From e7e1aa898478ae7d85785f2245132ac02b6f2e63 Mon Sep 17 00:00:00 2001
From: "devin-ai-integration[bot]"
 <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Wed, 21 Oct 2026 00:10:05 +0000
Subject: [PATCH] fix(vertex_ai): bill per-character text embeddings on
 Google's billableCharacterCount (#47460)

Backport of #47460 to stable/1.103.x

Cherry-picked from d29200fa0511ae50f41b692de5922659a1f176c0 (main)
---
 .../litellm_core_utils/llm_cost_calc/utils.py |  8 ++++-
 .../vertex_embeddings/transformation.py       | 34 +++++++++++++++++--
 .../llm_cost_calc/test_llm_cost_calc_utils.py | 25 ++++++++++++++
 3 files changed, 63 insertions(+), 4 deletions(-)

diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index 74e4c3c669c..5892f446787 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -955,8 +955,14 @@ def _calculate_input_cost(
 ) -> float:
     """
     Calculates the input cost for a given model, prompt tokens, and completion tokens.
+
+    A model priced per character (`input_cost_per_character`) bills the reported character count
+    in place of its text tokens, so the same prompt is never charged under both units.
     """
-    prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost
+    bills_characters: Final = (
+        prompt_tokens_details["character_count"] > 0 and model_info.get("input_cost_per_character") is not None
+    )
+    prompt_cost = 0.0 if bills_characters else float(prompt_tokens_details["text_tokens"]) * prompt_base_cost
 
     ### CACHE READ COST - Now uses tiered pricing
     cache_hit_audio_tokens: Final = prompt_tokens_details["cache_hit_audio_tokens"]
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
index e4bbdd1bd0d..e4f7c513eb9 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py
@@ -1,14 +1,34 @@
 import types
 from typing import Final, Literal
 
-from pydantic import BaseModel
+from pydantic import BaseModel, TypeAdapter
+from typing_extensions import ReadOnly, TypedDict
 
 from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels
-from litellm.types.utils import EmbeddingResponse, Usage
+from litellm.types.utils import EmbeddingResponse, PromptTokensDetailsWrapper, Usage
 
 from .types import *
 
 
+class _PredictMetadata(TypedDict, total=False):
+    billableCharacterCount: ReadOnly[int]
+
+
+class _PredictResponseMetadata(TypedDict, total=False):
+    metadata: ReadOnly[_PredictMetadata]
+
+
+_PREDICT_RESPONSE_METADATA: Final = TypeAdapter(_PredictResponseMetadata)
+
+
+def billable_character_count(response: _PredictResponseMetadata) -> int | None:
+    """
+    `metadata.billableCharacterCount` of a Vertex text embedding predict response, the count Google
+    bills per-character embedding models (text-embedding-005, text-multilingual-embedding-002) on.
+    """
+    return response.get("metadata", {}).get("billableCharacterCount")
+
+
 class VertexAITextEmbeddingConfig(BaseModel):
     """
     Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
@@ -248,7 +268,15 @@ class VertexAITextEmbeddingConfig(BaseModel):
         model_response.object = "list"
         model_response.data = embedding_response
         model_response.model = model
-        usage: Final = Usage(prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens)
+        billable_characters: Final = billable_character_count(_PREDICT_RESPONSE_METADATA.validate_python(response))
+        usage: Final = Usage(
+            prompt_tokens=input_tokens,
+            completion_tokens=0,
+            total_tokens=input_tokens,
+            prompt_tokens_details=(
+                None if billable_characters is None else PromptTokensDetailsWrapper(character_count=billable_characters)
+            ),
+        )
         setattr(model_response, "usage", usage)
         return model_response
 
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index 656585820d9..893d6105921 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -97,6 +97,31 @@ def test_generic_cost_per_token_bills_cache_reads_at_input_rate_when_no_cache_re
     assert completion_cost == pytest.approx(380 * 9.7e-7)
 
 
+@pytest.mark.parametrize(
+    ("model_info", "expected_prompt_cost"),
+    [
+        ({"input_cost_per_token": 1e-7, "input_cost_per_character": 2.5e-8, "output_cost_per_token": 0.0}, 35 * 2.5e-8),
+        ({"input_cost_per_token": 1.5e-7, "output_cost_per_token": 0.0}, 11 * 1.5e-7),
+    ],
+    ids=["character_priced_model_bills_characters_instead_of_tokens", "token_priced_model_ignores_the_character_count"],
+)
+def test_generic_cost_per_token_bills_a_character_counted_prompt_under_one_unit(
+    model_info: ModelInfo, expected_prompt_cost: float
+) -> None:
+    usage = Usage(
+        prompt_tokens=11,
+        completion_tokens=0,
+        total_tokens=11,
+        prompt_tokens_details=PromptTokensDetailsWrapper(character_count=35),
+    )
+
+    prompt_cost, completion_cost = generic_cost_per_token(
+        model="embedding-fixture", usage=usage, custom_llm_provider="vertex_ai", model_info=model_info
+    )
+
+    assert (prompt_cost, completion_cost) == (pytest.approx(expected_prompt_cost), 0.0)
+
+
 def test_apply_provider_cache_read_default_only_derives_a_rate_for_fireworks() -> None:
     openai_info: ModelInfo = {"input_cost_per_token": 2e-6}
     fireworks_info: ModelInfo = {"input_cost_per_token": 2e-6}