Vision

Cura 1T reasons over clinical images natively — its MedXpertQA multimodal score (72.2%) comes from expert-level cases pairing diverse clinical images with patient records and examination results. Images ride in the messages array as image_url content parts.

Cura 1T is a research model, not a medical service, and not a substitute for a clinician. Benchmark scores do not establish safety for unsupervised clinical use.

Base64 upload

The most common path for PHI-sensitive workloads: encode the image and inline it as a data: URI — nothing needs to be publicly hosted.

import base64import osfrom openai import OpenAI client = OpenAI(api_key=os.environ["ACTAVA_API_KEY"], base_url="https://inference.actava.ai/v1") with open("cxr.png", "rb") as f:    image_b64 = base64.b64encode(f.read()).decode() response = client.chat.completions.create(    model="actava/cura-soar",    messages=[        {            "role": "user",            "content": [                {"type": "text", "text": "Describe the abnormality in this chest X-ray."},                {                    "type": "image_url",                    "image_url": {"url": f"data:image/png;base64,{image_b64}"},                },            ],        }    ],)print(response.choices[0].message.content)

URL reference

Alternatively pass an https URL the API can fetch:

JSON
{  "type": "image_url",  "image_url": { "url": "https://your-host.example/scans/cxr-1042.png" }}

Best practices

  • Mix text and images freely — a content array can interleave several text and image_url parts (e.g. current + prior study for comparison reads).
  • Ask a focused question per image. "Describe the abnormality" outperforms an unconstrained "read this scan".
  • Image tokens count as input tokens and are billed at the standard input rate.
  • De-identify before sending. Strip PHI burned into image headers or overlays — Cura 1T is a research model.
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