diff --git a/crates/brightstaff/src/streaming.rs b/crates/brightstaff/src/streaming.rs
index 40cbbe7c..03913bdc 100644
--- a/crates/brightstaff/src/streaming.rs
+++ b/crates/brightstaff/src/streaming.rs
@@ -100,6 +100,12 @@ impl ExtractedUsage {
 /// Try to pull usage out of an accumulated response body.
 /// Handles both a single JSON object (non-streaming) and SSE streams where the
 /// final `data: {...}` event carries the `usage` field.
+///
+/// Resolved model is captured independently from usage: many providers (DO
+/// Inference Cloud, OpenAI in some modes) emit `model` on every SSE chunk but
+/// `usage` only on the terminal one — and that terminal chunk may omit the
+/// model field entirely. Scanning both fields separately means we still
+/// surface the real upstream model even when usage extraction fails.
 fn extract_usage_from_bytes(buf: &[u8]) -> ExtractedUsage {
     if buf.is_empty() {
         return ExtractedUsage::default();
@@ -113,11 +119,14 @@ fn extract_usage_from_bytes(buf: &[u8]) -> ExtractedUsage {
         }
     }
 
-    // SSE path: scan from the end for a `data:` line containing a usage object.
     let text = match std::str::from_utf8(buf) {
         Ok(t) => t,
         Err(_) => return ExtractedUsage::default(),
     };
+
+    let mut out = ExtractedUsage::default();
+    let mut found_usage = false;
+
     for line in text.lines().rev() {
         let trimmed = line.trim_start();
         let payload = match trimmed.strip_prefix("data:") {
@@ -127,18 +136,50 @@ fn extract_usage_from_bytes(buf: &[u8]) -> ExtractedUsage {
         if payload == "[DONE]" || payload.is_empty() {
             continue;
         }
-        if !payload.contains("\"usage\"") {
+
+        let has_model_field = out.resolved_model.is_none() && payload.contains("\"model\"");
+        let has_usage_field = !found_usage && payload.contains("\"usage\"");
+        if !has_model_field && !has_usage_field {
             continue;
         }
-        if let Ok(value) = serde_json::from_str::<serde_json::Value>(payload) {
+
+        let value = match serde_json::from_str::<serde_json::Value>(payload) {
+            Ok(v) => v,
+            Err(_) => continue,
+        };
+
+        if has_usage_field {
             let u = ExtractedUsage::from_json(&value);
-            if !u.is_empty() {
-                return u;
+            if u.prompt_tokens.is_some()
+                || u.completion_tokens.is_some()
+                || u.total_tokens.is_some()
+            {
+                let prior_model = out.resolved_model.take();
+                out = u;
+                if out.resolved_model.is_none() {
+                    out.resolved_model = prior_model;
+                }
+                found_usage = true;
+                if out.resolved_model.is_some() {
+                    break;
+                }
+                continue;
+            }
+        }
+
+        if out.resolved_model.is_none() {
+            if let Some(model) = value.get("model").and_then(|v| v.as_str()) {
+                if !model.is_empty() {
+                    out.resolved_model = Some(model.to_string());
+                    if found_usage {
+                        break;
+                    }
+                }
             }
         }
     }
 
-    ExtractedUsage::default()
+    out
 }
 
 /// Trait for processing streaming chunks
@@ -634,4 +675,36 @@ data: [DONE]
     fn no_usage_in_body_returns_default() {
         assert!(extract_usage_from_bytes(br#"{"ok":true}"#).is_empty());
     }
+
+    #[test]
+    fn streaming_resolved_model_from_chunk_without_usage() {
+        // DO Inference Cloud often emits model on every chunk and usage only on
+        // the last — and the usage chunk itself omits the model field.
+        let sse = b"data: {\"id\":\"x\",\"model\":\"openai-gpt-5.4\",\"choices\":[{\"delta\":{\"content\":\"hi\"}}]}
+
+data: {\"choices\":[{\"delta\":{},\"finish_reason\":\"stop\"}],\"usage\":{\"prompt_tokens\":7,\"completion_tokens\":3,\"total_tokens\":10}}
+
+data: [DONE]
+
+";
+        let u = extract_usage_from_bytes(sse);
+        assert_eq!(u.prompt_tokens, Some(7));
+        assert_eq!(u.completion_tokens, Some(3));
+        assert_eq!(u.resolved_model.as_deref(), Some("openai-gpt-5.4"));
+    }
+
+    #[test]
+    fn streaming_resolved_model_when_usage_missing() {
+        // Even without any usage chunk, we should still surface the upstream
+        // model so the obs view doesn't fall back to the router alias.
+        let sse = b"data: {\"id\":\"x\",\"model\":\"openai-gpt-5.4\",\"choices\":[{\"delta\":{\"content\":\"hi\"}}]}
+
+data: [DONE]
+
+";
+        let u = extract_usage_from_bytes(sse);
+        assert_eq!(u.prompt_tokens, None);
+        assert_eq!(u.resolved_model.as_deref(), Some("openai-gpt-5.4"));
+        assert!(!u.is_empty());
+    }
 }
diff --git a/crates/hermesllm/src/bin/provider_models.yaml b/crates/hermesllm/src/bin/provider_models.yaml
index 22f69a7d..d07e265d 100644
--- a/crates/hermesllm/src/bin/provider_models.yaml
+++ b/crates/hermesllm/src/bin/provider_models.yaml
@@ -95,6 +95,7 @@ providers:
   anthropic:
   - anthropic/claude-sonnet-4-6
   - anthropic/claude-opus-4-6
+  - anthropic/claude-opus-4-7
   - anthropic/claude-opus-4-5-20251101
   - anthropic/claude-opus-4-5
   - anthropic/claude-haiku-4-5-20251001