide, a calculator page or a data page for outreach.

So read these scores as a signal for asset building, not for the link work itself.

How GoldieBench Scores Work

The methodology page sets out the rules.

Every model gets the same fixed prompt for each task, with one attempt and no help.

The output has to be one self-contained HTML file that runs in a browser.

Each score is Julian's own number from 0 to 10, and the ranking is the average across every task a model was scored on.

The leaderboard showed 50 tasks and 33 models on the day we read it, grouped into Games, Visuals, Sims and Pages.

Five Models And Their Overall Scores

We picked five models with full or near-full task coverage and a spread of strengths.

These are the overall averages from the leaderboard.

ModelOverall avg /10Tasks"Best at" tag
Claude Fable 58.1047Games
Kimi K37.8950Games
GLM-5.27.7747Pages
Claude Opus 5.57.5750Sims
MiMo-V2.6 Pro6.3550Pages

The Claude Fable 5 model page also shows a separate 7.72 average, labelled as a one-shot pass before agentic repair of 8 crashed or weak tasks.

MiMo-V2.6 Pro sits low overall, yet the leaderboard credits it with 5 gold, 8 silver and 4 bronze task medals, so its average hides some sharp peaks.

Page Task Scores Side By Side

Each model page lists its per-task scores.

Here are the three Page tasks for all five.

ModelLanding PageWebos PageAipbpromo Page
GLM-5.29.0 🥇7.57.5
Kimi K38.68.66.5
MiMo-V2.6 Pro8.18.7 🥉7.8
Claude Opus 5.57.58.7 🥉7.2
Claude Fable 57.28.07.5

GLM-5.2 takes gold on the Landing Page task with 9.0.

MiMo-V2.6 Pro and Claude Opus 5.5 both post 8.7 on Webos Page, each marked bronze.

Kimi K3 is the most even of the five on the first two tasks, at 8.6 on both.

The promo page task is the hardest of the three here, with no model above 7.8.

Which Model For Which Link Building Job

Here is how we would route the work, based only on those numbers.

Job in a white label programmeModel to try firstWhy, from the bench
Resource or guide page to pitchGLM-5.2Gold on Landing Page at 9.0
Interactive tool page, such as a checklist or calculatorMiMo-V2.6 Pro or Claude Opus 5.5Both 8.7 on Webos Page, the more app-like task
A steady all-rounder for mixed page workKimi K38.6 on two Page tasks, 7.89 overall across 50 tasks
Long briefs that feed many tasksClaude Fable 5Highest overall average of the five at 8.10
Outreach emails and client reportsAny, with a human editorNo bench task covers them

That split is a starting point, not a rule.

Run the same brief through two models and keep the page a publisher would actually want to link to.

Where The Bench Says Nothing

Be clear about the gaps before you lean on these scores.

No task measures factual accuracy, which is the first thing an editor checks before linking.

No task measures tone in a cold email, which decides whether outreach gets a reply.

No task checks whether a link carries the right rel attribute or sits on a relevant site.

Those jobs need people, documented rules and a review step.

The Policy Layer No Model Handles

Whatever model drafts the asset, the links themselves must follow Google's rules.

Google's spam policies define link spam as links built mainly to manipulate rankings, including paying for links that pass ranking credit and using automated programs to create links.

The same policy accepts paid placements as normal advertising when they carry rel="nofollow" or rel="sponsored".

Google's outbound link guide names sponsored as the preferred value for paid links.

Its site reputation policy lists white-label services as one example of third-party content, and it becomes a policy issue only when a host carries it chiefly for that host's ranking strength.

Bing's Webmaster Guidelines name link buying and private networks as link schemes too.

Scaled content matters as well, since Google counts pages generated mainly to manipulate rankings as abuse no matter how they are made.

So one strong, useful asset per campaign beats a stack of thin model-written pages every time.

A Worked Example: Linkable Asset For A Garden Centre Client

Here is how an agency could run one campaign end to end.

  1. Brief GLM-5.2 to draft a seasonal planting guide page for the garden centre, using only facts the owner supplies.
  2. Brief a second model, such as Kimi K3, with the same prompt and compare the two drafts.
  3. Have the owner check every planting date and plant name, then publish the stronger page.
  4. Build a prospect list of local gardening blogs, allotment groups and community news sites, and approve each one by hand.
  5. Send personal pitches that point each editor at the guide, with no payment for editorial links.
  6. Tag any paid sponsorship with rel="sponsored", check every live link and report it under your agency brand.

The model saved drafting time in steps 1 and 2.

Everything from step 3 onward is human work.

For a full view of vetting the people side, see our guide to choosing a link building agency.

Where Julian's Team Comes In

The agency's own line is "Manual outreach, no PBNs", backed by a five-step process that ends with every live link in a report.

Julian's published, self-reported figures show 240+ clients served across 1,000+ campaigns.

Julian also scores every model on this bench himself, writes about link building in his book SEO Link Building Mastery and publishes tests for 425K+ YouTube subscribers.

Nobody can promise a ranking, and Google's hiring guide says to avoid anyone who guarantees one.

What a good team can promise is a clear method, real outreach and every link on the report.

If you want to run the model side yourself, learn AI SEO yourself inside the Boardroom.

Also On Our Network

Pick the model by its Page scores, keep humans on facts, outreach and tagging, and book a free strategy session when you want the links built, or Book a free SEO strategy session on the calendar.

Strong assets and fair outreach are what make white label link building work.

FAQ

Which model scored best on the GoldieBench Landing Page task?

Of the five models compared here, GLM-5.2 scored highest with 9.0 and a gold medal, read on its goldiebench.com model page on 11 November 2026.

Does GoldieBench test link building directly?

No. It scores one-shot builds in Games, Visuals, Sims and Pages. The Page tasks are the closest match to drafting a resource page for outreach.

Can an AI model handle white label link building on its own?

No. Models can draft assets and reports, but prospect approval, outreach, rel attribute checks and fact checks need people.

Why not mass-produce pages with the top model?

Google treats pages generated mainly to manipulate rankings as scaled content abuse, however they are made. One useful asset per campaign works better.

The same stack Julian uses

Run this stack yourself.

Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 3,400+ founders shipping with it every day all live inside the AI Profit Boardroom.

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