diff --git a/readme.md b/readme.md
index a8c15f2c..6cf9f538 100644
--- a/readme.md
+++ b/readme.md
@@ -17,7 +17,7 @@
 
 Matbench Discovery is an [interactive leaderboard](https://janosh.github.io/matbench-discovery/models) and associated [PyPI package](https://pypi.org/project/matbench-discovery) which together make it easy to rank ML energy models on a task designed to simulate a high-throughput discovery campaign for new stable inorganic crystals.
 
-So far, we've tested 8 models covering multiple methodologies ranging from random forests with structure fingerprints to graph neural networks, from one-shot predictors to iterative Bayesian optimizers and interatomic potential relaxers.
+We've tested <slot name="model-count" />models covering multiple methodologies ranging from random forests with structure fingerprints to graph neural networks, from one-shot predictors to iterative Bayesian optimizers and interatomic potential relaxers.
 
 <slot name="best-report" />
 
diff --git a/site/src/routes/+page.svelte b/site/src/routes/+page.svelte
index dbf595f1..074e4445 100644
--- a/site/src/routes/+page.svelte
+++ b/site/src/routes/+page.svelte
@@ -26,6 +26,7 @@
 </script>
 
 <Readme>
+  <span slot="model-count">{Object.keys(all_stats).length}&ensp;</span>
   <div slot="best-report">
     {#if best_model}
       {@const { model_name, F1, R2, DAF, repo, doi } = best_model}