Skip to main content
The Gemini native format accepts images, audio, and video directly for understanding and analysis, and ships a built-in code_execution tool that runs Python in a sandbox. Examples below assume the client setup from Native Calls.
Two hard limits on the APIYI channel:
  1. The Files API is not supported (client.files.upload() works only on Google’s official endpoint) — media must be passed inline
  2. Inline media is capped at 20MB per file; compress or extract frames first if larger

​
Image Understanding

Pass a PIL Image directly — the SDK handles encoding: 
from google import genai
from PIL import Image

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options={"base_url": "https://api.apiyi.com"}
)

img = Image.open("photo.jpg")

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=[
        "Describe this image in detail: key elements, colors, composition.",
        img
    ]
)
print(response.text)
Or pass bytes explicitly with types.Part.from_bytes: 
from google.genai import types

with open("photo.jpg", "rb") as f:
    image_bytes = f.read()

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=[
        types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"),
        "What's in this image?"
    ]
)

​
Audio Understanding

from google.genai import types

with open("meeting.mp3", "rb") as f:
    audio_bytes = f.read()

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=[
        types.Part.from_bytes(data=audio_bytes, mime_type="audio/mp3"),
        "Transcribe this audio and summarize the main topics."
    ]
)
print(response.text)

​
Video Understanding

from google.genai import types

with open("demo.mp4", "rb") as f:
    video_bytes = f.read()

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=[
        types.Part.from_bytes(data=video_bytes, mime_type="video/mp4"),
        "Summarize the main content and key information of this video."
    ]
)
print(response.text)
Video is tokenized per frame plus the audio track — longer videos get expensive. More video workflows: Video Understanding.

​
Cost Control with media_resolution

Token consumption for media scales with resolution. For “rough look” tasks (classification, presence checks), lower resolution saves real money: 
from google.genai import types

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents=["What's the theme of this image?", img],
    config=types.GenerateContentConfig(
        media_resolution="MEDIA_RESOLUTION_LOW"  # LOW / MEDIUM / HIGH
    )
)
LevelUse for
LOWClassification, coarse recognition — cheapest
MEDIUMGeneral description and understanding (balanced default)
HIGHOCR, small text, detail-dense tasks

​
Supported Formats

TypeFormatsHow to pass
ImagesJPG, PNG, WebPPIL Image or Part.from_bytes
AudioMP3, WAVPart.from_bytes
VideoMP4, MOVPart.from_bytes
All inline, max 20MB per file.

​
Code Execution

Declare the code_execution tool and the model writes Python, runs it in a sandbox, and answers based on the result — ideal for calculations and data analysis: 
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="""
Sales data: Product A 100 units × \$50, Product B 200 × \$30, Product C 150 × \$40.
Compute total revenue, average unit price, and each product's revenue share.
""",
    config={"tools": [{"code_execution": {}}]}
)

for part in response.candidates[0].content.parts:
    if getattr(part, "executable_code", None):
        print(f"[Code executed]\n{part.executable_code.code}")
    if getattr(part, "code_execution_result", None):
        print(f"[Result]\n{part.code_execution_result.output}")
    if getattr(part, "text", None):
        print(f"[Explanation]\n{part.text}")
Code execution limits: Python only; the sandbox has no network or filesystem access; execution time is capped. To call your own external services, use Function Calling.