diff --git a/crates/brightstaff/src/router/model_metrics.rs b/crates/brightstaff/src/router/model_metrics.rs
index ceafe4b8..1adb408d 100644
--- a/crates/brightstaff/src/router/model_metrics.rs
+++ b/crates/brightstaff/src/router/model_metrics.rs
@@ -6,7 +6,7 @@ use common::configuration::{
     CostProvider, LatencyProvider, MetricsSource, SelectionPolicy, SelectionPreference,
 };
 use tokio::sync::RwLock;
-use tracing::{info, warn};
+use tracing::{debug, info, warn};
 
 const DO_PRICING_URL: &str = "https://api.digitalocean.com/v2/gen-ai/models/catalog";
 
@@ -80,24 +80,32 @@ impl ModelMetricsService {
     /// Rank `models` by `policy`, returning them in preference order.
     /// Models with no metric data are appended at the end in their original order.
     pub async fn rank_models(&self, models: &[String], policy: &SelectionPolicy) -> Vec<String> {
+        let cost_data = self.cost.read().await;
+        let latency_data = self.latency.read().await;
+        debug!(
+            input_models = ?models,
+            cost_data = ?cost_data.iter().collect::<Vec<_>>(),
+            latency_data = ?latency_data.iter().collect::<Vec<_>>(),
+            prefer = ?policy.prefer,
+            "rank_models called"
+        );
+
         match policy.prefer {
             SelectionPreference::Cheapest => {
-                let data = self.cost.read().await;
                 for m in models {
-                    if !data.contains_key(m.as_str()) {
+                    if !cost_data.contains_key(m.as_str()) {
                         warn!(model = %m, "no cost data for model — ranking last (prefer: cheapest)");
                     }
                 }
-                rank_by_ascending_metric(models, &data)
+                rank_by_ascending_metric(models, &cost_data)
             }
             SelectionPreference::Fastest => {
-                let data = self.latency.read().await;
                 for m in models {
-                    if !data.contains_key(m.as_str()) {
+                    if !latency_data.contains_key(m.as_str()) {
                         warn!(model = %m, "no latency data for model — ranking last (prefer: fastest)");
                     }
                 }
-                rank_by_ascending_metric(models, &data)
+                rank_by_ascending_metric(models, &latency_data)
             }
             SelectionPreference::None => models.to_vec(),
         }
@@ -117,13 +125,20 @@ impl ModelMetricsService {
 fn rank_by_ascending_metric(models: &[String], data: &HashMap<String, f64>) -> Vec<String> {
     let mut with_data: Vec<(&String, f64)> = models
         .iter()
-        .filter_map(|m| data.get(m.as_str()).map(|v| (m, *v)))
+        .filter_map(|m| {
+            let v = *data.get(m.as_str())?;
+            if v.is_nan() {
+                None
+            } else {
+                Some((m, v))
+            }
+        })
         .collect();
     with_data.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
 
     let without_data: Vec<&String> = models
         .iter()
-        .filter(|m| !data.contains_key(m.as_str()))
+        .filter(|m| data.get(m.as_str()).is_none_or(|v| v.is_nan()))
         .collect();
 
     with_data
@@ -352,4 +367,22 @@ mod tests {
         // none → original order, despite gpt-4o-mini being cheaper
         assert_eq!(result, vec!["gpt-4o", "gpt-4o-mini"]);
     }
+
+    #[test]
+    fn test_rank_by_ascending_metric_nan_treated_as_missing() {
+        let models = vec![
+            "a".to_string(),
+            "b".to_string(),
+            "c".to_string(),
+            "d".to_string(),
+        ];
+        let mut data = HashMap::new();
+        data.insert("a".to_string(), f64::NAN);
+        data.insert("b".to_string(), 0.5);
+        data.insert("c".to_string(), 0.1);
+        // "d" has no entry at all
+        let result = rank_by_ascending_metric(&models, &data);
+        // c (0.1) < b (0.5), then NaN "a" and missing "d" appended in original order
+        assert_eq!(result, vec!["c", "b", "a", "d"]);
+    }
 }