diff --git a/website/_includes/_page_models.jade b/website/_includes/_page_models.jade
index 1cab930fb..c7742fa38 100644
--- a/website/_includes/_page_models.jade
+++ b/website/_includes/_page_models.jade
@@ -40,6 +40,8 @@ for id in CURRENT_MODELS
 
             each label in ["Pipeline", "Vectors", "Sources", "Author", "License"]
                 - var field = label.toLowerCase()
+                if field == "vectors"
+                    - field = "vecs"
                 +row
                     +cell.u-nowrap
                         +label=label
diff --git a/website/assets/js/models.js b/website/assets/js/models.js
index 2d371ee1f..f5757c8cb 100644
--- a/website/assets/js/models.js
+++ b/website/assets/js/models.js
@@ -20,21 +20,33 @@ const CHART_FONTS = {
  * @property {function} vectors - Format vector data (entries and dimensions).
  * @property {function} version - Format model version number.
  */
-export const formats = {
+const formats = {
     author: (author, url) => url ? `<a href="${url}" target="_blank">${author}</a>` : author,
     license: (license, url) => url ? `<a href="${url}" target="_blank">${license}</a>` : license,
     sources: sources => (sources instanceof Array) ? sources.join(', ') : sources,
     pipeline: pipes => (pipes && pipes.length) ? pipes.map(p => `<code>${p}</code>`).join(', ') : '-',
-    vectors: vec => vec ? `${abbrNumber(vec.keys)} keys, ${abbrNumber(vec.vectors)} unique vectors (${vec.width} dimensions)` : 'n/a',
+    vectors: vec => formatVectors(vec),
     version: version => `<code>v${version}</code>`
 };
 
+/**
+ * Format word vectors data depending on contents.
+ * @property {Object} data - The vectors object from the model's meta.json.
+ */
+const formatVectors = data => {
+    if (!data) return 'n/a';
+    if (Object.values(data).every(n => n == 0)) return 'context vectors only';
+    const { keys, vectors: vecs, width } = data;
+    return `${abbrNumber(keys)} keys, ${abbrNumber(vecs)} unique vectors (${width} dimensions)`;
+}
+
+
 /**
  * Find the latest version of a model in a compatibility table.
  * @param {string} model - The model name.
  * @param {Object} compat - Compatibility table, keyed by spaCy version.
  */
-export const getLatestVersion = (model, compat = {}) => {
+const getLatestVersion = (model, compat = {}) => {
     for (let [spacy_v, models] of Object.entries(compat)) {
         if (models[model]) return models[model][0];
     }
@@ -90,7 +102,7 @@ export class ModelLoader {
         const tpl = new Templater(modelId);
         tpl.get('table').removeAttribute('data-loading');
         tpl.get('error').style.display = 'block';
-        for (let key of ['sources', 'pipeline', 'vectors', 'author', 'license']) {
+        for (let key of ['sources', 'pipeline', 'vecs', 'author', 'license']) {
             tpl.get(key).parentElement.parentElement.style.display = 'none';
         }
     }
@@ -120,8 +132,8 @@ export class ModelLoader {
         if (author) tpl.fill('author', formats.author(author, url), true);
         if (license) tpl.fill('license', formats.license(license, this.licenses[license]), true);
         if (sources) tpl.fill('sources', formats.sources(sources));
-        if (vectors) tpl.fill('vectors', formats.vectors(vectors));
-        else tpl.get('vectors').parentElement.parentElement.style.display = 'none';
+        if (vectors) tpl.fill('vecs', formats.vectors(vectors));
+        else tpl.get('vecs').parentElement.parentElement.style.display = 'none';
         if (pipeline && pipeline.length) tpl.fill('pipeline', formats.pipeline(pipeline), true);
         else tpl.get('pipeline').parentElement.parentElement.style.display = 'none';
     }
@@ -223,8 +235,9 @@ export class ModelComparer {
         const version = getLatestVersion(name, this.compat);
         const modelName = `${name}-${version}`;
         return new Promise((resolve, reject) => {
+            if (!version) reject();
             // resolve immediately if model already loaded, e.g. in this.models
-            if (this.models[name]) resolve(this.models[name]);
+            else if (this.models[name]) resolve(this.models[name]);
             else fetch(`${this.url}/meta/${modelName}.json`)
                 .then(res => handleResponse(res))
                 .then(json => json.ok ? resolve(this.saveModel(name, json)) : reject())
@@ -306,12 +319,13 @@ export class ModelComparer {
         this.tpl.fill(`size${i}`, size);
         this.tpl.fill(`desc${i}`, description || 'n/a');
         this.tpl.fill(`pipeline${i}`, formats.pipeline(pipeline), true);
-        this.tpl.fill(`vectors${i}`, formats.vectors(vectors));
+        this.tpl.fill(`vecs${i}`, formats.vectors(vectors));
         this.tpl.fill(`sources${i}`, formats.sources(sources));
         this.tpl.fill(`author${i}`, formats.author(author, url), true);
         this.tpl.fill(`license${i}`, formats.license(license, this.licenses[license]), true);
         // check if model accuracy or speed includes one of the pre-set keys
-        for (let key of [...metaKeys, ...Object.keys(this.benchKeys.speed)]) {
+        const allKeys = [].concat(...Object.entries(this.benchKeys).map(([_, v]) => Object.keys(v)));
+        for (let key of allKeys) {
             if (accuracy[key]) this.tpl.fill(`${key}${i}`, accuracy[key].toFixed(2))
             else if (speed[key]) this.tpl.fill(`${key}${i}`, convertNumber(Math.round(speed[key])))
             else this.tpl.fill(`${key}${i}`, 'n/a')
diff --git a/website/models/_data.json b/website/models/_data.json
index d64c94074..8507a3fa1 100644
--- a/website/models/_data.json
+++ b/website/models/_data.json
@@ -68,6 +68,7 @@
         "gpu": "words per second on GPU",
         "pipeline": "Processing pipeline components in order",
         "sources": "Sources of training data",
+        "vecs": "Word vectors included in the model. Models that only support context vectors compute similarity via the tensors shared with the pipeline.",
         "benchmark_parser": "Parser accuracy",
         "benchmark_ner": "NER accuracy",
         "benchmark_speed": "Speed"
diff --git a/website/models/comparison.jade b/website/models/comparison.jade
index 881a9aff4..b0ab61efe 100644
--- a/website/models/comparison.jade
+++ b/website/models/comparison.jade
@@ -53,6 +53,8 @@ div(data-tpl=TPL data-tpl-key="result" style="display: none")
 
         for label in ["Version", "Size", "Pipeline", "Vectors", "Sources", "Author", "License"]
             - var field = label.toLowerCase()
+            if field == "vectors"
+                - field = "vecs"
             +row
                 +cell.u-nowrap
                     +label=label
diff --git a/website/usage/_spacy-101/_word-vectors.jade b/website/usage/_spacy-101/_word-vectors.jade
index bb9add8a6..c38360014 100644
--- a/website/usage/_spacy-101/_word-vectors.jade
+++ b/website/usage/_spacy-101/_word-vectors.jade
@@ -4,9 +4,9 @@ p
     |  Similarity is determined by comparing #[strong word vectors] or "word
     |  embeddings", multi-dimensional meaning representations of a word. Word
     |  vectors can be generated using an algorithm like
-    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
-    |  #[+a("/models") default models] come with
-    |  #[strong 300-dimensional vectors] that look like this:
+    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. spaCy's medium
+    |  #[code md] and large #[code lg] #[+a("/models") models] come with
+    |  #[strong multi-dimensional vectors] that look like this:
 
 +code("banana.vector", false, false, 250).
     array([2.02280000e-01,  -7.66180009e-02,   3.70319992e-01,
diff --git a/website/usage/_vectors-similarity/_basics.jade b/website/usage/_vectors-similarity/_basics.jade
index 07ad6bcd4..734495c6e 100644
--- a/website/usage/_vectors-similarity/_basics.jade
+++ b/website/usage/_vectors-similarity/_basics.jade
@@ -4,12 +4,9 @@
     |  Dense, real valued vectors representing distributional similarity
     |  information are now a cornerstone of practical NLP. The most common way
     |  to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
-    |  family of algorithms. The default
-    |  #[+a("/models/en") English model] installs
-    |  300-dimensional vectors trained on the
-    |  #[+a("http://commoncrawl.org") Common Crawl] corpus.
-    |  If you need to train a word2vec model, we recommend the implementation in
-    |  the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
+    |  family of algorithms. If you need to train a word2vec model, we recommend
+    |  the implementation in the Python library
+    |  #[+a("https://radimrehurek.com/gensim/") Gensim].
 
 include ../_spacy-101/_similarity
 include ../_spacy-101/_word-vectors