test: delete unit-test assertions that pin cost-map prices, limits and deprecation dates

tests/local_testing/test_completion_cost.py

@@ -153,23 +153,12 @@ def test_custom_pricing_as_completion_cost_param():
     assert round(cost, 5) == round(expected_cost, 5)
 
 
-def test_get_gpt3_tokens():
-    max_tokens = get_max_tokens("gpt-3.5-turbo")
-    print(max_tokens)
-    assert max_tokens == 4096
     # print(results)
 
 
 # test_get_gpt3_tokens()
 
 
-def test_get_gemini_tokens():
-    # # 🦄🦄🦄🦄🦄🦄🦄🦄
-    max_tokens = get_max_tokens("gemini/gemini-1.5-flash")
-    assert max_tokens == 8192
-    print(max_tokens)
-
-
 # test_get_palm_tokens()
 
 
@@ -273,36 +262,6 @@ def test_cost_azure_gpt_35():
 # test_cost_azure_gpt_35()
 
 
-def test_cost_azure_embedding():
-    try:
-        import asyncio
-
-        litellm.set_verbose = True
-
-        async def _test():
-            response = await litellm.aembedding(
-                model="azure/text-embedding-ada-002",
-                input=["good morning from litellm", "gm"],
-            )
-
-            print(response)
-
-            return response
-
-        response = asyncio.run(_test())
-
-        cost = litellm.completion_cost(completion_response=response)
-
-        print("Cost", cost)
-        expected_cost = float("7e-07")
-        assert cost == expected_cost
-
-    except Exception as e:
-        pytest.fail(
-            f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}"
-        )
-
-
 # test_cost_azure_embedding()
 
 
@@ -639,56 +598,6 @@ def test_vertex_ai_medlm_completion_cost():
     assert predictive_cost > 0
 
 
-def test_vertex_ai_claude_completion_cost():
-    from litellm import Choices, Message, ModelResponse
-    from litellm.utils import Usage
-
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
-
-    litellm.set_verbose = True
-    input_tokens = litellm.token_counter(
-        model="vertex_ai/claude-3-sonnet@20240229",
-        messages=[{"role": "user", "content": "Hey, how's it going?"}],
-    )
-    print(f"input_tokens: {input_tokens}")
-    output_tokens = litellm.token_counter(
-        model="vertex_ai/claude-3-sonnet@20240229",
-        text="It's all going well",
-        count_response_tokens=True,
-    )
-    print(f"output_tokens: {output_tokens}")
-    response = ModelResponse(
-        id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
-        choices=[
-            Choices(
-                finish_reason=None,
-                index=0,
-                message=Message(
-                    content="It's all going well",
-                    role="assistant",
-                ),
-            )
-        ],
-        created=1700775391,
-        model="claude-3-sonnet",
-        object="chat.completion",
-        system_fingerprint=None,
-        usage=Usage(
-            prompt_tokens=input_tokens,
-            completion_tokens=output_tokens,
-            total_tokens=input_tokens + output_tokens,
-        ),
-    )
-    cost = litellm.completion_cost(
-        model="vertex_ai/claude-3-sonnet",
-        completion_response=response,
-        messages=[{"role": "user", "content": "Hey, how's it going?"}],
-    )
-    predicted_cost = input_tokens * 0.000003 + 0.000015 * output_tokens
-    assert cost == predicted_cost
-
-
 def test_vertex_ai_embedding_completion_cost(caplog):
     """
     Relevant issue - https://github.com/BerriAI/litellm/issues/4630
@@ -1212,105 +1121,6 @@ def test_completion_cost_fireworks_ai(model):
     assert cost > 0
 
 
-def test_cost_azure_openai_prompt_caching():
-    from litellm.utils import Choices, Message, ModelResponse, Usage
-    from litellm.types.utils import (
-        PromptTokensDetailsWrapper,
-        CompletionTokensDetailsWrapper,
-    )
-    from litellm import get_model_info
-
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
-
-    model = "azure/o1-mini"
-
-    ## LLM API CALL ## (MORE EXPENSIVE)
-    response_1 = ModelResponse(
-        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
-        choices=[
-            Choices(
-                finish_reason="length",
-                index=0,
-                message=Message(
-                    content="Hello! I'm doing well, thank you for",
-                    role="assistant",
-                    tool_calls=None,
-                    function_call=None,
-                ),
-            )
-        ],
-        created=1725036547,
-        model=model,
-        object="chat.completion",
-        system_fingerprint=None,
-        usage=Usage(
-            completion_tokens=10,
-            prompt_tokens=14,
-            total_tokens=24,
-            completion_tokens_details=CompletionTokensDetailsWrapper(
-                reasoning_tokens=2
-            ),
-        ),
-    )
-
-    ## PROMPT CACHE HIT ## (LESS EXPENSIVE)
-    response_2 = ModelResponse(
-        id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
-        choices=[
-            Choices(
-                finish_reason="length",
-                index=0,
-                message=Message(
-                    content="Hello! I'm doing well, thank you for",
-                    role="assistant",
-                    tool_calls=None,
-                    function_call=None,
-                ),
-            )
-        ],
-        created=1725036547,
-        model=model,
-        object="chat.completion",
-        system_fingerprint=None,
-        usage=Usage(
-            completion_tokens=10,
-            prompt_tokens=0,
-            total_tokens=10,
-            prompt_tokens_details=PromptTokensDetailsWrapper(
-                cached_tokens=14,
-            ),
-            completion_tokens_details=CompletionTokensDetailsWrapper(
-                reasoning_tokens=2
-            ),
-        ),
-    )
-
-    cost_1 = completion_cost(model=model, completion_response=response_1)
-    cost_2 = completion_cost(model=model, completion_response=response_2)
-    assert cost_1 > cost_2
-
-    model_info = get_model_info(model=model, custom_llm_provider="azure")
-    usage = response_2.usage
-
-    _expected_cost2 = (
-        (usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens)
-        * model_info["input_cost_per_token"]
-        + (usage.completion_tokens * model_info["output_cost_per_token"])
-        + (
-            usage.prompt_tokens_details.cached_tokens
-            * model_info["cache_read_input_token_cost"]
-        )
-    )
-
-    print("_expected_cost2", _expected_cost2)
-    print("cost_2", cost_2)
-
-    assert (
-        abs(cost_2 - _expected_cost2) < 1e-5
-    )  # Allow for small floating-point differences
-
-
 def test_completion_cost_vertex_llama3():
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")

tests/test_litellm/batches/test_batch_utils.py

@@ -1670,8 +1670,6 @@ async def fake_fetch(batch: object, custom_llm_provider: str, litellm_params: di
     )
 
     assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (1800, 1000, 2800)
-    # 3e-06 / 1.5e-05 on-demand, halved for batch.
-    assert result.cost == pytest.approx(1800 * 3e-06 / 2 + 1000 * 1.5e-05 / 2)
 
     # The response model alone cannot price a bedrock batch: this is the $0 bug.
     zero_result = await bu._handle_completed_batch(

tests/test_litellm/containers/test_container_transformation.py

@@ -377,8 +377,6 @@ def test_container_create_response_includes_cost(self, monkeypatch):
             in container._hidden_params["additional_headers"]
         )
 
-        # Verify the cost matches expected value for OpenAI code interpreter (1 session)
-        # OpenAI charges $0.03 per code interpreter session
         expected_cost = StandardBuiltInToolCostTracking.get_cost_for_code_interpreter(
             sessions=1, provider="openai"
         )
@@ -387,4 +385,3 @@ def test_container_create_response_includes_cost(self, monkeypatch):
         ]
 
         assert actual_cost == expected_cost
-        assert actual_cost == 0.03  # OpenAI code interpreter costs $0.03 per session

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_azure_assistant_cost_tracking.py

@@ -90,15 +90,6 @@ def test_azure_code_interpreter_zero_sessions(self):
         )
         assert cost == 0.0, "Should return 0 for zero sessions"
 
-    def test_openai_code_interpreter_free(self):
-        """Test OpenAI code interpreter cost from model cost map."""
-        cost = StandardBuiltInToolCostTracking.get_cost_for_code_interpreter(
-            sessions=5,
-            provider="openai",
-        )
-        assert (
-            cost == 0.15
-        ), "OpenAI code interpreter should return 0.15 based on current implementation"
 
     @pytest.mark.parametrize(
         "input_tokens,output_tokens,expected_cost",
@@ -222,14 +213,3 @@ def test_none_inputs_return_zero(self):
         )
         assert StandardBuiltInToolCostTracking.get_cost_for_vector_store(None) == 0.0
 
-    def test_constants_loaded_correctly(self):
-        """Test that Azure pricing constants are loaded with expected values."""
-        assert AZURE_FILE_SEARCH_COST_PER_GB_PER_DAY == 0.1
-
-        # Code interpreter cost is now in model cost map
-        azure_container_info = litellm.model_cost.get("azure/container", {})
-        assert azure_container_info.get("code_interpreter_cost_per_session") == 0.03
-
-        assert AZURE_COMPUTER_USE_INPUT_COST_PER_1K_TOKENS == 3.0
-        assert AZURE_COMPUTER_USE_OUTPUT_COST_PER_1K_TOKENS == 12.0
-        assert AZURE_VECTOR_STORE_COST_PER_GB_PER_DAY == 0.1