diff --git a/tests/test_litellm/llms/voyage/test_voyage_contextual_embedding.py b/tests/test_litellm/llms/voyage/test_voyage_contextual_embedding.py
new file mode 100644
index 00000000000..865608e286f
--- /dev/null
+++ b/tests/test_litellm/llms/voyage/test_voyage_contextual_embedding.py
@@ -0,0 +1,222 @@
+import json
+from unittest.mock import MagicMock
+
+import pytest
+
+
+class TestVoyageContextualEmbeddings:
+    def test_contextual_model_detection(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        assert VoyageContextualEmbeddingConfig.is_contextualized_embeddings("voyage-context-3")
+        assert VoyageContextualEmbeddingConfig.is_contextualized_embeddings("voyage-context-4")
+        assert not VoyageContextualEmbeddingConfig.is_contextualized_embeddings("voyage-3-lite")
+
+    def test_url_generation(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        assert (
+            config.get_complete_url(None, None, "voyage-context-4", {}, {})
+            == "https://api.voyageai.com/v1/contextualizedembeddings"
+        )
+        assert (
+            config.get_complete_url("https://custom.api.com", None, "voyage-context-4", {}, {})
+            == "https://custom.api.com/contextualizedembeddings"
+        )
+        assert (
+            config.get_complete_url(
+                "https://custom.api.com/contextualizedembeddings",
+                None,
+                "voyage-context-4",
+                {},
+                {},
+            )
+            == "https://custom.api.com/contextualizedembeddings"
+        )
+
+    def test_get_supported_openai_params(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        assert config.get_supported_openai_params("voyage-context-4") == [
+            "encoding_format",
+            "dimensions",
+        ]
+
+    def test_map_openai_params(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        result = config.map_openai_params(
+            {"encoding_format": "float", "dimensions": 512}, {}, "voyage-context-4", False
+        )
+        assert result["encoding_format"] == "float"
+        assert result["output_dimension"] == 512
+
+    def test_validate_environment_with_api_key(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        headers = config.validate_environment(
+            {}, "voyage-context-4", [], {}, {}, api_key="test-key"
+        )
+        assert headers == {"Authorization": "Bearer test-key"}
+
+    def test_validate_environment_secret_fallback(self, monkeypatch):
+        import litellm.llms.voyage.embedding.transformation_contextual as module
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        def fake_get_secret(name):
+            return "secret-key" if name == "VOYAGE_API_KEY" else None
+
+        monkeypatch.setattr(module, "get_secret_str", fake_get_secret)
+        config = VoyageContextualEmbeddingConfig()
+        headers = config.validate_environment(
+            {}, "voyage-context-4", [], {}, {}, api_key=None
+        )
+        assert headers == {"Authorization": "Bearer secret-key"}
+
+    def test_nested_list_passthrough(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        nested = [["Hello", "world"], ["Test"]]
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", nested, {}, {}
+        )
+        assert transformed["inputs"] == nested
+        assert transformed["model"] == "voyage-context-4"
+        assert "enable_auto_chunking" not in transformed
+
+    def test_flat_list_str_auto_chunked(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", ["Hello", "world"], {}, {}
+        )
+        assert transformed["inputs"] == ["Hello", "world"]
+        assert transformed["enable_auto_chunking"] is True
+        assert transformed["chunk_size"] == 32000
+        assert transformed["input_type"] == "document"
+
+    def test_flat_list_str_query_no_auto_chunk(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", ["Hello", "world"], {"input_type": "query"}, {}
+        )
+        assert transformed["inputs"] == ["Hello", "world"]
+        assert transformed["input_type"] == "query"
+        assert "enable_auto_chunking" not in transformed
+
+    def test_flat_list_str_document_preserves_input_type(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", ["Hello"], {"input_type": "document"}, {}
+        )
+        assert transformed["input_type"] == "document"
+        assert transformed["enable_auto_chunking"] is True
+
+    def test_single_string_auto_chunked(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", "Hello", {}, {}
+        )
+        assert transformed["inputs"] == ["Hello"]
+        assert transformed["enable_auto_chunking"] is True
+        assert transformed["input_type"] == "document"
+
+    def test_single_string_query_no_auto_chunk(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+
+        config = VoyageContextualEmbeddingConfig()
+        transformed = config.transform_embedding_request(
+            "voyage-context-4", "Hello", {"input_type": "query"}, {}
+        )
+        assert transformed["inputs"] == ["Hello"]
+        assert transformed["input_type"] == "query"
+        assert "enable_auto_chunking" not in transformed
+
+    def test_response_transformation(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+        )
+        from litellm.types.utils import EmbeddingResponse
+
+        config = VoyageContextualEmbeddingConfig()
+        response_payload = {
+            "object": "list",
+            "data": [{"object": "embedding", "embedding": [0.1, 0.2], "index": 0}],
+            "model": "voyage-context-4",
+            "usage": {"total_tokens": 24},
+        }
+        raw_response = MagicMock()
+        raw_response.json.return_value = response_payload
+        raw_response.status_code = 200
+        raw_response.text = json.dumps(response_payload)
+
+        model_response = EmbeddingResponse()
+        transformed = config.transform_embedding_response(
+            "voyage-context-4", raw_response, model_response, MagicMock()
+        )
+        assert transformed.model == "voyage-context-4"
+        assert transformed.object == "list"
+        assert transformed.data == response_payload["data"]
+        assert transformed.usage.prompt_tokens == 24
+        assert transformed.usage.total_tokens == 24
+
+    def test_error_response_and_error_class(self):
+        from litellm.llms.voyage.embedding.transformation_contextual import (
+            VoyageContextualEmbeddingConfig,
+            VoyageError,
+        )
+        from litellm.types.utils import EmbeddingResponse
+
+        config = VoyageContextualEmbeddingConfig()
+        raw_response = MagicMock()
+        raw_response.json.side_effect = ValueError("not json")
+        raw_response.status_code = 400
+        raw_response.text = "bad request"
+
+        with pytest.raises(VoyageError) as exc_info:
+            config.transform_embedding_response(
+                "voyage-context-4", raw_response, EmbeddingResponse(), MagicMock()
+            )
+        assert exc_info.value.status_code == 400
+        assert exc_info.value.message == "bad request"
+
+        error = config.get_error_class("rate limited", 429, {"x-test": "1"})
+        assert isinstance(error, VoyageError)
+        assert error.status_code == 429
+        assert error.message == "rate limited"