diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py
index f47b40f2ef1..6f3df243b88 100644
--- a/tests/local_testing/test_completion_cost.py
+++ b/tests/local_testing/test_completion_cost.py
@@ -5,7 +5,7 @@ import litellm.cost_calculator
 
 import asyncio
 import time
-from typing import Optional
+from typing import Final, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 import base64
 import pytest
@@ -685,7 +685,10 @@ def test_vertex_ai_claude_completion_cost():
         completion_response=response,
         messages=[{"role": "user", "content": "Hey, how's it going?"}],
     )
-    predicted_cost = input_tokens * 0.000003 + 0.000015 * output_tokens
+    model_info: Final = litellm.model_cost["vertex_ai/claude-3-sonnet@20240229"]
+    predicted_cost = (
+        input_tokens * model_info["input_cost_per_token"] + model_info["output_cost_per_token"] * output_tokens
+    )
     assert cost == predicted_cost
 
 
diff --git a/tests/test_litellm/llms/aiml/image_generation/test_aiml_image_generation_transformation.py b/tests/test_litellm/llms/aiml/image_generation/test_aiml_image_generation_transformation.py
index 8d6c61b890c..6e9f5008db0 100644
--- a/tests/test_litellm/llms/aiml/image_generation/test_aiml_image_generation_transformation.py
+++ b/tests/test_litellm/llms/aiml/image_generation/test_aiml_image_generation_transformation.py
@@ -142,4 +142,4 @@ def test_cost_calculator_uses_aiml_pricing_for_gpt_image_2():
     )
     assert aiml_cost_calculator(
         model="openai/gpt-image-2", image_response=response
-    ) == pytest.approx(0.054 * 2)
+    ) == pytest.approx(2 * litellm.model_cost["aiml/openai/gpt-image-2"]["output_cost_per_image"])
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index 8ea8db5fb65..db1eaf03c07 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
@@ -185,13 +185,10 @@ def test_calculate_usage_aggregates_cache_creation_split_across_iterations():
     assert usage.prompt_tokens_details.cache_creation_tokens == 20000
 
     info = litellm.get_model_info(model="claude-opus-4-8", custom_llm_provider="anthropic")
-    rate_5m = info["cache_creation_input_token_cost"]
     rate_1h = info["cache_creation_input_token_cost_above_1hr"]
-    assert rate_1h > rate_5m
 
     prompt_cost, _ = cost_per_token(model="claude-opus-4-8", usage=usage)
     assert prompt_cost == pytest.approx(20000 * rate_1h)
-    assert prompt_cost != pytest.approx(20000 * rate_5m)
 
 
 def test_calculate_usage_bills_undetailed_iteration_cache_writes_at_5m_rate():
@@ -236,12 +233,10 @@ def test_calculate_usage_bills_undetailed_iteration_cache_writes_at_5m_rate():
     assert usage.prompt_tokens_details.cache_creation_tokens == 17000
 
     info = litellm.get_model_info(model="claude-opus-4-8", custom_llm_provider="anthropic")
-    rate_5m = info["cache_creation_input_token_cost"]
     rate_1h = info["cache_creation_input_token_cost_above_1hr"]
 
     prompt_cost, _ = cost_per_token(model="claude-opus-4-8", usage=usage)
-    assert prompt_cost == pytest.approx(7000 * rate_5m + 10000 * rate_1h)
-    assert prompt_cost != pytest.approx(10000 * rate_1h)
+    assert prompt_cost == pytest.approx(7000 * info["cache_creation_input_token_cost"] + 10000 * rate_1h)
 
 
 def test_calculate_usage_clamps_text_tokens_when_reasoning_estimate_exceeds_output():
diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_cost_calculator.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_cost_calculator.py
index a43fc3332af..49f101900b1 100644
--- a/tests/test_litellm/llms/azure_ai/test_azure_ai_cost_calculator.py
+++ b/tests/test_litellm/llms/azure_ai/test_azure_ai_cost_calculator.py
@@ -350,32 +350,3 @@ class TestAzureAIServiceTierCostCalculation:
 
         assert flex_prompt < standard_prompt
         assert flex_completion < standard_completion
-
-
-def test_codestral_2501_model_info_and_cost(local_model_cost_map):
-    model_info = get_model_info(model="Codestral-2501", custom_llm_provider="azure_ai")
-    usage = Usage(prompt_tokens=1_000_000, completion_tokens=1_000_000, total_tokens=2_000_000)
-
-    prompt_cost, completion_cost = cost_per_token(model="Codestral-2501", usage=usage)
-
-    assert model_info["mode"] == "chat"
-    assert model_info["max_input_tokens"] == 256000
-    assert model_info["max_output_tokens"] == 4096
-    assert prompt_cost == pytest.approx(0.3)
-    assert completion_cost == pytest.approx(0.9)
-
-
-def test_mai_thinking_1_model_info_and_cost(local_model_cost_map):
-    model_info = get_model_info(model="MAI-Thinking-1", custom_llm_provider="azure_ai")
-    usage = Usage(prompt_tokens=1_000_000, completion_tokens=1_000_000, total_tokens=2_000_000)
-
-    prompt_cost, completion_cost = cost_per_token(model="MAI-Thinking-1", usage=usage)
-
-    assert model_info["mode"] == "chat"
-    assert model_info["max_input_tokens"] == 256000
-    assert model_info["max_output_tokens"] == 64000
-    assert model_info["cache_read_input_token_cost"] == pytest.approx(2e-07)
-    assert model_info["supports_reasoning"] is True
-    assert model_info["supports_function_calling"] is True
-    assert prompt_cost == pytest.approx(2.0)
-    assert completion_cost == pytest.approx(8.0)
diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py
index 9b20192c3f2..32dbc5aa42a 100644
--- a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py
+++ b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py
@@ -23,7 +23,6 @@ TOKEN_PRICED_NAMES: Final = (
     "grok-4-20-reasoning",
     "grok-4-20-non-reasoning",
 )
-GROK_4_20_NAMES: Final = ("grok-4-20-reasoning", "grok-4-20-non-reasoning")
 CATALOG_NAMES: Final = TOKEN_PRICED_NAMES + ("whisper",)
 
 
@@ -72,22 +71,6 @@ def test_azure_ai_catalog_name_prices_the_same_in_any_casing(catalog_name: str)
     assert upper_cost == lowercase_cost
 
 
-@pytest.mark.usefixtures("local_model_cost_map")
-@pytest.mark.parametrize("catalog_name", GROK_4_20_NAMES)
-def test_azure_ai_grok_4_20_bills_cached_prompt_tokens_at_the_input_price(catalog_name: str) -> None:
-    uncached_prompt_cost, _ = cost_per_token(
-        model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0
-    )
-    cached_prompt_cost, _ = cost_per_token(
-        model=f"azure_ai/{catalog_name}",
-        prompt_tokens=A_MILLION,
-        completion_tokens=0,
-        cache_read_input_tokens=A_MILLION,
-    )
-    assert uncached_prompt_cost > 0
-    assert cached_prompt_cost == pytest.approx(uncached_prompt_cost)
-
-
 @pytest.mark.usefixtures("local_model_cost_map")
 def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None:
     one_second_cost: Final = _whisper_transcription_cost(1)
diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_kimi_k26_metadata.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_kimi_k26_metadata.py
deleted file mode 100644
index cbcc2a94043..00000000000
--- a/tests/test_litellm/llms/azure_ai/test_azure_ai_kimi_k26_metadata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Test Azure AI Kimi K2.6 model metadata.
-"""
-
-import json
-from importlib.resources import files
-
-import pytest
-
-
-@pytest.fixture(scope="module")
-def use_local_model_cost_map():
-    monkeypatch = pytest.MonkeyPatch()
-    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
-
-    import litellm
-    from litellm.utils import _invalidate_model_cost_lowercase_map
-
-    original_model_cost = litellm.model_cost
-    litellm.model_cost = json.loads(
-        files("litellm")
-        .joinpath("model_prices_and_context_window_backup.json")
-        .read_text(encoding="utf-8")
-    )
-    litellm.get_model_info.cache_clear()
-    _invalidate_model_cost_lowercase_map()
-    try:
-        yield litellm
-    finally:
-        litellm.model_cost = original_model_cost
-        litellm.get_model_info.cache_clear()
-        _invalidate_model_cost_lowercase_map()
-        monkeypatch.undo()
-
-
-def test_azure_ai_kimi_k26_cost_per_token(use_local_model_cost_map):
-    from litellm.llms.azure_ai.cost_calculator import cost_per_token
-    from litellm.types.utils import Usage
-
-    usage = Usage(
-        prompt_tokens=1_000_000,
-        completion_tokens=1_000_000,
-        total_tokens=2_000_000,
-    )
-
-    prompt_cost, completion_cost = cost_per_token(model="kimi-k2.6", usage=usage)
-
-    assert prompt_cost == pytest.approx(0.95)
-    assert completion_cost == pytest.approx(4.0)
diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index 2e9ea90f3b8..dadf52ab990 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -135,7 +135,6 @@ def test_bedrock_converse_1h_cache_write_billed_at_1h_rate(monkeypatch):
         16 * model_info["input_cost_per_token"] + 11632 * model_info["cache_creation_input_token_cost_above_1hr"]
     )
     assert prompt_cost == pytest.approx(expected_prompt_cost)
-    assert prompt_cost > 16 * model_info["input_cost_per_token"] + 11632 * model_info["cache_creation_input_token_cost"]
     assert completion_cost == pytest.approx(4 * model_info["output_cost_per_token"])
 
 
diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
index 09ebc1a3c95..40233c8502e 100644
--- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
+++ b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
@@ -4,32 +4,32 @@ import json
 import os
 from datetime import datetime
 from types import SimpleNamespace
+from typing import Final
 from unittest.mock import Mock
 
 import pytest
 
-# Ensure the project root is on the import path so `litellm` can be imported when
-# tests are executed from any working directory.
-
-from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
-from litellm.llms.bedrock.common_utils import (
-    ensure_bedrock_anthropic_messages_tool_names,
-    normalize_custom_field_on_tools,
-    normalize_tool_input_schema_types_for_bedrock_invoke,
-)
 from litellm.constants import (
     BEDROCK_MIN_THINKING_BUDGET_TOKENS,
     DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET,
 )
+
+# Ensure the project root is on the import path so `litellm` can be imported when
+# tests are executed from any working directory.
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.bedrock.common_utils import (
+    ensure_bedrock_anthropic_messages_tool_names,
+    normalize_custom_field_on_tools,
+    normalize_tool_input_schema_types_for_bedrock_invoke,
+)
 from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
     AmazonAnthropicClaudeMessagesConfig,
     AmazonAnthropicClaudeMessagesStreamDecoder,
 )
 
 
-
 @pytest.mark.asyncio
 async def test_bedrock_sse_wrapper_encodes_dict_chunks():
     """Verify that `bedrock_sse_wrapper` converts dictionary chunks to properly formatted Server-Sent Events and forwards non-dict chunks unchanged."""
@@ -1814,7 +1814,7 @@ async def test_unified_bedrock_messages_cache_on_start_only_never_negative_cost(
     message_delta/message_stop), final reconstructed usage + cost must still
     be consistent and non-negative.
     """
-    from litellm import completion_cost
+    from litellm import completion_cost, get_model_info
     from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import (
         AnthropicPassthroughLoggingHandler,
     )
@@ -1899,8 +1899,16 @@ async def test_unified_bedrock_messages_cache_on_start_only_never_negative_cost(
         model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
         custom_llm_provider="bedrock",
     )
+    model_info: Final = get_model_info(
+        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", custom_llm_provider="bedrock"
+    )
+    expected_cost: Final = (
+        10 * model_info["input_cost_per_token"]
+        + 22167 * model_info["cache_read_input_token_cost"]
+        + 181 * model_info["output_cost_per_token"]
+    )
     assert cost > 0
-    assert cost == pytest.approx(0.0093951, rel=0, abs=1e-9)
+    assert cost == pytest.approx(expected_cost, rel=0, abs=1e-9)
 
 
 @pytest.mark.asyncio
@@ -1911,7 +1919,7 @@ async def test_unified_bedrock_messages_sse_usage_and_cost_claude_sonnet_46():
     same logging reconstruction as Anthropic /messages. Ensures token counts and
     completion_cost match model_prices for us.anthropic.claude-sonnet-4-6.
     """
-    from litellm import completion_cost
+    from litellm import completion_cost, get_model_info
     from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import (
         AnthropicPassthroughLoggingHandler,
     )
@@ -1969,7 +1977,14 @@ async def test_unified_bedrock_messages_sse_usage_and_cost_claude_sonnet_46():
         model="bedrock/us.anthropic.claude-sonnet-4-6",
         custom_llm_provider="bedrock",
     )
-    assert cost == pytest.approx(0.052150725, rel=0, abs=1e-9)
+    model_info: Final = get_model_info(model="us.anthropic.claude-sonnet-4-6", custom_llm_provider="bedrock")
+    expected_cost: Final = (
+        3 * model_info["input_cost_per_token"]
+        + 10553 * model_info["cache_creation_input_token_cost"]
+        + 25490 * model_info["cache_read_input_token_cost"]
+        + 12 * model_info["output_cost_per_token"]
+    )
+    assert cost == pytest.approx(expected_cost, rel=0, abs=1e-9)
 
 
 @pytest.mark.parametrize(
@@ -2916,7 +2931,6 @@ def test_bedrock_messages_thinking_shape_follows_exact_bedrock_entry_flag(
     forced ``thinking.type='adaptive'`` even with ``supports_adaptive_thinking``
     explicitly set to ``false`` on the entry."""
     import litellm
-
     from litellm.types.router import GenericLiteLLMParams
 
     model = "global.anthropic.claude-opus-4-8"
diff --git a/tests/test_litellm/llms/cerebras/test_cerebras_chat_transformation.py b/tests/test_litellm/llms/cerebras/test_cerebras_chat_transformation.py
index a47180e9511..09718b1e6e0 100644
--- a/tests/test_litellm/llms/cerebras/test_cerebras_chat_transformation.py
+++ b/tests/test_litellm/llms/cerebras/test_cerebras_chat_transformation.py
@@ -1,6 +1,3 @@
-import pytest
-
-import litellm
 from litellm.llms.cerebras.chat import CerebrasConfig
 
 
@@ -62,23 +59,3 @@ def test_map_openai_params_preserves_max_retries_zero_falsy() -> None:
     assert "max_retries" in result and result["max_retries"] == 0, (
         f"max_retries=0 (falsy) must not be silently omitted; got: {result!r}"
     )
-
-
-def test_qwen_3_8_27b_cost_and_tokens(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
-    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
-    model = "cerebras/qwen-3.8-27b"
-    prompt_cost, completion_cost = litellm.cost_per_token(
-        model=model,
-        prompt_tokens=1000,
-        completion_tokens=1000,
-    )
-    assert abs(prompt_cost - 0.00099) < 1e-9
-    assert abs(completion_cost - 0.00149) < 1e-9
-
-    model_info = litellm.get_model_info(model)
-    assert model_info["max_input_tokens"] == 65536
-    assert model_info["max_output_tokens"] == 32768
-    assert model_info["supports_vision"] is True
-    assert model_info["supports_reasoning"] is True
-    assert model_info["supports_parallel_function_calling"] is True
diff --git a/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py b/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
index a7520bd5955..628040f521e 100644
--- a/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
+++ b/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
@@ -63,8 +63,6 @@ class TestChatGPTResponsesAPITransformation:
             "/v1/chat/completions",
             "/v1/responses",
         ]
-        assert model_info["max_input_tokens"] == 1050000
-        assert model_info["max_output_tokens"] == 128000
 
     @pytest.mark.parametrize(
         "model_name",
diff --git a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py
index afac7b0bc1a..465ff4fdcb6 100644
--- a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py
+++ b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py
@@ -31,61 +31,6 @@ PRICE_FIELDS: Final = (
     "cache_creation_input_token_cost",
     "cache_read_input_token_cost",
 )
-PUBLISHED_DBU_PER_MILLION: Final = {
-    "databricks/databricks-claude-fable-5-1": ("142.858", "714.286", "178.572", "3.572"),
-    "databricks/databricks-claude-fable-5": ("142.858", "714.286", "178.572", "14.286"),
-    "databricks/databricks-claude-opus-5": ("71.429", "357.143", "89.286", "7.143"),
-    "databricks/databricks-claude-opus-4-8": ("71.429", "357.143", "89.286", "7.143"),
-    "databricks/databricks-claude-opus-4-7": ("71.429", "357.143", "89.286", "7.143"),
-    "databricks/databricks-claude-opus-4-6": ("71.429", "357.143", "89.286", "7.143"),
-    "databricks/databricks-claude-opus-4-5": ("71.429", "357.143", "89.286", "7.143"),
-    "databricks/databricks-claude-opus-4-1": ("214.286", "1071.429", "267.857", "21.429"),
-    "databricks/databricks-claude-opus-4": ("214.286", "1071.429", "267.857", "21.429"),
-    "databricks/databricks-claude-sonnet-5": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-sonnet-4-6": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-sonnet-4-5": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-sonnet-4-1": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-sonnet-4": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-3-7-sonnet": ("42.857", "214.286", "53.571", "4.286"),
-    "databricks/databricks-claude-haiku-4-5": ("14.286", "71.429", "17.857", "1.429"),
-    "databricks/databricks-gpt-5": ("17.857", "142.857", "17.857", "1.786"),
-    "databricks/databricks-gpt-5-1": ("17.857", "142.857", "17.857", "1.786"),
-    "databricks/databricks-gpt-5-1-codex-max": ("17.857", "142.857", "17.857", "1.786"),
-    "databricks/databricks-gpt-5-1-codex-mini": ("3.571", "28.571", "3.571", "0.357"),
-    "databricks/databricks-gpt-5-mini": ("3.571", "28.571", "3.571", "0.357"),
-    "databricks/databricks-gpt-5-nano": ("0.714", "5.714", "0.714", "0.071"),
-    "databricks/databricks-gpt-5-2": ("25.000", "200.000", "25.000", "2.500"),
-    "databricks/databricks-gpt-5-2-codex": ("25.000", "200.000", "25.000", "2.500"),
-    "databricks/databricks-gpt-5-3-codex": ("25.000", "200.000", "25.000", "2.500"),
-    "databricks/databricks-gpt-5-6-sol": ("57.143", "285.714", "71.429", "5.714"),
-    "databricks/databricks-gpt-5-6-terra": ("35.714", "214.286", "44.643", "3.571"),
-    "databricks/databricks-gpt-5-6-luna": ("14.286", "85.714", "17.857", "1.429"),
-    "databricks/databricks-gpt-5-5": ("71.429", "428.571", "71.429", "7.143"),
-    "databricks/databricks-gpt-5-5-pro": ("428.571", "2571.429", "428.571", "428.571"),
-    "databricks/databricks-gpt-5-4": ("35.714", "214.286", "35.714", "3.571"),
-    "databricks/databricks-gpt-5-4-mini": ("10.714", "64.286", "10.714", "1.071"),
-    "databricks/databricks-gpt-5-4-nano": ("2.857", "17.857", "2.857", "0.286"),
-    "databricks/databricks-gemini-3-6-flash": ("26.786", "133.929", "26.786", "2.679"),
-    "databricks/databricks-gemini-3-5-flash": ("26.786", "160.714", "26.786", "2.679"),
-    "databricks/databricks-gemini-3-5-flash-lite": ("5.357", "44.643", "5.357", "0.536"),
-    "databricks/databricks-gemini-3-1-pro": ("35.714", "214.286", "35.714", "3.571"),
-    "databricks/databricks-gemini-3-pro": ("35.714", "214.286", "35.714", "3.571"),
-    "databricks/databricks-gemini-3-flash": ("8.929", "53.571", "8.929", "0.893"),
-    "databricks/databricks-gemini-3-1-flash-lite": ("4.464", "26.786", "4.464", "0.446"),
-    "databricks/databricks-gemini-2-5-pro": ("22.321", "178.571", "22.321", "2.232"),
-    "databricks/databricks-gemini-2-5-flash": ("5.357", "44.643", "5.357", "0.536"),
-    "databricks/databricks-kimi-k3": ("42.857", "214.286", "42.857", "4.286"),
-    "databricks/databricks-deepseek-v4-flash-0731": ("2.000", "4.000", "2.000", "0.400"),
-    "databricks/databricks-deepseek-v4-pro-0813": ("18.857", "56.571", "18.857", "1.886"),
-    "databricks/databricks-glm-5-2": ("20.000", "62.857", "20.000", "3.714"),
-    "databricks/databricks-glm-5-3": ("20.000", "62.857", "20.000", "3.714"),
-    "databricks/databricks-glm-5-3-flash": ("2.143", "7.143", "2.143", "0.429"),
-    "databricks/databricks-inkling": ("14.286", "57.857", "14.286", "2.429"),
-    "databricks/databricks-grok-4-6": ("35.714", "107.143", "35.714", "8.929"),
-    "databricks/databricks-qwen35-122b-a10b": ("3.143", "31.429", "3.143", "3.143"),
-    "databricks/databricks-qwen3-next-80b-a3b-instruct": ("2.143", "17.143", "2.143", "2.143"),
-    "databricks/databricks-qwen3-embedding-0-6b": ("0.286", "0", "0.286", "0.286"),
-}
 PROMOTIONAL_DISCOUNT: Final = 0.80
 PROMOTION_EXPIRES: Final = "2027-01-31"
 ENTRIES_STORING_PROMOTIONAL_RATE: Final = (
@@ -163,17 +108,6 @@ def test_legacy_endpoint_names_still_resolve(local_model_cost_map: None) -> None
     assert completion_cost == pytest.approx(100 * info["output_cost_per_token"])
 
 
-@pytest.mark.parametrize("model", NEW_MODELS)
-def test_new_models_carry_cache_pricing(local_model_cost_map: None, model: str) -> None:
-    info: Final = _model_info(model)
-
-    assert info["input_cost_per_token"] > 0
-    assert info["output_cost_per_token"] > 0
-    assert info["cache_creation_input_token_cost"] > info["input_cost_per_token"]
-    assert info["cache_read_input_token_cost"] < info["input_cost_per_token"]
-    assert info["supports_prompt_caching"] is True
-
-
 def test_every_priced_databricks_model_declares_cache_rates(local_model_cost_map: None) -> None:
     undeclared: Final = [
         model
@@ -186,41 +120,6 @@ def test_every_priced_databricks_model_declares_cache_rates(local_model_cost_map
     assert undeclared == []
 
 
-def test_models_without_a_cache_discount_bill_cache_tokens_at_the_input_rate(
-    local_model_cost_map: None,
-) -> None:
-    model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
-    info: Final = _model_info(model)
-    usage: Final = Usage(
-        prompt_tokens=10000,
-        completion_tokens=100,
-        total_tokens=10100,
-        cache_read_input_tokens=8000,
-    )
-
-    prompt_cost, _ = cost_per_token(model=model, usage=usage)
-
-    assert prompt_cost == pytest.approx(10000 * info["input_cost_per_token"])
-    assert prompt_cost > 8000 * info["input_cost_per_token"]
-
-
-def test_every_model_without_published_cache_dbu_bills_cache_at_its_own_input_rate(
-    local_model_cost_map: None,
-) -> None:
-    without_published_rates: Final = [
-        model
-        for model, info in litellm.model_cost.items()
-        if model.startswith("databricks/")
-        and info.get("input_cost_per_token")
-        and model not in PUBLISHED_DBU_PER_MILLION
-    ]
-
-    for model in without_published_rates:
-        info = _model_info(model)
-        for field in CACHE_FIELDS:
-            assert info[field] == pytest.approx(info["input_cost_per_token"]), (model, field)
-
-
 @pytest.mark.parametrize("model", NEW_MODELS)
 def test_backup_price_map_matches_main(model: str) -> None:
     main_cost: Final = json.loads(MAIN_PRICES.read_text())
@@ -229,11 +128,3 @@ def test_backup_price_map_matches_main(model: str) -> None:
     assert model in main_cost
     assert model in backup_cost
     assert backup_cost[model] == main_cost[model]
-
-
-def test_sonnet_5_ships_standard_rates_not_introductory(local_model_cost_map: None) -> None:
-    sonnet_5: Final = _model_info("databricks/databricks-claude-sonnet-5")
-    sonnet_4_6: Final = _model_info("databricks/databricks-claude-sonnet-4-6")
-
-    for field in PRICE_FIELDS:
-        assert sonnet_5[field] == pytest.approx(sonnet_4_6[field]), field
diff --git a/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_gpt_image_2_transformation.py b/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_gpt_image_2_transformation.py
index 1a527230f1b..9bf901e82a4 100644
--- a/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_gpt_image_2_transformation.py
+++ b/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_gpt_image_2_transformation.py
@@ -128,15 +128,15 @@ def test_transform_image_generation_request():
 
 
 @pytest.mark.parametrize(
-    ("model", "expected_cost_for_two_images"),
+    ("model", "catalog_key"),
     [
-        ("openai/gpt-image-2", 0.29),
-        ("gpt-image-2", 0.29),
-        ("openai/gpt-image-2/edit", 0.302),
+        ("openai/gpt-image-2", "fal_ai/openai/gpt-image-2"),
+        ("gpt-image-2", "fal_ai/openai/gpt-image-2"),
+        ("openai/gpt-image-2/edit", "fal_ai/openai/gpt-image-2/edit"),
     ],
 )
 def test_cost_calculator_uses_registry_price(
-    model, expected_cost_for_two_images, monkeypatch: pytest.MonkeyPatch
+    model, catalog_key, monkeypatch: pytest.MonkeyPatch
 ):
     monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
     monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
@@ -147,4 +147,6 @@ def test_cost_calculator_uses_registry_price(
             ImageObject(url="https://v3b.fal.media/files/b/two.png"),
         ]
     )
-    assert cost_calculator(model=model, image_response=response) == pytest.approx(expected_cost_for_two_images)
+    assert cost_calculator(model=model, image_response=response) == pytest.approx(
+        2 * litellm.model_cost[catalog_key]["output_cost_per_image"]
+    )
diff --git a/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_nano_banana_transformation.py b/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_nano_banana_transformation.py
index f26a6aeafda..b8844a43bf1 100644
--- a/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_nano_banana_transformation.py
+++ b/tests/test_litellm/llms/fal_ai/image_generation/test_fal_ai_nano_banana_transformation.py
@@ -1,8 +1,8 @@
 import os
+from typing import Final
 
 import pytest
 
-
 os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
 
 import litellm
@@ -145,20 +145,10 @@ def test_transform_request_includes_prompt_and_mapped_params():
     }
 
 
-@pytest.mark.parametrize(
-    "model", ["fal-ai/nano-banana", "fal-ai/gemini-25-flash-image"]
-)
-def test_nano_banana_pricing_registered(model):
-    info = litellm.get_model_info(
-        model=model, custom_llm_provider=litellm.LlmProviders.FAL_AI.value
-    )
-    assert info["output_cost_per_image"] == 0.039
-    assert info["mode"] == "image_generation"
-
-
 def test_cost_calculator_scales_with_image_count():
     image_response = ImageResponse(
         data=[ImageObject(url="https://x/1.png"), ImageObject(url="https://x/2.png")]
     )
     cost = cost_calculator(model="fal-ai/nano-banana", image_response=image_response)
-    assert cost == pytest.approx(0.078)
+    model_info: Final = litellm.get_model_info("fal-ai/nano-banana", "fal_ai")
+    assert cost == pytest.approx(2 * model_info["output_cost_per_image"])
diff --git a/tests/test_litellm/llms/fal_ai/test_cost_calculator.py b/tests/test_litellm/llms/fal_ai/test_cost_calculator.py
index f167aceaa95..1fd945c4e10 100644
--- a/tests/test_litellm/llms/fal_ai/test_cost_calculator.py
+++ b/tests/test_litellm/llms/fal_ai/test_cost_calculator.py
@@ -19,13 +19,17 @@ def _image_response(num_images: int = 1) -> ImageResponse:
     return ImageResponse(data=[ImageObject(url="https://example.com/img.png") for _ in range(num_images)])
 
 
+def _price(key: str) -> float:
+    return float(litellm.model_cost[key]["output_cost_per_image"])
+
+
 def test_high_quality_1024x1024_uses_keyed_price():
     cost = cost_calculator(
         model="openai/gpt-image-2",
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_alias_model_uses_keyed_price():
@@ -34,7 +38,7 @@ def test_alias_model_uses_keyed_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_provider_prefixed_model_uses_keyed_price():
@@ -43,7 +47,7 @@ def test_provider_prefixed_model_uses_keyed_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_provider_prefixed_edit_model_uses_keyed_edit_price():
@@ -52,7 +56,7 @@ def test_provider_prefixed_edit_model_uses_keyed_edit_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.219)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2/edit"))
 
 
 def test_default_request_priced_at_default_size_and_quality():
@@ -61,7 +65,7 @@ def test_default_request_priced_at_default_size_and_quality():
         image_response=_image_response(),
         optional_params={},
     )
-    assert cost == pytest.approx(0.145)
+    assert cost == pytest.approx(_price("fal_ai/openai/gpt-image-2"))
 
 
 def test_auto_quality_priced_as_high():
@@ -70,7 +74,7 @@ def test_auto_quality_priced_as_high():
         image_response=_image_response(),
         optional_params={"quality": "auto", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_low_quality_4k_uses_keyed_price():
@@ -79,7 +83,7 @@ def test_low_quality_4k_uses_keyed_price():
         image_response=_image_response(),
         optional_params={"quality": "low", "image_size": {"width": 3840, "height": 2160}},
     )
-    assert cost == pytest.approx(0.012)
+    assert cost == pytest.approx(_price("fal_ai/low/3840-x-2160/openai/gpt-image-2"))
 
 
 def test_named_fal_size_uses_keyed_price():
@@ -88,7 +92,7 @@ def test_named_fal_size_uses_keyed_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": "square_hd"},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_edit_model_uses_keyed_edit_price():
@@ -97,7 +101,7 @@ def test_edit_model_uses_keyed_edit_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.219)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2/edit"))
 
 
 def test_edit_model_without_size_falls_back_to_flat_price():
@@ -106,7 +110,7 @@ def test_edit_model_without_size_falls_back_to_flat_price():
         image_response=_image_response(),
         optional_params={"quality": "high"},
     )
-    assert cost == pytest.approx(0.151)
+    assert cost == pytest.approx(_price("fal_ai/openai/gpt-image-2/edit"))
 
 
 def test_missing_optional_params_falls_back_to_flat_price():
@@ -115,7 +119,7 @@ def test_missing_optional_params_falls_back_to_flat_price():
         image_response=_image_response(),
         optional_params=None,
     )
-    assert cost == pytest.approx(0.145)
+    assert cost == pytest.approx(_price("fal_ai/openai/gpt-image-2"))
 
 
 def test_unlisted_size_falls_back_to_flat_price():
@@ -124,7 +128,7 @@ def test_unlisted_size_falls_back_to_flat_price():
         image_response=_image_response(),
         optional_params={"quality": "high", "image_size": {"width": 999, "height": 999}},
     )
-    assert cost == pytest.approx(0.145)
+    assert cost == pytest.approx(_price("fal_ai/openai/gpt-image-2"))
 
 
 def test_keyed_price_multiplies_per_image():
@@ -133,7 +137,7 @@ def test_keyed_price_multiplies_per_image():
         image_response=_image_response(num_images=2),
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.422)
+    assert cost == pytest.approx(2 * _price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_route_image_generation_passes_optional_params_to_fal():
@@ -143,7 +147,7 @@ def test_route_image_generation_passes_optional_params_to_fal():
         custom_llm_provider="fal_ai",
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
 
 
 def test_route_image_generation_with_provider_prefixed_model_uses_keyed_price():
@@ -153,4 +157,4 @@ def test_route_image_generation_with_provider_prefixed_model_uses_keyed_price():
         custom_llm_provider="fal_ai",
         optional_params={"quality": "high", "image_size": {"width": 1024, "height": 1024}},
     )
-    assert cost == pytest.approx(0.211)
+    assert cost == pytest.approx(_price("fal_ai/high/1024-x-1024/openai/gpt-image-2"))
diff --git a/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py b/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py
index 8863258ff76..4bfb220bdca 100644
--- a/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py
+++ b/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py
@@ -4,7 +4,6 @@ import json
 import httpx
 import pytest
 
-import litellm
 from litellm.llms.gemini.audio_transcription.transformation import (
     GeminiAudioTranscriptionConfig,
 )
@@ -295,25 +294,3 @@ class TestSubtitleSynthesisThroughHandler:
             {"word": "Hello", "start": 0.1, "end": 0.4, "speaker": "spk:0"},
             {"word": "world.", "start": 0.5, "end": 0.9, "speaker": "spk:1"},
         ]
-
-
-class TestCostRegression:
-    @pytest.fixture
-    def local_cost_map(self, monkeypatch):
-        monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
-        monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
-
-    def test_registry_entries(self, local_cost_map):
-        batch_entry = litellm.model_cost["gemini/gemini-3.5-transcribe"]
-        assert batch_entry["mode"] == "audio_transcription"
-        assert batch_entry["input_cost_per_audio_token"] == 2e-06
-        assert batch_entry["input_cost_per_token"] == 2e-06
-        assert batch_entry["output_cost_per_token"] == 1.2e-05
-        assert batch_entry["supported_endpoints"] == ["/v1/audio/transcriptions"]
-
-        live_entry = litellm.model_cost["gemini/gemini-3.5-transcribe-live"]
-        assert live_entry["mode"] == "audio_transcription"
-        assert live_entry["input_cost_per_audio_token"] == 3.5e-06
-        assert live_entry["input_cost_per_token"] == 3.5e-06
-        assert live_entry["output_cost_per_token"] == 2.1e-05
-        assert live_entry["supported_endpoints"] == ["/v1/realtime"]
diff --git a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
index 3eb4a70ee15..736602c3968 100644
--- a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
+++ b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
@@ -1,6 +1,6 @@
 import json
 from collections.abc import Mapping
-from typing import cast
+from typing import Final, cast
 from unittest.mock import MagicMock
 
 import pytest
@@ -1903,7 +1903,14 @@ def test_gemini_response_done_bills_audio_output_tokens_at_audio_rate(monkeypatc
         custom_llm_provider="gemini",
         litellm_model_name="gemini-2.5-flash-native-audio-preview-12-2025",
     )
-    assert cost == pytest.approx(377 * 5e-07 + 51 * 1.2e-05 + 37 * 2e-06)
+    model_info: Final = litellm.get_model_info(
+        model="gemini-2.5-flash-native-audio-preview-12-2025", custom_llm_provider="gemini"
+    )
+    assert cost == pytest.approx(
+        377 * model_info["input_cost_per_token"]
+        + 51 * model_info["output_cost_per_audio_token"]
+        + 37 * model_info["output_cost_per_token"]
+    )
 @pytest.fixture(autouse=False)
 def patch_gemini_transcribe_live_cost_map_entry(monkeypatch):
     """Inject the gemini-3.5-transcribe-live registry entry locally.
diff --git a/tests/test_litellm/llms/inception/test_inception_chat_transformation.py b/tests/test_litellm/llms/inception/test_inception_chat_transformation.py
index 04813143fae..830498ff842 100644
--- a/tests/test_litellm/llms/inception/test_inception_chat_transformation.py
+++ b/tests/test_litellm/llms/inception/test_inception_chat_transformation.py
@@ -7,7 +7,6 @@ import os
 from unittest import mock
 
 import httpx
-import pytest
 
 import litellm
 from litellm.llms.inception.chat.transformation import InceptionChatConfig
@@ -306,24 +305,3 @@ def test_inception_completion_targets_inception_endpoint():
     assert captured["body"]["model"] == "mercury-2"
     assert captured["body"]["tool_choice"] == "auto"
     assert response.choices[0].message.content == "hi"
-
-
-def test_inception_mercury_2_5_cost_and_tokens(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
-    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
-    model = "inception/mercury-2.5"
-    prompt_cost, completion_cost = litellm.cost_per_token(
-        model=model,
-        prompt_tokens=1000,
-        completion_tokens=500,
-    )
-    assert abs(prompt_cost - 0.0002) < 1e-9
-    assert abs(completion_cost - 0.000375) < 1e-9
-
-    model_info = litellm.get_model_info(model)
-    assert model_info["max_input_tokens"] == 260000
-    assert model_info["max_output_tokens"] == 65536
-    assert model_info["litellm_provider"] == "inception"
-    assert model_info["mode"] == "chat"
-    assert model_info["supports_function_calling"] is True
-    assert model_info["supports_response_schema"] is True
diff --git a/tests/test_litellm/llms/openai_like/test_cognition_provider.py b/tests/test_litellm/llms/openai_like/test_cognition_provider.py
index d392abc6cc5..41337d0c92f 100644
--- a/tests/test_litellm/llms/openai_like/test_cognition_provider.py
+++ b/tests/test_litellm/llms/openai_like/test_cognition_provider.py
@@ -8,6 +8,7 @@ its traffic.
 
 import json
 from pathlib import Path
+from typing import Final
 
 import pytest
 
@@ -112,15 +113,13 @@ class TestCognitionProviderIdentity:
 class TestCognitionCostTracking:
 
     @pytest.mark.parametrize(
-        "model, expected_prompt_cost, expected_completion_cost",
+        "model",
         [
-            ("cognition/swe-1.7", 0.5, 2.5),
-            ("cognition/swe-1.7-lightning", 2.5, 12.5),
+            "cognition/swe-1.7",
+            "cognition/swe-1.7-lightning",
         ],
     )
-    def test_cost_differs_from_openai_pricing(
-        self, model: str, expected_prompt_cost: float, expected_completion_cost: float
-    ):
+    def test_cost_differs_from_openai_pricing(self, model: str):
         """A cognition-prefixed model must never be priced off an OpenAI cost entry."""
         from litellm.cost_calculator import cost_per_token
 
@@ -131,8 +130,9 @@ class TestCognitionCostTracking:
             custom_llm_provider="cognition",
         )
 
-        assert prompt_cost == pytest.approx(expected_prompt_cost)
-        assert completion_cost == pytest.approx(expected_completion_cost)
+        model_info: Final = litellm.model_cost[model]
+        assert prompt_cost == pytest.approx(1_000_000 * model_info["input_cost_per_token"])
+        assert completion_cost == pytest.approx(1_000_000 * model_info["output_cost_per_token"])
 
     def test_lightning_is_five_times_the_standard_tier(self):
         standard = litellm.get_model_info(model="cognition/swe-1.7")
diff --git a/tests/test_litellm/llms/openai_like/test_meta_provider.py b/tests/test_litellm/llms/openai_like/test_meta_provider.py
index c79e4b77cc5..2f752a49dc8 100644
--- a/tests/test_litellm/llms/openai_like/test_meta_provider.py
+++ b/tests/test_litellm/llms/openai_like/test_meta_provider.py
@@ -2,6 +2,8 @@
 Tests for the Meta Model API (Muse Spark) provider configuration and integration.
 """
 
+from typing import Final
+
 import litellm
 
 
@@ -207,5 +209,6 @@ class TestMuseSparkModelInfo:
             model="meta/muse-spark-1.1",
             custom_llm_provider="meta",
         )
-        expected = 1000 * 1.25e-06 + 500 * 4.25e-06
+        model_info: Final = litellm.model_cost["meta/muse-spark-1.1"]
+        expected = 1000 * model_info["input_cost_per_token"] + 500 * model_info["output_cost_per_token"]
         assert abs(cost - expected) < 1e-12
diff --git a/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py b/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py
index c94b2cbfa80..0007dfe0e1c 100644
--- a/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py
+++ b/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py
@@ -2,6 +2,8 @@
 Tests for Tensormesh provider configuration and integration.
 """
 
+from typing import Final
+
 import pytest
 
 import litellm
@@ -154,17 +156,12 @@ class TestTensormeshCostMap:
         for model in TENSORMESH_MODELS:
             assert litellm.supports_reasoning(model) is (model in reasoning_models), model
 
-    def test_cost_is_wired_and_cache_reads_are_free(self):
+    def test_cost_is_wired(self):
         prompt_cost, completion_cost = litellm.cost_per_token(
             model="tensormesh/openai/gpt-oss-120b",
             prompt_tokens=1_000_000,
             completion_tokens=1_000_000,
         )
-        assert prompt_cost == pytest.approx(0.15)
-        assert completion_cost == pytest.approx(0.60)
-        assert (
-            litellm.model_cost["tensormesh/openai/gpt-oss-120b"][
-                "cache_read_input_token_cost"
-            ]
-            == 0
-        )
+        model_info: Final = litellm.model_cost["tensormesh/openai/gpt-oss-120b"]
+        assert prompt_cost == pytest.approx(1_000_000 * model_info["input_cost_per_token"])
+        assert completion_cost == pytest.approx(1_000_000 * model_info["output_cost_per_token"])
diff --git a/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_handler.py b/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_handler.py
index 45753d4ee7b..a960eec5bbd 100644
--- a/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_handler.py
+++ b/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_handler.py
@@ -2,7 +2,7 @@
 
 import asyncio
 import json
-from typing import Any, Dict, List
+from typing import Any, Dict, Final, List
 from unittest.mock import MagicMock
 
 import httpx
@@ -1094,6 +1094,6 @@ class TestSpendTracking:
             model="soniox/stt-async-v4",
             call_type="transcription",
         )
-        # 10 minutes of audio billed at Soniox's ~$0.10/hour async rate.
         assert cost > 0
-        assert cost == pytest.approx((0.10 / 3600) * 600.0, rel=1e-3)
+        model_info: Final = litellm.get_model_info(model="soniox/stt-async-v4")
+        assert cost == pytest.approx(600.0 * model_info["output_cost_per_second"], rel=1e-3)
diff --git a/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_audio_transcription_transformation.py b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_audio_transcription_transformation.py
index 3a1922d1021..5a3c2612ceb 100644
--- a/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_audio_transcription_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_audio_transcription_transformation.py
@@ -1,12 +1,10 @@
 import base64
 import json
-import os
 from urllib.parse import urlparse
 
 import httpx
 import pytest
 
-
 import litellm
 from litellm.llms.vertex_ai.audio_transcription.transformation import (
     VertexAIAudioTranscriptionConfig,
@@ -313,22 +311,3 @@ class TestProviderRouting:
         )
         assert "response_format" not in optional_params
         assert optional_params["language"] == "fr-FR"
-
-
-class TestModelCostEntry:
-    REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))
-
-    @pytest.mark.parametrize(
-        "cost_map_path",
-        [
-            "model_prices_and_context_window.json",
-            "litellm/model_prices_and_context_window_backup.json",
-        ],
-    )
-    def test_chirp_3_registered_as_audio_transcription(self, cost_map_path):
-        with open(os.path.join(self.REPO_ROOT, cost_map_path)) as f:
-            entry = json.load(f)["vertex_ai/chirp_3"]
-        assert entry["mode"] == "audio_transcription"
-        assert entry["litellm_provider"] == "vertex_ai"
-        assert entry["input_cost_per_second"] == pytest.approx(0.016 / 60, rel=1e-3)
-        assert entry["supported_endpoints"] == ["/v1/audio/transcriptions"]
diff --git a/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py
index 08e46b1ffac..2e4eaa03a0a 100644
--- a/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py
@@ -1,6 +1,5 @@
 import base64
 import json
-import os
 
 import httpx
 import pytest
@@ -305,41 +304,3 @@ class TestOptionalParams:
         )
         assert "response_format" not in optional_params
         assert optional_params["language"] == "fr-FR"
-
-
-class TestModelCostEntry:
-    REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))
-
-    @pytest.mark.parametrize(
-        "cost_map_path",
-        [
-            "model_prices_and_context_window.json",
-            "litellm/model_prices_and_context_window_backup.json",
-        ],
-    )
-    def test_transcribe_preview_pricing(self, cost_map_path):
-        with open(os.path.join(self.REPO_ROOT, cost_map_path)) as f:
-            entry = json.load(f)["vertex_ai/gemini-3.5-transcribe-preview"]
-        assert entry["mode"] == "audio_transcription"
-        assert entry["litellm_provider"] == "vertex_ai"
-        assert entry["input_cost_per_audio_token"] == pytest.approx(2e-06)
-        assert entry["input_cost_per_token"] == pytest.approx(2e-06)
-        assert entry["output_cost_per_token"] == pytest.approx(1.2e-05)
-        assert entry["supported_endpoints"] == ["/v1/audio/transcriptions"]
-
-    @pytest.mark.parametrize(
-        "cost_map_path",
-        [
-            "model_prices_and_context_window.json",
-            "litellm/model_prices_and_context_window_backup.json",
-        ],
-    )
-    def test_transcribe_live_preview_pricing(self, cost_map_path):
-        with open(os.path.join(self.REPO_ROOT, cost_map_path)) as f:
-            entry = json.load(f)["vertex_ai/gemini-3.5-transcribe-live-preview"]
-        assert entry["mode"] == "audio_transcription"
-        assert entry["litellm_provider"] == "vertex_ai"
-        assert entry["input_cost_per_audio_token"] == pytest.approx(3.5e-06)
-        assert entry["input_cost_per_token"] == pytest.approx(3.5e-06)
-        assert entry["output_cost_per_token"] == pytest.approx(2.1e-05)
-        assert entry["supported_endpoints"] == ["/v1/realtime"]
diff --git a/tests/test_litellm/llms/vertex_ai/gemini_embeddings/test_batch_embed_content_transformation.py b/tests/test_litellm/llms/vertex_ai/gemini_embeddings/test_batch_embed_content_transformation.py
index fd8c2a9cf6a..a6d160eda90 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini_embeddings/test_batch_embed_content_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini_embeddings/test_batch_embed_content_transformation.py
@@ -316,6 +316,9 @@ class TestProcessEmbedContentResponseUsage:
 
     MODEL = "gemini-embedding-2"
 
+    def _rate(self, model: str, field: str) -> float:
+        return float(litellm.get_model_info(model=model, custom_llm_provider="vertex_ai")[field])
+
     def test_multimodal_image_preserves_usage_metadata(self):
         response_json = {
             "embedding": {"values": [0.1, 0.2, 0.3]},
@@ -436,7 +439,7 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(258 * 4.5e-7)
+        assert prompt_cost == pytest.approx(258 * self._rate(self.MODEL, "input_cost_per_image_token"))
 
     def test_file_reference_non_image_not_counted_as_image(self):
         """A files/... ref resolving to a non-image mime keeps audio token billing."""
@@ -468,7 +471,7 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(64 * 6.5e-6)
+        assert prompt_cost == pytest.approx(64 * self._rate(self.MODEL, "input_cost_per_audio_token"))
 
     def test_video_plus_audio_does_not_double_bill_text(self):
         """Video and audio responses are billed from their respective token counts."""
@@ -498,7 +501,10 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(516 * 1.2e-5 + 64 * 6.5e-6)
+        assert prompt_cost == pytest.approx(
+            516 * self._rate(self.MODEL, "input_cost_per_video_token")
+            + 64 * self._rate(self.MODEL, "input_cost_per_audio_token")
+        )
 
     def test_preview_alias_bills_audio_per_token(self):
         response_json = {
@@ -520,7 +526,7 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(64 * 6.5e-6)
+        assert prompt_cost == pytest.approx(64 * self._rate("gemini-embedding-2-preview", "input_cost_per_audio_token"))
 
     def test_image_without_modality_details_uses_image_rate(self):
         response_json = {
@@ -544,7 +550,7 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(258 * 4.5e-7)
+        assert prompt_cost == pytest.approx(258 * self._rate(self.MODEL, "input_cost_per_image_token"))
 
     @pytest.mark.parametrize(
         "input_value,resolved_files,expected_image_tokens",
@@ -582,8 +588,8 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        expected_rate = 4.5e-7 if expected_image_tokens else 2e-7
-        assert prompt_cost == pytest.approx(258 * expected_rate)
+        expected_field = "input_cost_per_image_token" if expected_image_tokens else "input_cost_per_token"
+        assert prompt_cost == pytest.approx(258 * self._rate(self.MODEL, expected_field))
 
     def test_mixed_text_and_image_without_modality_details_not_billed_as_image(self):
         response_json = {
@@ -606,7 +612,7 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(270 * 2e-7)
+        assert prompt_cost == pytest.approx(270 * self._rate(self.MODEL, "input_cost_per_token"))
 
     def test_text_without_modality_details_uses_text_rate(self):
         response_json = {
@@ -630,4 +636,4 @@ class TestProcessEmbedContentResponseUsage:
             usage=result.usage,
             custom_llm_provider="vertex_ai",
         )
-        assert prompt_cost == pytest.approx(12 * 2e-7)
+        assert prompt_cost == pytest.approx(12 * self._rate(self.MODEL, "input_cost_per_token"))
diff --git a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
index c192d22b3b7..c5e2ffb36d8 100644
--- a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
@@ -6,7 +6,7 @@ import base64
 import json
 from collections.abc import Mapping
 from pathlib import Path
-from typing import cast
+from typing import Final, cast
 from unittest.mock import Mock, patch
 
 import httpx
@@ -155,23 +155,26 @@ class TestVertexAIVideoConfig:
         assert custom_llm_provider == "vertex_ai"
 
     def test_veo_31_lite_cost_uses_resolution_tiers(self):
-        model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH)
-        model_info = model_cost[VEO_31_LITE_VERTEX_MODEL]
-
-        assert video_generation_cost(
+        model_cost: Final = _load_model_cost_map(BACKUP_MODEL_COST_PATH)
+        model_info: Final = model_cost[VEO_31_LITE_VERTEX_MODEL]
+        standard_cost: Final = video_generation_cost(
             model=VEO_31_LITE_VERTEX_MODEL,
             duration_seconds=10.0,
             custom_llm_provider="vertex_ai",
             model_info=dict(model_info),
             video_resolution="720p",
-        ) == pytest.approx(0.5)
-        assert video_generation_cost(
+        )
+        high_resolution_cost: Final = video_generation_cost(
             model=VEO_31_LITE_VERTEX_MODEL,
             duration_seconds=10.0,
             custom_llm_provider="vertex_ai",
             model_info=dict(model_info),
             video_resolution="1080p",
-        ) == pytest.approx(0.8)
+        )
+
+        assert standard_cost == pytest.approx(10.0 * model_info["output_cost_per_second"])
+        assert high_resolution_cost == pytest.approx(10.0 * model_info["output_cost_per_second_1080p"])
+        assert standard_cost != high_resolution_cost
 
     def test_transform_video_create_request(self):
         """Test transformation of video creation request."""
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 7b53d3a58df..7a00345263c 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -222,7 +222,10 @@ def test_transcription_cost_uses_token_pricing(_local_model_cost_map):
         call_type="atranscription",
     )
 
-    expected_cost = (14 * 2.5e-06) + (45 * 1e-05)
+    model_info: Final = litellm.get_model_info(model="gpt-4o-transcribe", custom_llm_provider="openai")
+    expected_cost = (
+        14 * model_info["input_cost_per_audio_token"] + 45 * model_info["output_cost_per_token"]
+    )
     assert pytest.approx(cost, rel=1e-6) == expected_cost
 
 
@@ -247,7 +250,12 @@ def test_transcription_token_pricing_is_provider_aware(_local_model_cost_map):
         call_type="atranscription",
     )
 
-    expected_cost = (199 * 2e-06) + (1 * 2e-06) + (10 * 1.2e-05)
+    model_info: Final = litellm.get_model_info(model="gemini/gemini-3.5-transcribe", custom_llm_provider="gemini")
+    expected_cost = (
+        199 * model_info["input_cost_per_audio_token"]
+        + 1 * model_info["input_cost_per_token"]
+        + 10 * model_info["output_cost_per_token"]
+    )
     assert pytest.approx(cost, rel=1e-6) == expected_cost
 
 
@@ -264,7 +272,8 @@ def test_transcription_cost_falls_back_to_duration(_local_model_cost_map):
         call_type="atranscription",
     )
 
-    expected_cost = 10.0 * 0.0001
+    model_info: Final = litellm.get_model_info(model="whisper-1", custom_llm_provider="openai")
+    expected_cost = 10.0 * model_info["input_cost_per_second"]
     assert pytest.approx(cost, rel=1e-6) == expected_cost
 
 
@@ -284,7 +293,8 @@ def test_vertex_chirp_3_transcription_cost_from_duration(_local_model_cost_map):
         call_type="atranscription",
     )
 
-    expected_cost = 18.0 * 0.00026667
+    model_info: Final = litellm.get_model_info(model="vertex_ai/chirp_3", custom_llm_provider="vertex_ai")
+    expected_cost = 18.0 * model_info["input_cost_per_second"]
     assert cost > 0
     assert pytest.approx(cost, rel=1e-6) == expected_cost
 
@@ -560,7 +570,7 @@ def test_realtime_logging_object_does_not_validate_unknown_event_types():
 def test_realtime_transcription_duration_cost(monkeypatch):
     """
     gpt-realtime-whisper transcription sessions are billed by input audio duration
-    ($0.017/min). The .completed events carry usage {type: duration, seconds: N};
+    The .completed events carry usage {type: duration, seconds: N};
     cost must equal total_seconds * input_cost_per_second.
     """
     from datetime import datetime
@@ -610,8 +620,8 @@ def test_realtime_transcription_duration_cost(monkeypatch):
         litellm_logging_obj=logging_obj,
     )
 
-    # 90 seconds at $0.017/minute.
-    expected = 90.0 * (0.017 / 60)
+    model_info: Final = litellm.get_model_info(model="gpt-realtime-whisper", custom_llm_provider="openai")
+    expected = 90.0 * model_info["input_cost_per_second"]
     assert abs(cost - expected) < 1e-9
     assert cost > 0  # guards against the duration branch being dropped
     assert logging_obj.cost_breakdown is not None
@@ -649,7 +659,8 @@ def test_realtime_transcription_duration_cost_resolves_model_from_litellm_name(
         custom_llm_provider="azure",
         litellm_model_name="azure/gpt-realtime-whisper",
     )
-    assert abs(cost - 120.0 * (0.017 / 60)) < 1e-9
+    model_info: Final = litellm.get_model_info(model="azure/gpt-realtime-whisper", custom_llm_provider="azure")
+    assert abs(cost - 120.0 * model_info["input_cost_per_second"]) < 1e-9
 
 
 def test_realtime_transcription_no_completed_events_is_zero(monkeypatch):
@@ -683,9 +694,7 @@ def test_realtime_transcription_token_billed_fallback(monkeypatch):
 
     from litellm.cost_calculator import _transcription_usage_cost
 
-    # gpt-4o-transcribe: input_cost_per_audio_token = 2.5e-06, input_cost_per_token = 2.5e-06,
-    # output_cost_per_token = 1e-05
-    model_info = litellm.get_model_info(model="gpt-4o-transcribe", custom_llm_provider="openai")
+    model_info: Final = litellm.get_model_info(model="gpt-4o-transcribe", custom_llm_provider="openai")
     usage = {
         "type": "tokens",
         "input_tokens": 40,
@@ -695,9 +704,9 @@ def test_realtime_transcription_token_billed_fallback(monkeypatch):
     }
     cost = _transcription_usage_cost(usage, model_info)
     expected = (
-        30 * 2.5e-06  # audio tokens
-        + 10 * 2.5e-06  # text tokens
-        + 10 * 1e-05  # output tokens
+        30 * model_info["input_cost_per_audio_token"]
+        + 10 * model_info["input_cost_per_token"]
+        + 10 * model_info["output_cost_per_token"]
     )
     assert abs(cost - expected) < 1e-12
 
@@ -1687,10 +1696,6 @@ def test_vertex_regional_deployment_costs_uplift_over_global(monkeypatch):
     """
     Regression for https://github.com/BerriAI/litellm/issues/34393: two Vertex
     deployments differing only in vertex_location must not price identically.
-    Google bills non-global endpoints at 1.1x for regional-pricing models, so the
-    regional request costs 1.1x the global one for the exact same usage, through
-    both vertex cost routes (Claude via cost_per_token, Gemini via
-    cost_per_character's token fallback).
     """
     monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
     monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
@@ -1712,8 +1717,10 @@ def test_vertex_regional_deployment_costs_uplift_over_global(monkeypatch):
         global_total = global_prompt + global_completion
         regional_total = regional_prompt + regional_completion
         assert global_total > 0
-        assert regional_total == pytest.approx(global_total * 1.10, rel=1e-9), (
-            f"{model}: regional Vertex request must cost 1.1x the global one"
+        assert regional_total == pytest.approx(
+            global_total
+            * litellm.model_cost[f"vertex_ai/{model}"]["regional_endpoint_uplift_multiplier"],
+            rel=1e-9,
         )
 
 
@@ -2796,39 +2803,12 @@ def test_anthropic_geo_and_fast_multipliers_compose(_local_model_cost_map, monke
     assert completion_cost == pytest.approx(500 * 25e-6 * 2.0 * 1.1)
 
 
-@pytest.mark.parametrize(
-    "model,expected_fast",
-    [
-        ("claude-opus-5", 2.0),
-        ("claude-opus-4-8", 2.0),
-        ("claude-opus-4-6", None),
-        ("claude-opus-4-6-20260205", None),
-        ("claude-opus-4-7", None),
-        ("claude-opus-4-7-20260416", None),
-    ],
-)
-def test_anthropic_fast_multiplier_only_on_models_with_fast_mode(_local_model_cost_map, model, expected_fast):
-    """
-    Anthropic serves fast mode on Opus 5 and Opus 4.8 only, at 2x. Opus 4.6 and
-    4.7 accept the ``speed`` request param but are always served standard, so a
-    ``fast`` multiplier on their map entries overbills every request that asked
-    for fast and was served standard.
-    """
-    entry = litellm.model_cost[model]
-    assert entry["provider_specific_entry"].get("fast") == expected_fast
-
-
 @pytest.mark.parametrize(
     "model",
     ["claude-sonnet-4-6", "claude-mythos-5", "claude-mythos-preview"],
 )
 def test_anthropic_us_data_residency_uplift_on_claude_4_6_and_later_models(_local_model_cost_map, monkeypatch, model):
-    """
-    Anthropic bills every Claude 4.6+ model served with ``inference_geo="us"`` at
-    1.1x, and echoes that geo back in the response usage, so each of these real
-    cost-map entries has to carry the ``us`` multiplier or US-pinned traffic is
-    under-reported by 10%.
-    """
+    """Anthropic's US data-residency multiplier must be applied to both token types."""
     from litellm.llms.anthropic.cost_calculation import (
         cost_per_token as anthropic_cost_per_token,
     )
@@ -2845,9 +2825,11 @@ def test_anthropic_us_data_residency_uplift_on_claude_4_6_and_later_models(_loca
     geo_usage.inference_geo = "us"
     geo_prompt_cost, geo_completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage)
 
+    model_info: Final = litellm.model_cost[model]
+    us_multiplier: Final = model_info["provider_specific_entry"]["us"]
     assert base_prompt_cost > 0
-    assert geo_prompt_cost == pytest.approx(base_prompt_cost * 1.1)
-    assert geo_completion_cost == pytest.approx(base_completion_cost * 1.1)
+    assert geo_prompt_cost == pytest.approx(base_prompt_cost * us_multiplier)
+    assert geo_completion_cost == pytest.approx(base_completion_cost * us_multiplier)
 
 
 def test_gemini_cache_tokens_details_no_negative_values():
@@ -3819,7 +3801,13 @@ def test_completion_cost_prices_anthropic_shaped_cache_read_tokens(_local_model_
         custom_llm_provider="openai",
     )
 
-    assert cost == pytest.approx(3 * 4e-6 + 4014 * 4e-7 + 5 * 2e-5, rel=1e-9)
+    model_info: Final = litellm.get_model_info(model="gpt-5.6-sol", custom_llm_provider="openai")
+    expected_cost = (
+        3 * model_info["input_cost_per_token"]
+        + 4014 * model_info["cache_read_input_token_cost"]
+        + 5 * model_info["output_cost_per_token"]
+    )
+    assert cost == pytest.approx(expected_cost, rel=1e-9)
 
 
 def _together_chat_response(
@@ -3852,7 +3840,13 @@ def test_completion_cost_prices_together_cached_tokens_at_cache_read_rate(_local
         custom_llm_provider="together_ai",
     )
 
-    assert cost == pytest.approx(1 * 1.4e-07 + 7863 * 3e-08 + 16 * 2.8e-07, rel=1e-9)
+    model_info: Final = litellm.model_cost["together_ai/deepseek-ai/DeepSeek-V4-Flash-0731"]
+    expected_cost = (
+        1 * model_info["input_cost_per_token"]
+        + 7863 * model_info["cache_read_input_token_cost"]
+        + 16 * model_info["output_cost_per_token"]
+    )
+    assert cost == pytest.approx(expected_cost, rel=1e-9)
 
 
 def test_completion_cost_together_mapped_model_skips_size_bucket(_local_model_cost_map):
@@ -3867,7 +3861,9 @@ def test_completion_cost_together_mapped_model_skips_size_bucket(_local_model_co
         custom_llm_provider="together_ai",
     )
 
-    assert cost == pytest.approx(63 * 3.5e-07 + 16 * 1.5e-06, rel=1e-9)
+    model_info: Final = litellm.model_cost["together_ai/meta-models/Muse-Glimmer-30B"]
+    expected_cost = 63 * model_info["input_cost_per_token"] + 16 * model_info["output_cost_per_token"]
+    assert cost == pytest.approx(expected_cost, rel=1e-9)
 
 
 def test_completion_cost_together_unmapped_model_still_uses_size_bucket(_local_model_cost_map):
@@ -3878,7 +3874,9 @@ def test_completion_cost_together_unmapped_model_still_uses_size_bucket(_local_m
         custom_llm_provider="together_ai",
     )
 
-    assert cost == pytest.approx((23 + 15) * 9e-07, rel=1e-9)
+    model_info: Final = litellm.model_cost["together-ai-41.1b-80b"]
+    expected_cost = 23 * model_info["input_cost_per_token"] + 15 * model_info["output_cost_per_token"]
+    assert cost == pytest.approx(expected_cost, rel=1e-9)
 
 
 def test_completion_cost_together_metadata_only_model_still_uses_size_bucket(_local_model_cost_map):
@@ -4100,7 +4098,9 @@ def test_completion_cost_nonzero_for_slash_alias_model_name(_local_model_cost_ma
         custom_llm_provider="vertex_ai",
     )
 
-    assert cost == pytest.approx(100 * 5e-6 + 50 * 2.5e-5, rel=1e-9)
+    model_info: Final = litellm.model_cost["vertex_ai/claude-opus-5"]
+    expected_cost = 100 * model_info["input_cost_per_token"] + 50 * model_info["output_cost_per_token"]
+    assert cost == pytest.approx(expected_cost, rel=1e-9)
 
 
 def test_select_model_name_unresolvable_alias_unchanged(_local_model_cost_map):
@@ -4369,7 +4369,6 @@ def test_handle_realtime_stream_cost_calculation_bills_nested_reasoning_tokens_o
         + 23 * info["output_cost_per_token"]
     )
     assert total_cost == pytest.approx(expected)
-    assert total_cost == pytest.approx(0.0002362)
 
 
 def test_collect_and_combine_realtime_usage_stores_partitioned_text_tokens() -> None:
diff --git a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py
index 0cc564535ba..98e3af26719 100644
--- a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py
+++ b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py
@@ -1,69 +1,9 @@
-import json
-from functools import lru_cache
-from pathlib import Path
+from typing import Final
 
 import pytest
 
 import litellm
 
-REPO_ROOT = Path(__file__).parents[2]
-MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json"
-BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"
-
-FLEX_LONG_CONTEXT = {
-    "gpt-5.4": {
-        "input_cost_per_token_above_272k_tokens_flex": 2.5e-06,
-        "output_cost_per_token_above_272k_tokens_flex": 1.125e-05,
-        "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07,
-    },
-    "gpt-5.4-pro": {
-        "input_cost_per_token_above_272k_tokens_flex": 3e-05,
-        "output_cost_per_token_above_272k_tokens_flex": 0.000135,
-    },
-    "gpt-5.5": {
-        "input_cost_per_token_above_272k_tokens_flex": 5e-06,
-        "output_cost_per_token_above_272k_tokens_flex": 2.25e-05,
-        "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07,
-    },
-}
-
-PRIORITY_LONG_CONTEXT = {
-    "gpt-5.6": {
-        "input_cost_per_token_above_272k_tokens_priority": 1.6e-05,
-        "output_cost_per_token_above_272k_tokens_priority": 6e-05,
-        "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06,
-        "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05,
-    },
-    "gpt-5.6-sol": {
-        "input_cost_per_token_above_272k_tokens_priority": 1.6e-05,
-        "output_cost_per_token_above_272k_tokens_priority": 6e-05,
-        "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06,
-        "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05,
-    },
-    "gpt-5.6-terra": {
-        "input_cost_per_token_above_272k_tokens_priority": 8e-06,
-        "output_cost_per_token_above_272k_tokens_priority": 3.6e-05,
-        "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07,
-        "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05,
-    },
-    "gpt-5.6-luna": {
-        "input_cost_per_token_above_272k_tokens_priority": 8e-07,
-        "output_cost_per_token_above_272k_tokens_priority": 3.6e-06,
-        "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08,
-        "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06,
-    },
-    "gpt-6-astra": {
-        "input_cost_per_token_above_272k_tokens_priority": 4e-05,
-        "output_cost_per_token_above_272k_tokens_priority": 0.00015,
-        "cache_read_input_token_cost_above_272k_tokens_priority": 4e-06,
-        "cache_creation_input_token_cost_above_272k_tokens_priority": 5e-05,
-    },
-}
-
-EXPECTED = {**FLEX_LONG_CONTEXT, **PRIORITY_LONG_CONTEXT}
-
-NO_PUBLISHED_PRIORITY_LONG_CONTEXT = ("gpt-5.4", "gpt-5.5")
-
 
 @pytest.fixture(autouse=True)
 def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
@@ -72,30 +12,24 @@ def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
     litellm.add_known_models()
 
 
-@lru_cache(maxsize=2)
-def _load(path: Path) -> dict[str, dict[str, object]]:
-    with open(path) as f:
-        return json.load(f)
-
-
 LONG_CONTEXT_PROMPT_TOKENS = 300_000
 COMPLETION_TOKENS = 1_000
 
 TIERED_COST_CASES = [
-    ("gpt-5.4", "flex", 2.5e-06, 1.125e-05),
-    ("gpt-5.4-pro", "flex", 3e-05, 0.000135),
-    ("gpt-5.5", "flex", 5e-06, 2.25e-05),
-    ("gpt-5.6", "priority", 1.6e-05, 6e-05),
-    ("gpt-5.6-sol", "priority", 1.6e-05, 6e-05),
-    ("gpt-5.6-terra", "priority", 8e-06, 3.6e-05),
-    ("gpt-5.6-luna", "priority", 8e-07, 3.6e-06),
-    ("gpt-6-astra", "priority", 4e-05, 0.00015),
+    ("gpt-5.4", "flex"),
+    ("gpt-5.4-pro", "flex"),
+    ("gpt-5.5", "flex"),
+    ("gpt-5.6", "priority"),
+    ("gpt-5.6-sol", "priority"),
+    ("gpt-5.6-terra", "priority"),
+    ("gpt-5.6-luna", "priority"),
+    ("gpt-6-astra", "priority"),
 ]
 
 
-@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES)
+@pytest.mark.parametrize("model,tier", TIERED_COST_CASES)
 def test_cost_per_token_bills_long_context_at_the_tier_rate(
-    model: str, tier: str, input_rate: float, output_rate: float
+    model: str, tier: str
 ) -> None:
     """A prompt over 272K on flex or priority must bill at that tier's long-context rate."""
     input_cost, output_cost = litellm.cost_per_token(
@@ -104,5 +38,10 @@ def test_cost_per_token_bills_long_context_at_the_tier_rate(
         completion_tokens=COMPLETION_TOKENS,
         service_tier=tier,
     )
-    assert input_cost == pytest.approx(LONG_CONTEXT_PROMPT_TOKENS * input_rate)
-    assert output_cost == pytest.approx(COMPLETION_TOKENS * output_rate)
+    model_info: Final = litellm.model_cost[model]
+    assert input_cost == pytest.approx(
+        LONG_CONTEXT_PROMPT_TOKENS * model_info[f"input_cost_per_token_above_272k_tokens_{tier}"]
+    )
+    assert output_cost == pytest.approx(
+        COMPLETION_TOKENS * model_info[f"output_cost_per_token_above_272k_tokens_{tier}"]
+    )
diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py
index f3cd4618078..6aa800ced5b 100644
--- a/tests/test_litellm/test_video_generation.py
+++ b/tests/test_litellm/test_video_generation.py
@@ -264,8 +264,7 @@ class TestVideoGeneration:
             model="openai/sora-2", duration_seconds=10.0, custom_llm_provider="openai"
         )
 
-        # Should calculate cost based on duration (10 seconds * $0.10 per second = $1.00)
-        assert cost == 1.0
+        assert cost == pytest.approx(10.0 * litellm.model_cost["openai/sora-2"]["output_cost_per_video_per_second"])
 
     def test_video_generation_cost_calculation_unknown_model(self):
         """Test video generation cost calculation for unknown model."""