fix(batches): only emit line items for final batches

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/litellm_core_utils/litellm_logging.py

@@ -3027,7 +3027,7 @@ class Logging(LiteLLMLoggingBaseClass):
                     cost_for_built_in_tools_cost_usd_dollar=0.0,
                 )
 
-            if litellm.store_batch_line_items_in_callbacks:
+            if litellm.store_batch_line_items_in_callbacks and (has_explicit_batch_data or should_compute_batch_data):
                 from litellm.batches.batch_line_item_logging import log_batch_line_items
 
                 await log_batch_line_items(

litellm/llms/bedrock/common_utils.py

@@ -34,6 +34,7 @@ if TYPE_CHECKING:
 
 
 _ERROR_REQUEST_URL: Final = "https://docs.litellm.ai/docs"
+_OPENAI_FAMILY_MODEL_RE: Final = re.compile(r"(^|[./])openai\.")
 
 
 def error_response_text(response: httpx.Response) -> str:
@@ -878,9 +879,10 @@ def bedrock_model_accepts_cache_points(model: str | None) -> bool:
     """
     Whether Converse ``cachePoint`` blocks may be sent to this model.
 
-    Bedrock rejects requests carrying cachePoint blocks for models without prompt
-    caching support ("You invoked an unsupported model or your request did not allow
-    prompt caching"), so a model whose cost-map entry does not declare
+    OpenAI-family models only support implicit caching and never accept explicit
+    ``cachePoint`` blocks. Bedrock rejects requests carrying cachePoint blocks for
+    models without prompt caching support ("You invoked an unsupported model or your
+    request did not allow prompt caching"), so a model whose cost-map entry does not declare
     ``supports_prompt_caching`` must not receive them. A model absent from the map
     (an application inference profile ARN, a model newer than the map) keeps emitting
     so existing caching setups never silently degrade. ``litellm.utils.supports_prompt_caching``
@@ -888,6 +890,8 @@ def bedrock_model_accepts_cache_points(model: str | None) -> bool:
     """
     if model is None:
         return True
+    if _OPENAI_FAMILY_MODEL_RE.search(model):
+        return False
     entries: Final = tuple(
         entry
         for candidate in (model, get_bedrock_base_model(model))

tests/test_litellm/batches/test_batch_line_item_logging.py

@@ -15,6 +15,7 @@ import json
 import uuid
 from datetime import datetime
 from types import SimpleNamespace
+from typing import Final
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -222,6 +223,29 @@ async def test_flag_off_emits_only_aggregate(recorder):
     file_mock.assert_not_called()
 
 
+@pytest.mark.asyncio
+async def test_in_progress_batch_poll_emits_no_line_events(recorder):
+    litellm.store_batch_line_items_in_callbacks = True  # test-quality-ok: the flag under test is a module global; fixture restores it
+    in_progress: Final = LiteLLMBatch(
+        id="batch_wip",
+        object="batch",
+        endpoint="/v1/chat/completions",
+        input_file_id="input-file-1",
+        output_file_id=None,
+        error_file_id=None,
+        status="in_progress",
+        completion_window="24h",
+        created_at=1,
+    )
+    file_mock: Final = AsyncMock(side_effect=_file_content)
+    with patch("litellm.files.main.afile_content", file_mock):  # test-quality-ok: afile_content is the provider boundary; no injection seam for managed file fetch
+        await _parent_logging().async_success_handler(result=in_progress)
+
+    file_mock.assert_not_called()
+    assert all(_hidden(e).get("batch_custom_id") is None for e in recorder.success_events)
+    assert len(recorder.failure_events) == 0
+
+
 @pytest.mark.asyncio
 async def test_input_fetch_failure_still_emits_aggregate(recorder):
     litellm.store_batch_line_items_in_callbacks = True  # test-quality-ok: the flag under test is a module global; fixture restores it

tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py

@@ -1189,17 +1189,24 @@ def test_get_supported_openai_params_bedrock_converse():
 
 
 @pytest.mark.parametrize(
-    "tools, expected_marker",
+    "tools, model, expected_marker",
     [
         pytest.param(
             [{"type": "function", "function": {"name": "f", "parameters": {"type": "object", "properties": {}}}}],
+            "anthropic.claude-sonnet-4-5-20250929-v1:0",
             "dep-bedrock",
             id="tools-present-so-the-cachepoint-is-placed",
         ),
-        pytest.param(None, None, id="no-tools-so-nothing-is-placed"),
+        pytest.param(None, "anthropic.claude-sonnet-4-5-20250929-v1:0", None, id="no-tools-so-nothing-is-placed"),
+        pytest.param(
+            [{"type": "function", "function": {"name": "f", "parameters": {"type": "object", "properties": {}}}}],
+            "global.openai.gpt-6-astra",
+            None,
+            id="openai-family-implicit-caching-only",
+        ),
     ],
 )
-def test_tool_config_cachepoint_is_credited_only_where_it_is_placed(tools, expected_marker):
+def test_tool_config_cachepoint_is_credited_only_where_it_is_placed(tools, model, expected_marker):
     """Spend attribution credits the gateway for breakpoints it placed, and a tool_config
     point becomes one here or nowhere.
 
@@ -1213,7 +1220,7 @@ def test_tool_config_cachepoint_is_credited_only_where_it_is_placed(tools, expec
         optional_params["tools"] = tools
 
     data = AmazonConverseConfig()._transform_request_helper(
-        model="anthropic.claude-sonnet-4-5-20250929-v1:0",
+        model=model,
         system_content_blocks=[],
         optional_params=optional_params,
         messages=[{"role": "user", "content": "hi"}],
@@ -5591,6 +5598,9 @@ def test_cache_control_injection_tool_config_drops_ttl_for_unsupported_model():
             True,
             id="unmapped-arn-keeps-emitting",
         ),
+        pytest.param("global.openai.gpt-6-astra", False, id="openai-family-implicit-caching-only"),
+        pytest.param("openai.gpt-oss-120b-1:0", False, id="openai-gpt-oss"),
+        pytest.param("us.openai.gpt-99-unmapped", False, id="unmapped-openai-family-still-suppressed"),
     ],
 )
 def test_cache_points_emitted_only_for_models_that_support_prompt_caching(model, expects_cache_points, monkeypatch):