diff --git a/docs/my-website/docs/sdk_custom_pricing.md b/docs/my-website/docs/sdk_custom_pricing.md
index ac956db472..c857711510 100644
--- a/docs/my-website/docs/sdk_custom_pricing.md
+++ b/docs/my-website/docs/sdk_custom_pricing.md
@@ -2,10 +2,7 @@
 
 Register custom pricing for sagemaker completion model. 
 
-For cost per second pricing, register `input_cost_per_second`. If your provider
-charges for audio output duration (e.g., TTS), also set `output_cost_per_second`.
-Values of `0` are treated as not billable, so `output_cost_per_second: 0` will
-not override `input_cost_per_second`.
+For cost per second pricing, you **just** need to register `input_cost_per_second`. 
 
 ```python
 # !pip install boto3 
diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py
index 339ce9caed..e5349db3af 100644
--- a/litellm/llms/openai/cost_calculation.py
+++ b/litellm/llms/openai/cost_calculation.py
@@ -105,21 +105,25 @@ def cost_per_second(
     prompt_cost = 0.0
     completion_cost = 0.0
     ## Speech / Audio cost calculation
-    output_cost_per_second = model_info.get("output_cost_per_second")
-    input_cost_per_second = model_info.get("input_cost_per_second")
-
-    if output_cost_per_second is not None and output_cost_per_second > 0:
+    if (
+        "output_cost_per_second" in model_info
+        and model_info["output_cost_per_second"] is not None
+    ):
         verbose_logger.debug(
-            f"For model={model} - output_cost_per_second: {output_cost_per_second}; duration: {duration}"
+            f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; duration: {duration}"
         )
         ## COST PER SECOND ##
-        completion_cost = output_cost_per_second * duration
-    if input_cost_per_second is not None and input_cost_per_second > 0:
+        completion_cost = model_info["output_cost_per_second"] * duration
+    elif (
+        "input_cost_per_second" in model_info
+        and model_info["input_cost_per_second"] is not None
+    ):
         verbose_logger.debug(
-            f"For model={model} - input_cost_per_second: {input_cost_per_second}; duration: {duration}"
+            f"For model={model} - input_cost_per_second: {model_info.get('input_cost_per_second')}; duration: {duration}"
         )
         ## COST PER SECOND ##
-        prompt_cost = input_cost_per_second * duration
+        prompt_cost = model_info["input_cost_per_second"] * duration
+        completion_cost = 0.0
 
     return prompt_cost, completion_cost
 
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 42bcfca231..4d6599fc1b 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -192,40 +192,6 @@ def test_transcription_cost_falls_back_to_duration():
     assert pytest.approx(cost, rel=1e-6) == expected_cost
 
 
-def test_transcription_cost_prefers_input_when_output_zero(monkeypatch):
-    from litellm import completion_cost
-
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
-
-    model_name = "custom-whisper-input-only"
-    custom_model_info = {
-        "input_cost_per_second": 0.00005,
-        "output_cost_per_second": 0.0,
-        "litellm_provider": "openai",
-        "mode": "audio_transcription",
-        "supported_endpoints": ["/v1/audio/transcriptions"],
-    }
-    monkeypatch.setattr(
-        litellm,
-        "model_cost",
-        {**litellm.model_cost, model_name: custom_model_info},
-    )
-
-    response = TranscriptionResponse(text="demo text")
-    response.duration = 300.0
-
-    cost = completion_cost(
-        completion_response=response,
-        model=model_name,
-        custom_llm_provider="openai",
-        call_type="atranscription",
-    )
-
-    expected_cost = 300.0 * 0.00005
-    assert pytest.approx(cost, rel=1e-6) == expected_cost
-
-
 def test_handle_realtime_stream_cost_calculation():
     from litellm.cost_calculator import RealtimeAPITokenUsageProcessor