Image Inputs

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Usage

from cerebras.cloud.sdk import Cerebras
import os
import base64

client = Cerebras(api_key=os.environ.get("CEREBRAS_API_KEY"))

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image = encode_image("screenshot.png")

response = client.chat.completions.create(
    model="gemma-4-31b",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image in one concise sentence."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{base64_image}"
                    },
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

from cerebras.cloud.sdk import Cerebras
import os
import base64

client = Cerebras(api_key=os.environ.get("CEREBRAS_API_KEY"))

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image_1 = encode_image("image1.jpeg")
base64_image_2 = encode_image("image2.png")

response = client.chat.completions.create(
    model="gemma-4-31b",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Compare these two images."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64_image_1}"
                    },
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{base64_image_2}"
                    },
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

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Input Requirements

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Token Usage

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Estimate Token Count

1. Start with the input width and height.
2. Compute the scale factor:
   scale = sqrt(645120 / (width × height))
   
3. Multiply the width and height by the scale factor.
   scaled_width = width × scale
   scaled_height = height × scale
   
4. Round each processed dimension down to the nearest multiple of 48.
5. Compute the token count:
   image_tokens = (processed_width / 48) × (processed_height / 48)
   
6. Cap the result at 280.

• 280 is the maximum image tokens per image for gemma-4-31b on Cerebras.
• Compressed file size does not directly determine token count. Processed image dimensions matter more than PNG or JPEG byte size.
• Image tokens are added to text prompt tokens and reported together in prompt_tokens in the API response.
• Image tokens occupy part of the model context window, just like text prompt tokens.

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Limitations

• Medical images — not suitable for interpreting specialized medical images such as CT scans or MRIs. Do not use for medical diagnosis or advice.
• Small text — may have difficulty reading small or low-resolution text. Enlarging text within the image before sending can improve results.
• Rotated content — may misinterpret text or images that are rotated or upside-down.
• Graphs and charts — may struggle to distinguish visual elements that differ only in color or line style, such as solid versus dashed lines.
• Spatial reasoning — not reliable for tasks requiring precise spatial localization, such as identifying positions on a map or board game.
• Object counting — the model may give approximate counts for objects in images.
• Image shape — may perform less accurately on panoramic or fisheye images.
• Preprocessing — the model cannot access original filenames or metadata. Images may be resized before analysis — see Token Usage for details.
• Accuracy — the model may generate inaccurate descriptions or captions in some scenarios. Verify outputs for high-stakes use cases.
• CAPTCHAs — CAPTCHA images are not supported.
• Indirect prompt injection — text embedded in an image is included in the model’s prompt context alongside the user’s text. If an image contains adversarial instructions (for example, text that says “ignore all previous instructions”) and the user prompt asks the model to answer based on the image, the model may follow those embedded instructions. Treat image content from untrusted sources as untrusted input, and use a system prompt to constrain the model’s behavior when processing images you don’t control.
• Untrusted output — the model may transcribe or describe text from an image verbatim, including HTML, script tags, URLs, or control characters. The API returns this content unmodified. Treat it the same as any other untrusted input before rendering, logging, or executing it in your application.

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FAQs