[Bug/Question] Significant Image Token Count Discrepancy between Vertex AI and Gemini API backends

### **Description**

I have noticed a significant discrepancy in token usage calculation for the same image when using the google-genai SDK, depending on whether the backend is **Vertex AI** or the **Gemini API** (AI Studio).

Using the same model (`gemini-2.5-flash`) and the same input image ($700 \times 1003$), the token counts differ drastically:
- Gemini API: **~258 tokens**.
- Vertex AI: **~1806 tokens**.

### **Environment details**

- **SDK version**: google-genai 1.12.1
- **Python version**: 3.10.16

### **Reproduction Code**

```python
import json
import os
import rich
from google import genai
from google.oauth2 import service_account
from PIL import Image

def get_gemini_client(vertexai: bool = False):
if vertexai:
# Assumes credentials.json is present
credentials = json.loads(open("credentials.json").read())
return genai.Client(
vertexai=True,
project=os.getenv("GOOGLE_CLOUD_PROJECT"),
location="us-central1", # or global
credentials=service_account.Credentials.from_service_account_info(
credentials,
scopes=["https://www.googleapis.com/auth/cloud-platform"],
),
)
else:
return genai.Client(
api_key=os.getenv("GEMINI_API_KEY"),
)

if __name__ == "__main__":
# Test image size: (700, 1003)
image = Image.open("test.png")
print(f"Image size: {image.size}")

model = "gemini-2.5-flash"

print("-" * 100)
print("Vertex AI:")
vertex_client = get_gemini_client(vertexai=True)
vertex_count = vertex_client.models.count_tokens(model=model, contents=[image])
print(f"Token count (Vertex): {vertex_count}")

vertex_response = vertex_client.models.generate_content(
model=model,
contents=[image, "Describe the image in detail."],
)
print("Vertex AI usage metadata:")
rich.print(vertex_response.usage_metadata.prompt_tokens_details)

print("-" * 100)
print("Gemini API:")
gemini_client = get_gemini_client(vertexai=False)
gemini_count = gemini_client.models.count_tokens(model=model, contents=[image])
print(f"Token count (Gemini API): {gemini_count}")

gemini_response = gemini_client.models.generate_content(
model=model,
contents=[image, "Describe the image in detail."],
)
print("Gemini API usage metadata:")
rich.print(gemini_response.usage_metadata.prompt_tokens_details)
print("-" * 100)
```

### **Logs / Output**
```
Image size: (700, 1003)
----------------------------------------------------------------------------------------------------
Vertex AI:
Token count (Vertex): total_tokens=1806 cached_content_token_count=None
Vertex AI usage metadata:
[
ModalityTokenCount(modality=, token_count=1806),
ModalityTokenCount(modality=, token_count=6)
]
----------------------------------------------------------------------------------------------------
Gemini API:
Token count (Gemini API): total_tokens=259 cached_content_token_count=None
Gemini API usage metadata:
[
ModalityTokenCount(modality=, token_count=7),
ModalityTokenCount(modality=, token_count=258)
]
----------------------------------------------------------------------------------------------------
```

### **Analysis**

According to the official documentation for Token Calculation (Gemini 2.0/2.5): 258 tokens if both dimensions <= 384 pixels. Larger images are tiled into 768x768 pixel tiles, each costing 258 tokens.

For an image of size 700x1003:
- Vertex AI returns 1806 tokens. Since $1806 / 258 = 7$, Vertex seems to be breaking the image into 7 tiles.
- Gemini API returns 258 tokens. This implies it is treating the image as a single tile, possibly resizing it heavily or calculating differently.

**Expected Behavior:** I expect consistency between the two backends for the same model and image. If the tiling logic is standard, both should return similar token counts. Currently, using Vertex AI is **~7x more expensive** for the exact same inference request.

Could you clarify if this is intended behavior, a bug in the Vertex implementation of the tokenizer, or an issue with how the SDK handles images for different clients?