Cross-Model Compatibility
All major multimodal LLMs — Claude, Gemini, GPT-5.6, and Grok — can read text from images. The 5×8 bitmap atlas produces clean, high-contrast output that every vision encoder handles well. You can generate your dense PNGs once and use them across all providers.
Gemini 3.1 Pro: Token Efficiency
Google's Gemini models process images differently from Claude. Gemini uses a proprietary vision tokenizer that tends to produce slightly fewer tokens per image tile. In our testing, a 1000×720 pxpipe page costs approximately 1,100 tokens in Gemini versus 1,334 in Claude — making Gemini slightly more cost-effective for image-based workflows.
GPT-5.6: High and Low Detail Modes
OpenAI offers two image detail modes: 'high' and 'low.' In low detail mode, any image costs a flat 85 tokens regardless of content. In high detail mode, the image is tiled similarly to Claude. For dense text reading, high detail is required, but the flat-rate low detail mode can be useful for quick summaries of shorter payloads.
Grok 4.5: xAI Vision Capabilities
Grok 4.5 supports image inputs through the xAI API. While pricing details differ from Anthropic, the same principle applies: dense PNGs pack more data per image token than raw text tokenization. Grok's vision encoder handles the 5×8 atlas cleanly in our testing.
Best Practices Across Models
Keep images under 1500 pixels wide to avoid unnecessary tile splits. Use grayscale PNG format consistently. Include a brief text prompt describing what the image contains — this helps all models orient their reading. Test with a small sample before committing to large batch processing.
Sending a Dense PNG to OpenAI GPT-5.6
from openai import OpenAI
import base64
client = OpenAI()
with open("output_page_1.png", "rb") as f:
img_b64 = base64.b64encode(f.read()).decode()
response = client.chat.completions.create(
model="GPT-5.6",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Review this code for security vulnerabilities."},
{"type": "image_url", "image_url": {
"url": f"data:image/png;base64,{img_b64}",
"detail": "high"
}}
]
}]
)
print(response.choices[0].message.content)
Frequently Asked Questions
Conclusion
Visual token encoding is a practical, immediately deployable optimization that works with your existing AI stack. Whether you are a developer building pipelines or a founder managing API budgets, the ~88% input cost reduction compounds into real savings that improve your margins and extend your runway.
Ready to try it? Open PXINK and drop a file to see your savings instantly.