diff --git a/py/autoevals/string.py b/py/autoevals/string.py
index 92d9c3a..598c2e7 100644
--- a/py/autoevals/string.py
+++ b/py/autoevals/string.py
@@ -132,33 +132,38 @@ def __init__(
 
         self.client = client
 
+    def _cache_key(self, value):
+        return (self.extra_args["model"], self.prefix, value)
+
     async def _a_embed(self, value):
         value = normalize_value(value, maybe_object=False)
+        cache_key = self._cache_key(value)
         with self._CACHE_LOCK:
-            if value in self._CACHE:
-                return self._CACHE[value]
+            if cache_key in self._CACHE:
+                return self._CACHE[cache_key]
 
         result = await arun_cached_request(
             client=self.client, request_type="embed", input=f"{self.prefix}{value}", **self.extra_args
         )
 
         with self._CACHE_LOCK:
-            self._CACHE[value] = result
+            self._CACHE[cache_key] = result
 
         return result
 
     def _embed(self, value):
         value = normalize_value(value, maybe_object=False)
+        cache_key = self._cache_key(value)
         with self._CACHE_LOCK:
-            if value in self._CACHE:
-                return self._CACHE[value]
+            if cache_key in self._CACHE:
+                return self._CACHE[cache_key]
 
         result = run_cached_request(
             client=self.client, request_type="embed", input=f"{self.prefix}{value}", **self.extra_args
         )
 
         with self._CACHE_LOCK:
-            self._CACHE[value] = result
+            self._CACHE[cache_key] = result
 
         return result
 
diff --git a/py/autoevals/test_embeddings.py b/py/autoevals/test_embeddings.py
index 6df632a..d2f6024 100644
--- a/py/autoevals/test_embeddings.py
+++ b/py/autoevals/test_embeddings.py
@@ -1,5 +1,8 @@
 import asyncio
 
+import pytest
+
+import autoevals.string as string_module
 from autoevals import EmbeddingSimilarity
 
 SYNONYMS = [
@@ -11,6 +14,60 @@
 UNRELATED = ["water", "The quick brown fox jumps over the lazy dog", "I like to eat apples"]
 
 
+@pytest.fixture(autouse=True)
+def reset_embedding_cache():
+    with EmbeddingSimilarity._CACHE_LOCK:
+        EmbeddingSimilarity._CACHE.clear()
+    yield
+    with EmbeddingSimilarity._CACHE_LOCK:
+        EmbeddingSimilarity._CACHE.clear()
+
+
+def test_embedding_cache_isolated_by_model_and_prefix(monkeypatch):
+    calls = []
+
+    def fake_run_cached_request(**kwargs):
+        calls.append((kwargs["model"], kwargs["input"]))
+        return {"data": [{"embedding": [1.0, 0.0]}]}
+
+    monkeypatch.setattr(string_module, "run_cached_request", fake_run_cached_request)
+    value = {"topic": "cache"}
+
+    EmbeddingSimilarity(model="model-a", prefix="query: ").eval(value, value)
+    EmbeddingSimilarity(model="model-a", prefix="query: ").eval(value, value)
+    EmbeddingSimilarity(model="model-b", prefix="query: ").eval(value, value)
+    EmbeddingSimilarity(model="model-a", prefix="document: ").eval(value, value)
+
+    assert calls == [
+        ("model-a", 'query: {"topic": "cache"}'),
+        ("model-b", 'query: {"topic": "cache"}'),
+        ("model-a", 'document: {"topic": "cache"}'),
+    ]
+
+
+@pytest.mark.asyncio
+async def test_async_embedding_cache_isolated_by_model_and_prefix(monkeypatch):
+    calls = []
+
+    async def fake_arun_cached_request(**kwargs):
+        calls.append((kwargs["model"], kwargs["input"]))
+        return {"data": [{"embedding": [1.0, 0.0]}]}
+
+    monkeypatch.setattr(string_module, "arun_cached_request", fake_arun_cached_request)
+    value = {"topic": "cache"}
+
+    await EmbeddingSimilarity(model="model-a", prefix="query: ").eval_async(value, value)
+    await EmbeddingSimilarity(model="model-a", prefix="query: ").eval_async(value, value)
+    await EmbeddingSimilarity(model="model-b", prefix="query: ").eval_async(value, value)
+    await EmbeddingSimilarity(model="model-a", prefix="document: ").eval_async(value, value)
+
+    assert calls == [
+        ("model-a", 'query: {"topic": "cache"}'),
+        ("model-b", 'query: {"topic": "cache"}'),
+        ("model-a", 'document: {"topic": "cache"}'),
+    ]
+
+
 def test_embeddings():
     evaluator = EmbeddingSimilarity(prefix="resource type: ")
     for word, synonyms in SYNONYMS: