diff --git a/crates/goose/src/providers/openai.rs b/crates/goose/src/providers/openai.rs
index 9884d147bffc..f3381d27beab 100644
--- a/crates/goose/src/providers/openai.rs
+++ b/crates/goose/src/providers/openai.rs
@@ -1,7 +1,7 @@
 use anyhow::Result;
 use async_trait::async_trait;
 use reqwest::Client;
-use serde_json::Value;
+use serde_json::{json, Value};
 use std::collections::HashMap;
 use std::time::Duration;
 
@@ -123,6 +123,79 @@ impl OpenAiProvider {
     }
 }
 
+/// Update the request when using anthropic model.
+/// For anthropic model, we can enable prompt caching to save cost. Since openrouter is the OpenAI compatible
+/// endpoint, we need to modify the open ai request to have anthropic cache control field.
+fn update_request_for_anthropic(original_payload: &Value) -> Value {
+    let mut payload = original_payload.clone();
+
+    if let Some(messages_spec) = payload
+        .as_object_mut()
+        .and_then(|obj| obj.get_mut("messages"))
+        .and_then(|messages| messages.as_array_mut())
+    {
+        // Add "cache_control" to the last and second-to-last "user" messages.
+        // During each turn, we mark the final message with cache_control so the conversation can be
+        // incrementally cached. The second-to-last user message is also marked for caching with the
+        // cache_control parameter, so that this checkpoint can read from the previous cache.
+        let mut user_count = 0;
+        for message in messages_spec.iter_mut().rev() {
+            if message.get("role") == Some(&json!("user")) {
+                if let Some(content) = message.get_mut("content") {
+                    if let Some(content_str) = content.as_str() {
+                        *content = json!([{
+                            "type": "text",
+                            "text": content_str,
+                            "cache_control": { "type": "ephemeral" }
+                        }]);
+                    }
+                }
+                user_count += 1;
+                if user_count >= 2 {
+                    break;
+                }
+            }
+        }
+
+        // Update the system message to have cache_control field.
+        if let Some(system_message) = messages_spec
+            .iter_mut()
+            .find(|msg| msg.get("role") == Some(&json!("system")))
+        {
+            if let Some(content) = system_message.get_mut("content") {
+                if let Some(content_str) = content.as_str() {
+                    *system_message = json!({
+                        "role": "system",
+                        "content": [{
+                            "type": "text",
+                            "text": content_str,
+                            "cache_control": { "type": "ephemeral" }
+                        }]
+                    });
+                }
+            }
+        }
+    }
+
+    if let Some(tools_spec) = payload
+        .as_object_mut()
+        .and_then(|obj| obj.get_mut("tools"))
+        .and_then(|tools| tools.as_array_mut())
+    {
+        // Add "cache_control" to the last tool spec, if any. This means that all tool definitions,
+        // will be cached as a single prefix.
+        if let Some(last_tool) = tools_spec.last_mut() {
+            if let Some(function) = last_tool.get_mut("function") {
+                function
+                    .as_object_mut()
+                    .unwrap()
+                    .insert("cache_control".to_string(), json!({ "type": "ephemeral" }));
+            }
+        }
+    }
+    payload
+}
+
 #[async_trait]
 impl Provider for OpenAiProvider {
     fn metadata() -> ProviderMetadata {
@@ -167,7 +240,13 @@ impl Provider for OpenAiProvider {
         messages: &[Message],
         tools: &[Tool],
     ) -> Result<(Message, ProviderUsage), ProviderError> {
-        let payload = create_request(&self.model, system, messages, tools, &ImageFormat::OpenAi)?;
+        let mut payload =
+            create_request(&self.model, system, messages, tools, &ImageFormat::OpenAi)?;
+
+        // Add cache_control for claude models (LiteLLM and other OpenAI-compatible services)
+        if self.model.model_name.to_lowercase().contains("claude") {
+            payload = update_request_for_anthropic(&payload);
+        }
 
         // Make request
         let response = self.post(payload.clone()).await?;