✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount

SleekRank for embedding model comparisons

Keep embedding models and pairs as rows, and SleekRank generates /embeddings/{model}/ and /embeddings/{a}-vs-{b}/ pages from your existing WordPress template, with dimensions, max tokens, pricing, and MTEB scores pulled from one source.

€50 off for the first 100 lifetime licenses!

SleekRank for embedding model comparisons

Embedding models ship faster than reviews can keep up

Embedding models change pricing, default dimensions, and benchmark scores constantly. A comparison of OpenAI, Cohere, Voyage, or open-weight models written six months ago is likely wrong on per-million-token cost, recommended dimensions, or MTEB ranking. Developer-facing publications running per-model reviews and head-to-heads end up with feature tables that disagree with the vendor's pricing and model card.

SleekRank reads one source, a sheet of models with name, vendor, dimensions, max_tokens, price_per_million_tokens, multilingual flag, license, mteb_score, and a verdict. It drives per-model pages at /embeddings/{model}/ and pair pages at /embeddings/{a}-vs-{b}/ from the same row data. The base page is a normal WordPress page, and row values fill the spec table, pricing block, and benchmark column.

MTEB score is the field readers anchor on, and it is the one that goes stale fastest. When a new release tops the leaderboard or a model gets re-evaluated, every page quoting the old rank looks careless. Stored as columns for mteb_score, mteb_rank, and benchmark_date, the page renders all three via tag mapping, with a freshness note pulled from benchmark_date so readers can see when the score was captured.

Workflow

From model sheet to per-model and head-to-head pages

1

Build the model sheet

One row per model with slug, name, vendor, dimensions, max tokens, price per million tokens, multilingual flag, license, MTEB score, MTEB rank, benchmark date, and a verdict paragraph.
2

Wire the model template

Place an h1, spec table, pricing block, multilingual badge, MTEB scorecard, and verdict on a WordPress page. Tag, selector, list, and meta mappings inject row values per model.
3

Add a pairs page group

A second page group from a pairs sheet generates /embeddings/{a}-vs-{b}/ pages, joining both rows side by side with a head-to-head verdict and a winner column specific to the matchup.
4

Refresh on release or leaderboard news

When a vendor ships a new model, cuts prices, or the MTEB leaderboard refreshes, edit the relevant columns and flush the cache. Per-model and pair pages reflect the new facts before the next crawl.

Data in, pages out

Model matrix in, embedding pages out

Each row is one embedding model with dimensions, max tokens, price, and MTEB snapshot.
Data source: Google Sheets / CSV
slug model dimensions price_per_m_tokens mteb_score
openai-text-embedding-3-large OpenAI text-embedding-3-large 3072 0.13 64.6
openai-text-embedding-3-small OpenAI text-embedding-3-small 1536 0.02 62.3
cohere-embed-v3 Cohere Embed v3 1024 0.10 64.5
voyage-3 Voyage-3 1024 0.06 65.0
bge-large-en-v1-5 BGE Large EN v1.5 (open weight) 1024 0 (self-host) 64.2
URL pattern: /embeddings/{slug}/
Generated pages
  • /embeddings/openai-text-embedding-3-large/
  • /embeddings/cohere-embed-v3/
  • /embeddings/voyage-3/
  • /embeddings/bge-large-en-v1-5/
  • /embeddings/openai-text-embedding-3-large-vs-voyage-3/

Comparison

Hand-edited embedding reviews versus one synced matrix

Manual model reviews

  • Per-token prices drift faster than editors can patch pages
  • Dimension recommendations disagree across pages
  • MTEB scores go stale after every leaderboard refresh
  • Adding a new model means writing a stack of new pages
  • Multilingual support claims rarely propagate to every review
  • Pair verdicts fall out of step with per-model facts

SleekRank

  • One row drives the per-model page and every pair
  • Pricing columns flow through to all cost comparisons
  • Dimensions and max tokens stay consistent everywhere
  • MTEB score renders with a benchmark_date freshness note
  • Cache flush updates every page after a sheet edit
  • Sitemap reflects current models as the matrix evolves

Features

What SleekRank gives you for embedding model comparisons

Pricing in one place

Per-million-token pricing renders into every cost block across the catalog, so a vendor cut or a new tier is one row edit instead of a sweep across per-model and pair pages.

Pair page support

A pairs page group joins two model rows into a /a-vs-b/ template, so head-to-heads stay in step with per-model pages, with side-by-side specs and a pair-specific verdict.

Benchmark snapshot

MTEB score, rank, and benchmark_date columns render a per-model freshness pill and a comparison grid on pair pages, so leaderboard refreshes flow through cleanly.

Use cases

Who builds embedding model comparisons with SleekRank

RAG-focused publications

Sites covering retrieval and search stacks run a master matrix of embedding models, with dimensions and pricing columns driving every per-model and pair page.

AI consultancies

Consulting firms publish embedding vendor resources for clients picking a model for production retrieval, with one sheet driving public reference pages used in pitches.

Open-source maintainers

Maintainers of retrieval libraries keep a supported-models matrix current, with rows driving public reference pages alongside the project's documentation.

The bigger picture

Why embedding comparisons need a data layer

Teams picking an embedding model are sizing a recurring cost line and a retrieval quality decision they will live with through several deploy cycles. They care about dimensions, price per million tokens, multilingual coverage, and MTEB performance, all of which change on the vendor's release cadence rather than the publication's editorial calendar. Hand-edited review pages drift on exactly these axes because patching every page when OpenAI cuts prices or a new open-weight model tops the leaderboard is a manual sweep no team completes before the facts move again.

SleekRank pins these facts to a single row, so when a vendor changes pricing or a benchmark refreshes, every per-model and pair page updates after the next cache cycle. For RAG-focused publications and infrastructure consultancies, this is the difference between a comparison catalog that holds credibility across release seasons and a list of half-correct numbers that gets quietly replaced by a competitor's fresher table.

Questions

Common questions about SleekRank for embedding model comparisons

Not directly. SleekRank renders from your data source. If the sheet is updated by a scraper hitting the MTEB leaderboard or by your editorial team on a regular cadence, the new scores flow through on the next cache cycle. The data acquisition layer lives upstream of SleekRank, which renders whatever is current in the source consistently across solo and pair pages.

 

Both page groups read from the models sheet. The pairs group joins two rows at render time using a slug pair from a pairs sheet. A change to a model row updates every page that references the model, including per-model, pair, and any category roll-ups, after the cache window expires.

 

Define another page group with a different URL pattern, source from the same sheet, and filter on language or domain columns. A /embeddings/multilingual/ landing page becomes its own SEO target, with intro copy on the base page and the matching subset rendered from the source. The same approach works for open-weight or code-specific cuts.

 

Yes. Add an open_weight flag and a side dataset keyed by model slug listing hosts, hardware tiers, and per-hour or per-million-token pricing. The template renders a hosts table beneath the headline pricing block, joined at render time, so BGE, GTE, and other open-weight families render cleanly without splitting into multiple rows.

 

Yes. The pairs sheet has its own verdict column. The per-model verdicts handle solo pages, and the pair verdict drives head-to-heads. If a pair row's verdict is empty, the template can fall back to a templated summary built from the two model rows' verdict snippets. Either way, you control the wording per pair when the comparison deserves it.

 

Add a deprecated flag and a successor_slug column. The template can render a deprecation banner via selector mapping when the flag is true, and the successor field links to the recommended replacement. Or drop the row entirely so the URL stops generating, and add a 301 redirect to the successor to preserve link equity for backlinks.

 

Yes. Map an image URL column to og:image with the meta type, so each per-model page renders its own social card. For per-pair pages, you can render both vendor logos side by side. Pairing with SleekPixel lets the OG image render on the fly from the row data, overlaying model name, dimensions, and MTEB on a styled background.

 

Store benchmark_date next to the score column and render a small freshness pill on each page that reads from it. Readers can see when the score was captured, and an automated update job can rewrite older rows when a refresh runs. The whole catalog stays auditable without a separate methodology page per model.

 

Pricing

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The Bundle (unlimited sites)

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What’s included

  • SleekAI

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  • SleekMotion

  • SleekPixel

  • SleekRank

  • SleekView