Merge ebeb782cd6 into df5f6813db

enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py

@@ -338,7 +338,7 @@ class CheckBatchCost:
 
                 # Pass deployment model_info so custom batch pricing
                 # (input_cost_per_token_batches etc.) is used for cost calc
-                deployment_model_info = deployment_info.model_info.model_dump() if deployment_info.model_info else {}
+                deployment_model_info = deployment_info.model_info.model_dump() if deployment_info.model_info else None
                 batch_cost, batch_usage, batch_models = (
                     await calculate_batch_cost_and_usage(
                         file_content_dictionary=file_content_as_dict,

litellm/batches/batch_utils.py

@@ -25,6 +25,17 @@ async def calculate_batch_cost_and_usage(
             deployment-specific pricing (e.g. input_cost_per_token_batches)
             is used instead of the global cost map.
     """
+    # Translate Anthropic JSONL format to OpenAI format so the shared
+    # cost/usage helpers work uniformly across providers.
+    # Anthropic uses {result: {type, message: {usage: {input_tokens, output_tokens}}}}
+    # but helpers expect {response: {status_code, body: {usage: {prompt_tokens, ...}}}}
+    # Ref: https://github.com/BerriAI/litellm/issues/27944
+    if custom_llm_provider == "anthropic":
+        file_content_dictionary = [
+            _translate_anthropic_batch_item_to_openai(item)
+            for item in file_content_dictionary
+        ]
+
     batch_cost = _batch_cost_calculator(
         custom_llm_provider=custom_llm_provider,
         file_content_dictionary=file_content_dictionary,
@@ -515,6 +526,51 @@ def _get_response_from_batch_job_output_file(batch_job_output_file: dict) -> Any
     return _response_body
 
 
+def _translate_anthropic_batch_item_to_openai(item: dict) -> dict:
+    """
+    Translate a single Anthropic batch result JSONL item to OpenAI format
+    so the shared cost/usage helpers can process it uniformly.
+
+    Anthropic format:
+        {"custom_id": "x", "result": {"type": "succeeded",
+         "message": {"model": "claude-...",
+                       "usage": {"input_tokens": N, "output_tokens": M}}}}
+
+    OpenAI format expected by _get_response_from_batch_job_output_file:
+        {"custom_id": "x", "response": {"status_code": 200,
+         "body": {"model": "claude-...",
+                    "usage": {"prompt_tokens": N, "completion_tokens": M,
+                                "total_tokens": N+M}}}}
+
+    Ref: https://github.com/BerriAI/litellm/issues/27944
+    """
+    result = item.get("result", {})
+    if result.get("type") != "succeeded":
+        # failed/errored items return as-is so _batch_response_was_successful
+        # correctly treats them as non-200
+        return item
+
+    message = result.get("message", {})
+    anthropic_usage = message.get("usage", {})
+    prompt_tokens = anthropic_usage.get("input_tokens", 0)
+    completion_tokens = anthropic_usage.get("output_tokens", 0)
+
+    return {
+        "custom_id": item.get("custom_id"),
+        "response": {
+            "status_code": 200,
+            "body": {
+                "model": message.get("model", ""),
+                "usage": {
+                    "prompt_tokens": prompt_tokens,
+                    "completion_tokens": completion_tokens,
+                    "total_tokens": prompt_tokens + completion_tokens,
+                },
+            },
+        },
+    }
+
+
 def _batch_response_was_successful(batch_job_output_file: dict) -> bool:
     """
     Check if the batch job response status == 200

litellm/llms/anthropic/files/transformation.py

@@ -271,6 +271,13 @@ class AnthropicFilesConfig(BaseFilesConfig):
             AnthropicModelInfo.get_api_base(litellm_params.get("api_base"))
             or ANTHROPIC_FILES_API_BASE
         )
+        if file_id and file_id.startswith("msgbatch_"):
+            encoded_batch_id = encode_url_path_segment(file_id, field_name="file_id")
+            return (
+                f"{api_base.rstrip('/')}/v1/messages/batches/{encoded_batch_id}/results",
+                {},
+            )
+
         encoded_file_id = encode_url_path_segment(file_id, field_name="file_id")
         return f"{api_base.rstrip('/')}/v1/files/{encoded_file_id}/content", {}
 

tests/test_litellm/llms/anthropic/files/test_anthropic_files_transformation.py

@@ -317,6 +317,29 @@ class TestAnthropicFilesConfig:
                 litellm_params={},
             )
 
+    def test_transform_file_content_request_msgbatch_routes_to_batch_endpoint(self):
+        url, params = self.config.transform_file_content_request(
+            file_content_request={"file_id": "msgbatch_abc123"},
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == f"{ANTHROPIC_FILES_API_BASE}/v1/messages/batches/msgbatch_abc123/results"
+        assert params == {}
+
+    def test_transform_file_content_request_msgbatch_encodes_path_traversal(self):
+        url, params = self.config.transform_file_content_request(
+            file_content_request={
+                "file_id": "msgbatch_x/../../../v1/messages/batches?limit=1#frag"
+            },
+            optional_params={},
+            litellm_params={},
+        )
+        assert (
+            url
+            == f"{ANTHROPIC_FILES_API_BASE}/v1/messages/batches/msgbatch_x%2F..%2F..%2F..%2Fv1%2Fmessages%2Fbatches%3Flimit%3D1%23frag/results"
+        )
+        assert params == {}
+
     def test_transform_file_content_response(self):
         mock_response = Mock(spec=httpx.Response)
         result = self.config.transform_file_content_response(

tests/test_litellm/llms/anthropic/test_anthropic_batch_translation.py

@@ -0,0 +1,51 @@
+"""
+Tests for Anthropic batch JSONL translation.
+Ref: https://github.com/BerriAI/litellm/issues/27944
+"""
+import pytest
+from litellm.batches.batch_utils import _translate_anthropic_batch_item_to_openai
+
+
+def test_translate_succeeded_item():
+    item = {
+        "custom_id": "req-1",
+        "result": {
+            "type": "succeeded",
+            "message": {
+                "model": "claude-opus-4-7",
+                "usage": {"input_tokens": 100, "output_tokens": 50},
+            },
+        },
+    }
+    result = _translate_anthropic_batch_item_to_openai(item)
+    assert result["response"]["status_code"] == 200
+    body = result["response"]["body"]
+    assert body["model"] == "claude-opus-4-7"
+    assert body["usage"]["prompt_tokens"] == 100
+    assert body["usage"]["completion_tokens"] == 50
+    assert body["usage"]["total_tokens"] == 150
+    assert result["custom_id"] == "req-1"
+
+
+def test_translate_failed_item_passes_through():
+    item = {
+        "custom_id": "req-2",
+        "result": {"type": "errored", "error": {"type": "server_error"}},
+    }
+    result = _translate_anthropic_batch_item_to_openai(item)
+    # failed items pass through unchanged; no response.status_code == 200
+    assert result == item
+
+
+def test_translate_zero_usage():
+    item = {
+        "custom_id": "req-3",
+        "result": {
+            "type": "succeeded",
+            "message": {"model": "claude-haiku-4-5", "usage": {}},
+        },
+    }
+    result = _translate_anthropic_batch_item_to_openai(item)
+    assert result["response"]["body"]["usage"]["prompt_tokens"] == 0
+    assert result["response"]["body"]["usage"]["completion_tokens"] == 0
+    assert result["response"]["body"]["usage"]["total_tokens"] == 0