From 6004f53d1ba2a80b6418a99aa7a6681881708e22 Mon Sep 17 00:00:00 2001
From: Praveen11558 <44603409+Praveen11558@users.noreply.github.com>
Date: Mon, 23 Mar 2026 00:25:10 +0530
Subject: [PATCH] Update test_llm_cost_calc_utils.py

---
 .../llm_cost_calc/test_llm_cost_calc_utils.py | 87 +------------------
 1 file changed, 1 insertion(+), 86 deletions(-)

diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index caebd2d3c57..7cb71d044fd 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -1452,92 +1452,7 @@ def test_image_count_billing_does_not_fill_prompt_token_gap():
         f"Gap should not be filled when image_count billing is active."
     )
     assert completion_cost == pytest.approx(expected_completion_cost)
- 
- 
-def test_character_count_billing_does_not_fill_prompt_token_gap():
-    """
-    Regression: when character_count pricing is active, gaps between
-    accounted token details and prompt_tokens should NOT be converted to
-    text_tokens, otherwise character-based providers may be over-billed.
-    """
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
- 
-    usage = Usage(
-        prompt_tokens=200,
-        completion_tokens=20,
-        total_tokens=220,
-        prompt_tokens_details=PromptTokensDetailsWrapper(
-            text_tokens=100,
-            character_count=1000,
-            image_tokens=0,
-            audio_tokens=0,
-            cached_tokens=0,
-        ),
-    )
- 
-    prompt_cost, completion_cost = generic_cost_per_token(
-        model="gemini-1.0-pro",
-        usage=usage,
-        custom_llm_provider="vertex_ai",
-    )
- 
-    model_info = litellm.model_cost["gemini-1.0-pro"]
-    expected_prompt_cost = (
-        100 * model_info["input_cost_per_token"]
-        + 1000 * model_info["input_cost_per_character"]
-    )
-    expected_completion_cost = 20 * model_info["output_cost_per_token"]
- 
-    assert prompt_cost == pytest.approx(expected_prompt_cost), (
-        f"Expected prompt_cost={expected_prompt_cost}, got {prompt_cost}. "
-        f"character_count-based requests should not fill token gaps as text."
-    )
-    assert completion_cost == pytest.approx(expected_completion_cost)
- 
- 
-def test_video_length_billing_does_not_fill_prompt_token_gap():
-    """
-    Regression: when video_length_seconds pricing is active, gaps between
-    accounted token details and prompt_tokens should NOT be converted to
-    text_tokens, otherwise video-based providers may be over-billed.
-    """
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
- 
-    usage = Usage(
-        prompt_tokens=150,
-        completion_tokens=10,
-        total_tokens=160,
-        prompt_tokens_details=PromptTokensDetailsWrapper(
-            text_tokens=50,
-            video_length_seconds=12.0,
-            image_tokens=0,
-            audio_tokens=0,
-            cached_tokens=0,
-        ),
-    )
- 
-    prompt_cost, completion_cost = generic_cost_per_token(
-        model="gemini-1.0-pro",
-        usage=usage,
-        custom_llm_provider="vertex_ai",
-    )
- 
-    model_info = litellm.model_cost["gemini-1.0-pro"]
-    expected_prompt_cost = (
-        50 * model_info["input_cost_per_token"]
-        + 12.0 * model_info["input_cost_per_video_per_second"]
-    )
-    expected_completion_cost = 10 * model_info["output_cost_per_token"]
- 
-    assert prompt_cost == pytest.approx(expected_prompt_cost), (
-        f"Expected prompt_cost={expected_prompt_cost}, got {prompt_cost}. "
-        f"video_length_seconds-based requests should not fill token gaps as text."
-    )
-    assert completion_cost == pytest.approx(expected_completion_cost)
- 
- 
+  
 def test_negative_text_tokens_clamped_to_zero():
     """
     Scenario: Malformed provider response where cached_tokens > prompt_tokens.