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Fix gemini 2.5 flash on Vertex AI #10189

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Fix gemini 2.5 flash on Vertex AI #10189

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58c2551
Gemini 2.5 Flash output cost is based on thinking enabled
awesie Apr 20, 2025
62ed5b2
Clean up Gemini thinking config
awesie Apr 20, 2025
f87f500
Fix completion_tokens on Vertex AI Gemini thinking models
awesie Apr 21, 2025
e546e5f
Add test for Gemini 2.5 Flash thinking off
awesie Apr 21, 2025
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4 changes: 4 additions & 0 deletions litellm/litellm_core_utils/llm_cost_calc/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,6 +128,10 @@ def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, fl
except Exception:
continue

output_cost_per_token_thinking = model_info.get("output_cost_per_token_thinking")
if usage.get("thinking_enabled") and output_cost_per_token_thinking is not None:
completion_base_cost = output_cost_per_token_thinking

return prompt_base_cost, completion_base_cost


Expand Down
38 changes: 28 additions & 10 deletions litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
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Expand Up @@ -365,17 +365,14 @@ def _map_reasoning_effort_to_thinking_budget(
if reasoning_effort == "low":
return {
"thinkingBudget": DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
"includeThoughts": True,
}
elif reasoning_effort == "medium":
return {
"thinkingBudget": DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
"includeThoughts": True,
}
elif reasoning_effort == "high":
return {
"thinkingBudget": DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
"includeThoughts": True,
}
else:
raise ValueError(f"Invalid reasoning effort: {reasoning_effort}")
Expand All @@ -388,9 +385,9 @@ def _map_thinking_param(
thinking_budget = thinking_param.get("budget_tokens")

params: GeminiThinkingConfig = {}
if thinking_enabled:
params["includeThoughts"] = True
if thinking_budget:
if not thinking_enabled:
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please add a unit test for this behaviour in here - https://github.com/BerriAI/litellm/blob/main/tests/litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py

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I wasn't really sure what you were looking for. Take a look at the test I added and let me know if you wanted something else.

params["thinkingBudget"] = 0
elif thinking_budget is not None:
params["thinkingBudget"] = thinking_budget

return params
Expand Down Expand Up @@ -743,6 +740,7 @@ def _handle_content_policy_violation(
def _calculate_usage(
self,
completion_response: GenerateContentResponseBody,
thinking_enabled: bool | None,
) -> Usage:
cached_tokens: Optional[int] = None
audio_tokens: Optional[int] = None
Expand All @@ -768,17 +766,24 @@ def _calculate_usage(
audio_tokens=audio_tokens,
text_tokens=text_tokens,
)
completion_tokens = completion_response["usageMetadata"].get(
"candidatesTokenCount", 0
)
if reasoning_tokens:
# Usage(...) constructor expects that completion_tokens includes the reasoning_tokens.
# However the Vertex AI usage metadata does not include reasoning tokens in candidatesTokenCount.
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is there any documentation / reference for this?

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Not that I'm immediately aware of. I didn't even know this was a problem until it was mentioned here: #10141 (comment). Once I looked at my logs and did manual testing, I confirmed the behavior for Vertex AI.

I have not tested the Gemini API myself.

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For example, with Gemini 2.5 Flash on Vertex AI:

  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 2622,
    "totalTokenCount": 4434,
    "trafficType": "ON_DEMAND",
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 10
      }
    ],
    "candidatesTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 2622
      }
    ],
    "thoughtsTokenCount": 1802
  },

As can be seen from the total token count, candidates token count does not include the thoughts token count (total = candidates + thoughts + prompt).

# Reportedly, this is different from the Gemini API.
completion_tokens += reasoning_tokens
## GET USAGE ##
usage = Usage(
prompt_tokens=completion_response["usageMetadata"].get(
"promptTokenCount", 0
),
completion_tokens=completion_response["usageMetadata"].get(
"candidatesTokenCount", 0
),
completion_tokens=completion_tokens,
total_tokens=completion_response["usageMetadata"].get("totalTokenCount", 0),
prompt_tokens_details=prompt_tokens_details,
reasoning_tokens=reasoning_tokens,
thinking_enabled=thinking_enabled,
)

return usage
Expand Down Expand Up @@ -910,6 +915,16 @@ def transform_response(
completion_response=completion_response,
)

thinking_enabled = None
if "gemini-2.5-flash" in model:
# Only Gemini 2.5 Flash can have its thinking disabled by setting the thinking budget to zero
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what happens on gemini-2.5-pro? so if you send it thinking budget = 0

  • what is the response from gemini-2.5-flash vs. gemini-2.5-pro?

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When I compared the behavior of gemini-2.5-flash and gemini-2.5-pro, setting the thinking budget to 0 only had an effect on gemini-2.5-flash.

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Maybe its related. Can you kindly confirm?

Using gemini as per docs at https://docs.litellm.ai/docs/tutorials/openai_codex
codex -m gemini-2.0-flash --full-auto
with a prompt that attempts to use a screenshot / image
Generate a web app with the backing fastapi based backend that mimics the a.png in this folder

I get the below error

litellm.exceptions.APIConnectionError: litellm.APIConnectionError: Invalid user message={'role': 'user', 'content': [{'type': 'text', 'text': 'Generate a web app with the backing fastapi based backend that mimics the  in this folder'}, {'type': 'image', 'text': None}]} at index 1. Please ensure all user messages are valid OpenAI chat completion messages.
Traceback (most recent call last):
  File "/Users/vichandrasekharan/code/temp/codex/.venv/lib/python3.10/site-packages/litellm/utils.py", line 6315, in validate_chat_completion_user_messages
    raise Exception("invalid content type")
Exception: invalid content type

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@vinaynair this is an unrelated error message.

It is because of this

{'type': 'image', 'text': None}

Which does look like invalid input

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please file a separate ticket for a feature request, where we filter this scenario

thinking_budget = (
request_data.get("generationConfig", {})
.get("thinkingConfig", {})
.get("thinkingBudget")
)
thinking_enabled = thinking_budget != 0

model_response.choices = []

try:
Expand All @@ -923,7 +938,10 @@ def transform_response(
_candidates, model_response, litellm_params
)

usage = self._calculate_usage(completion_response=completion_response)
usage = self._calculate_usage(
completion_response=completion_response,
thinking_enabled=thinking_enabled,
)
setattr(model_response, "usage", usage)

## ADD METADATA TO RESPONSE ##
Expand Down
4 changes: 2 additions & 2 deletions litellm/model_prices_and_context_window_backup.json
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Original file line number Diff line number Diff line change
Expand Up @@ -5413,7 +5413,7 @@
"input_cost_per_audio_token": 1e-6,
"input_cost_per_token": 0.15e-6,
"output_cost_per_token": 0.6e-6,
"output_cost_per_reasoning_token": 3.5e-6,
"output_cost_per_token_thinking": 3.5e-6,
"litellm_provider": "gemini",
"mode": "chat",
"rpm": 10,
Expand Down Expand Up @@ -5443,7 +5443,7 @@
"input_cost_per_audio_token": 1e-6,
"input_cost_per_token": 0.15e-6,
"output_cost_per_token": 0.6e-6,
"output_cost_per_reasoning_token": 3.5e-6,
"output_cost_per_token_thinking": 3.5e-6,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_reasoning": true,
Expand Down
3 changes: 3 additions & 0 deletions litellm/types/utils.py
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Expand Up @@ -138,6 +138,9 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
input_cost_per_token_batches: Optional[float]
output_cost_per_token_batches: Optional[float]
output_cost_per_token: Required[float]
output_cost_per_token_thinking: Optional[
float
] # only for vertex ai gemini-2.5-flash models
output_cost_per_character: Optional[float] # only for vertex ai models
output_cost_per_audio_token: Optional[float]
output_cost_per_token_above_128k_tokens: Optional[
Expand Down
3 changes: 3 additions & 0 deletions litellm/utils.py
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Expand Up @@ -4557,6 +4557,9 @@ def _get_model_info_helper( # noqa: PLR0915
"output_cost_per_token_batches"
),
output_cost_per_token=_output_cost_per_token,
output_cost_per_token_thinking=_model_info.get(
"output_cost_per_token_thinking", None
),
output_cost_per_audio_token=_model_info.get(
"output_cost_per_audio_token", None
),
Expand Down
4 changes: 2 additions & 2 deletions model_prices_and_context_window.json
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Original file line number Diff line number Diff line change
Expand Up @@ -5413,7 +5413,7 @@
"input_cost_per_audio_token": 1e-6,
"input_cost_per_token": 0.15e-6,
"output_cost_per_token": 0.6e-6,
"output_cost_per_reasoning_token": 3.5e-6,
"output_cost_per_token_thinking": 3.5e-6,
"litellm_provider": "gemini",
"mode": "chat",
"rpm": 10,
Expand Down Expand Up @@ -5443,7 +5443,7 @@
"input_cost_per_audio_token": 1e-6,
"input_cost_per_token": 0.15e-6,
"output_cost_per_token": 0.6e-6,
"output_cost_per_reasoning_token": 3.5e-6,
"output_cost_per_token_thinking": 3.5e-6,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_reasoning": true,
Expand Down
49 changes: 44 additions & 5 deletions tests/litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
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Expand Up @@ -87,6 +87,49 @@ def test_reasoning_tokens_gemini():
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=None, cached_tokens=None, text_tokens=17, image_tokens=None
),
thinking_enabled=True,
)
model_cost_map = litellm.model_cost[model]
prompt_cost, completion_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
)

assert round(prompt_cost, 10) == round(
model_cost_map["input_cost_per_token"] * usage.prompt_tokens,
10,
)
assert round(completion_cost, 10) == round(
(
model_cost_map["output_cost_per_token_thinking"]
* usage.completion_tokens
),
10,
)


def test_reasoning_disabled_tokens_gemini():
model = "gemini-2.5-flash-preview-04-17"
custom_llm_provider = "gemini"
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")

usage = Usage(
completion_tokens=1578,
prompt_tokens=17,
total_tokens=1595,
completion_tokens_details=CompletionTokensDetailsWrapper(
accepted_prediction_tokens=None,
audio_tokens=None,
reasoning_tokens=None,
rejected_prediction_tokens=None,
text_tokens=1578,
),
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=None, cached_tokens=None, text_tokens=17, image_tokens=None
),
thinking_enabled=False,
)
model_cost_map = litellm.model_cost[model]
prompt_cost, completion_cost = generic_cost_per_token(
Expand All @@ -102,11 +145,7 @@ def test_reasoning_tokens_gemini():
assert round(completion_cost, 10) == round(
(
model_cost_map["output_cost_per_token"]
* usage.completion_tokens_details.text_tokens
)
+ (
model_cost_map["output_cost_per_reasoning_token"]
* usage.completion_tokens_details.reasoning_tokens
* usage.completion_tokens
),
10,
)
Expand Down
56 changes: 56 additions & 0 deletions tests/litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
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Expand Up @@ -259,3 +259,59 @@ def test_vertex_ai_empty_content():
content, reasoning_content = v.get_assistant_content_message(parts=parts)
assert content is None
assert reasoning_content is None


def test_vertex_ai_thinking_disabled():
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
VertexGeminiConfig,
)
from litellm.types.llms.anthropic import AnthropicThinkingParam

v = VertexGeminiConfig()
optional_params = v.map_openai_params(
non_default_params={
"thinking": AnthropicThinkingParam(type="enabled", budget_tokens=0),
},
optional_params={},
model="gemini-2.5-flash-preview-04-17",
drop_params=False,
)
assert optional_params["thinkingConfig"]["thinkingBudget"] == 0

optional_params = v.map_openai_params(
non_default_params={
"thinking": AnthropicThinkingParam(type="enabled"),
},
optional_params={},
model="gemini-2.5-flash-preview-04-17",
drop_params=False,
)
assert "thinkingBudget" not in optional_params["thinkingConfig"]

optional_params = v.map_openai_params(
non_default_params={
"thinking": AnthropicThinkingParam(type="enabled", budget_tokens=1024),
},
optional_params={},
model="gemini-2.5-flash-preview-04-17",
drop_params=False,
)
assert optional_params["thinkingConfig"]["thinkingBudget"] == 1024

optional_params = v.map_openai_params(
non_default_params={
"thinking": cast(AnthropicThinkingParam, {"type": "invalid"}),
},
optional_params={},
model="gemini-2.5-flash-preview-04-17",
drop_params=False,
)
assert optional_params["thinkingConfig"]["thinkingBudget"] == 0

optional_params = v.map_openai_params(
non_default_params={},
optional_params={},
model="gemini-2.5-flash-preview-04-17",
drop_params=False,
)
assert "thinkingConfig" not in optional_params
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