diff --git a/bc2/lib/embedding/openai.py b/bc2/lib/embedding/openai.py
index cd49767..d1a4f41 100644
--- a/bc2/lib/embedding/openai.py
+++ b/bc2/lib/embedding/openai.py
@@ -107,7 +107,13 @@ def __init__(
 
     def embed(self, text: str) -> Embedding:
         text = self._trim_input(text)
-        result = self.client.embeddings.create(input=text, model=self.config.model)
+        params = {
+            "input": text,
+            "model": self.config.model,
+        }
+        if self.config.dimensions:
+            params["dimensions"] = self.config.dimensions
+        result = self.client.embeddings.create(**params)
         return self._format_result(result)
 
     async def embed_async(self, text: str) -> Embedding:
diff --git a/bc2/lib/embedding/test_openai.py b/bc2/lib/embedding/test_openai.py
index a98d9fb..bd555a0 100644
--- a/bc2/lib/embedding/test_openai.py
+++ b/bc2/lib/embedding/test_openai.py
@@ -1,9 +1,16 @@
+from unittest.mock import MagicMock
+
 import pytest
+from openai import AsyncOpenAI, OpenAI
 
 from bc2.core.common.openai import OpenAIClientConfig
 from bc2.core.common.openai_metadata import get_encoding_for_model
 
-from .openai import OpenAIEmbeddingConfig, OpenAIEmbeddingGeneratorConfig
+from .openai import (
+    OpenAIEmbeddingConfig,
+    OpenAIEmbeddingDriver,
+    OpenAIEmbeddingGeneratorConfig,
+)
 
 
 @pytest.mark.parametrize(
@@ -33,3 +40,57 @@ def test_trim_input(model: str, text: str, trimmed: str):
 
     assert embed.driver._trim_input(text) == trimmed
     assert len(encoding.encode(trimmed)) <= embed.generator.max_input_tokens
+
+
+def _mock_embedding_response(model: str = "text-embedding-3-large") -> MagicMock:
+    response = MagicMock()
+    response.data = [MagicMock(embedding=[0.0, 0.1, 0.2])]
+    response.model = model
+    return response
+
+
+@pytest.mark.parametrize(
+    "dimensions",
+    [None, 256, 1024],
+    ids=["unset", "256", "1024"],
+)
+def test_embed_passes_dimensions(dimensions: int | None):
+    client = MagicMock(spec=OpenAI)
+    client.embeddings.create.return_value = _mock_embedding_response()
+    aclient = MagicMock(spec=AsyncOpenAI)
+
+    config = OpenAIEmbeddingGeneratorConfig(
+        model="text-embedding-3-large",
+        dimensions=dimensions,
+    )
+    driver = OpenAIEmbeddingDriver(client, aclient, config)
+
+    driver.embed("hello")
+
+    client.embeddings.create.assert_called_once()
+    call_kwargs = client.embeddings.create.call_args.kwargs
+    assert call_kwargs["input"] == "hello"
+    assert call_kwargs["model"] == "text-embedding-3-large"
+    if dimensions is None:
+        assert "dimensions" not in call_kwargs
+    else:
+        assert call_kwargs["dimensions"] == dimensions
+
+
+def test_embed_config_dimensions_default():
+    config = OpenAIEmbeddingConfig(
+        client=OpenAIClientConfig(api_key="test"),
+        generator=OpenAIEmbeddingGeneratorConfig(model="text-embedding-3-large"),
+    )
+    assert config.generator.dimensions is None
+
+
+def test_embed_config_dimensions_override():
+    config = OpenAIEmbeddingConfig(
+        client=OpenAIClientConfig(api_key="test"),
+        generator=OpenAIEmbeddingGeneratorConfig(
+            model="text-embedding-3-large", dimensions=512
+        ),
+    )
+    assert config.generator.dimensions == 512
+    assert config.generator.model_dimensions == 512