diff --git a/py/autoevals/string.py b/py/autoevals/string.py
index 92d9c3a..a14ecde 100644
--- a/py/autoevals/string.py
+++ b/py/autoevals/string.py
@@ -132,33 +132,42 @@ def __init__(
 
         self.client = client
 
+    def _cache_key(self, value):
+        # The cache is shared across scorer instances, so include every setting that
+        # can select a different embedding provider. A custom client may route to a
+        # different provider even when the model and base URL match.
+        client_key = id(self.client) if self.client is not None else None
+        return (self.extra_args["model"], self.prefix, self.extra_args.get("base_url"), client_key, 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..de754ba 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()
+
+
+@pytest.mark.parametrize("run_async", [False, True])
+@pytest.mark.asyncio
+async def test_embedding_cache_isolated_by_configuration(monkeypatch, run_async):
+    calls = []
+
+    def fake_run_cached_request(**kwargs):
+        calls.append((kwargs["model"], kwargs["input"], kwargs.get("base_url"), kwargs.get("client")))
+        return {"data": [{"embedding": [1.0, 0.0]}]}
+
+    async def fake_arun_cached_request(**kwargs):
+        calls.append((kwargs["model"], kwargs["input"], kwargs.get("base_url"), kwargs.get("client")))
+        return {"data": [{"embedding": [1.0, 0.0]}]}
+
+    if run_async:
+        monkeypatch.setattr(string_module, "arun_cached_request", fake_arun_cached_request)
+    else:
+        monkeypatch.setattr(string_module, "run_cached_request", fake_run_cached_request)
+
+    value = {"topic": "cache"}
+    client_a = object()
+    client_b = object()
+    evaluators = [
+        EmbeddingSimilarity(model="model-a", prefix="query: ", base_url="https://one.example", client=client_a),
+        EmbeddingSimilarity(model="model-a", prefix="query: ", base_url="https://one.example", client=client_a),
+        EmbeddingSimilarity(model="model-b", prefix="query: ", base_url="https://one.example", client=client_a),
+        EmbeddingSimilarity(model="model-a", prefix="document: ", base_url="https://one.example", client=client_a),
+        EmbeddingSimilarity(model="model-a", prefix="query: ", base_url="https://two.example", client=client_a),
+        EmbeddingSimilarity(model="model-a", prefix="query: ", base_url="https://one.example", client=client_b),
+    ]
+
+    for evaluator in evaluators:
+        if run_async:
+            await evaluator.eval_async(value, value)
+        else:
+            evaluator.eval(value, value)
+
+    assert calls == [
+        ("model-a", 'query: {"topic": "cache"}', "https://one.example", client_a),
+        ("model-b", 'query: {"topic": "cache"}', "https://one.example", client_a),
+        ("model-a", 'document: {"topic": "cache"}', "https://one.example", client_a),
+        ("model-a", 'query: {"topic": "cache"}', "https://two.example", client_a),
+        ("model-a", 'query: {"topic": "cache"}', "https://one.example", client_b),
+    ]
+
+
 def test_embeddings():
     evaluator = EmbeddingSimilarity(prefix="resource type: ")
     for word, synonyms in SYNONYMS: