diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index b37ff865c65..8369bc3a6a2 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -2160,7 +2160,7 @@ def batch_cost_calculator(
     output_cost_per_token: Final = model_info.get("output_cost_per_token")
     total_prompt_cost = 0.0
     total_completion_cost = 0.0
-    if input_cost_per_token_batches:
+    if input_cost_per_token_batches is not None:
         total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches
     elif input_cost_per_token:
         details: Final = parse_prompt_tokens_details(usage)
@@ -2180,7 +2180,7 @@ def batch_cost_calculator(
 
         cache_creation_cost: Final = model_info.get("cache_creation_input_token_cost") or input_cost_per_token
         total_prompt_cost += cache_creation_tokens * cache_creation_cost / 2
-    if output_cost_per_token_batches:
+    if output_cost_per_token_batches is not None:
         total_completion_cost = usage.completion_tokens * output_cost_per_token_batches
     elif output_cost_per_token:
         total_completion_cost = (
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index 4acbe276cf5..8e1d4cb877f 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -437,6 +437,19 @@ class TestGetRouterDeploymentModelInfo:
         finally:
             litellm.model_cost.pop(deployment_id, None)
 
+    def test_returns_none_when_the_deployment_id_resolves_no_provider(self, logging_obj) -> None:
+        """A registration whose id get_model_info cannot resolve yields no pricing."""
+        deployment_id = "deploy-unresolvable-provider-1"
+        litellm.model_cost[deployment_id] = {"id": deployment_id, "input_cost_per_token": 4e-06}
+        logging_obj.litellm_params = {"litellm_metadata": {"model_info": {"id": deployment_id}}}
+        logging_obj.model_call_details["model"] = None
+        logging_obj.model = None
+        try:
+            with patch.object(litellm, "get_model_info", side_effect=Exception("unresolvable")):
+                assert logging_obj.get_router_deployment_model_info() is None
+        finally:
+            litellm.model_cost.pop(deployment_id, None)
+
     def test_falls_back_to_declared_rates_when_the_model_has_no_published_entry(self, logging_obj) -> None:
         """With no published entry to layer under, the declared rates still apply."""
         deployment_id = "deploy-unpublished-model-1"
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index a51f4e733b6..850d860b9d2 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -3595,6 +3595,43 @@ def test_batch_cost_calculator_cache_creation_falls_back_to_input_rate():
     assert prompt_cost == pytest.approx((1000 * 3e-6 + 8000 * 3e-7 + 2000 * 3e-6) / 2)
 
 
+@pytest.mark.parametrize(
+    "batch_rate,expected_prompt,expected_completion",
+    [
+        (0.0, 0.0, 0.0),
+        (1e-6, 1000 * 1e-6, 500 * 1e-6),
+        (None, 1000 * 3e-6 / 2, 500 * 15e-6 / 2),
+    ],
+    ids=["explicit-zero", "explicit-nonzero", "unset"],
+)
+def test_batch_cost_calculator_honors_an_explicitly_zero_batch_rate(
+    batch_rate: float | None,
+    expected_prompt: float,
+    expected_completion: float,
+) -> None:
+    """A batch rate configured as 0.0 means free, not unset.
+
+    Gating the batch fields on truthiness read an explicit 0.0 as absent and
+    charged half the standard rate for that token direction instead.
+    """
+    from litellm.cost_calculator import batch_cost_calculator
+
+    model_info: dict[str, float] = {"input_cost_per_token": 3e-6, "output_cost_per_token": 15e-6}
+    if batch_rate is not None:
+        model_info["input_cost_per_token_batches"] = batch_rate
+        model_info["output_cost_per_token_batches"] = batch_rate
+
+    prompt_cost, completion_cost_value = batch_cost_calculator(
+        usage=Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500),
+        model="claude-sonnet-4-5-20250929",
+        custom_llm_provider="anthropic",
+        model_info=model_info,  # type: ignore[arg-type]
+    )
+
+    assert prompt_cost == pytest.approx(expected_prompt)
+    assert completion_cost_value == pytest.approx(expected_completion)
+
+
 def test_combine_usage_objects_sums_mirrored_cache_write_fields_once():
     """
     cache_write_tokens and cache_creation_tokens mirror each other on