pixelprompt 0.4.0

Compress LLM context by rendering text as optimized images

PixelPrompt

Compress LLM context by rendering text as optimized images. Based on the research paper "Pixels Beat Tokens" (Venturi, 2026).

Why PixelPrompt?

When working with LLMs, token counts directly impact cost and latency. PixelPrompt converts text content into visually optimized PNG images, achieving 38-80% net cost savings compared to raw text tokens, with 100% accuracy on a 125-question benchmark (Claude Opus 4.6).

Key benefits:

• 💰 38-80% net cost savings — input token reduction minus output cost, with optimized prompts
• 🎯 100% accuracy — verified across prose, code, JSON, and config content types
• 📊 Content-type presets — optimal settings for prose, code, JSON, and config files
• 🔄 Automatic splitting — large content automatically split across multiple images
• 📝 Prompt optimization — built-in helpers to eliminate output token inflation
• 🚀 Easy integration — simple API for any Claude workflow

Installation

pip install pixelprompt

Quick Start

from pixelprompt import PixelPrompt, RenderConfig

# Use content-type presets for optimal settings
pxl = PixelPrompt(RenderConfig.for_content("json"))
images = pxl.render(json_data)

# Or use default settings (good for prose)
pxl = PixelPrompt()
images = pxl.render("Your long context here...")

# Use with Claude API
from anthropic import Anthropic

client = Anthropic()
message = client.messages.create(
    model="claude-opus-4-6-20250219",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                *[img.to_content_block() for img in images],
                {
                    "type": "text",
                    "text": "What are the key points? Answer with ONLY the answer value. No explanation, no preamble."
                }
            ]
        }
    ]
)

Content-Type Presets

Different content types have different optimal rendering settings. Use presets for best results:

from pixelprompt import RenderConfig, CONTENT_PRESETS

# Prose: font 9, minify=True — 69% net savings
config = RenderConfig.for_content("prose")

# JSON: font 9, minify=True (compact JSON) — 80% net savings
config = RenderConfig.for_content("json")

# Code: font 7, minify=False — 59% net savings
config = RenderConfig.for_content("code")

# Config (YAML, TOML, INI): font 8, minify=False — 39% net savings
config = RenderConfig.for_content("config")

Content Type	Font Size	Minify	Input Savings	Net Savings
JSON	9	Yes	83%	80%
Prose	9	Yes	71%	69%
Code	7	No	61%	59%
Config	8	No	41%	39%

Prompt Optimization

Critical: Without prompt optimization, image-mode responses are 2-4x more verbose, eating into your savings. Use the built-in helpers:

from pixelprompt import optimize_prompt, image_query

# Wrap any prompt with output-suppression
prompt = optimize_prompt("What is the server port?")
# → "What is the server port? Answer with ONLY the answer value. No explanation, no preamble."

# Build a complete image-query prompt
prompt = image_query("What functions are defined?", style="concise")
# → "Based on the content shown in the image(s): What functions are defined? Answer with ONLY the answer value. No explanation, no preamble."

Available styles: "concise" (default), "extract", "structured", "none".

Cost Comparison

Compare text vs image costs before committing:

pxl = PixelPrompt(RenderConfig.for_content("json"))
result = pxl.compare(json_data, model="claude-opus-4-6")

print(f"Text tokens: {result['text_tokens']}")
print(f"Image tokens: {result['image_tokens']}")
print(f"Input savings: {result['input_savings_pct']}%")

Configuration

from pixelprompt import PixelPrompt, RenderConfig

config = RenderConfig(
    font_size=9,           # Range: 6-20, default: 9
    font_family="monospace",  # "monospace", "serif", "sans-serif"
    max_width=1568,        # Max image width (Claude vision max: 1568)
    max_height=1568,       # Max image height (Claude vision max: 1568)
    dynamic_width=True,    # Fit width to content (saves tokens)
    dynamic_height=True,   # Fit height to content (saves tokens)
    minify=True,           # Strip markdown formatting for density
    content_type=None,     # Or use RenderConfig.for_content()
    padding=5,             # Minimal padding for density
    line_spacing=1,        # Tight line spacing
)

pxl = PixelPrompt(config=config)

API Reference

PixelPrompt

class PixelPrompt:
    def __init__(self, config: RenderConfig | None = None): ...
    def render(self, text: str) -> list[RenderedImage]: ...
    def compare(self, text: str, model: str = "claude-opus-4-6") -> dict: ...

    @staticmethod
    def minify_text(text: str) -> str: ...

    @staticmethod
    def compact_json(text: str) -> str: ...

RenderConfig

@dataclass
class RenderConfig:
    font_size: int = 9
    font_family: str = "monospace"
    max_width: int = 1568
    max_height: int = 1568
    dynamic_width: bool = True
    dynamic_height: bool = True
    minify: bool = True
    content_type: str | None = None

    @staticmethod
    def for_content(content_type: str) -> RenderConfig: ...

RenderedImage

class RenderedImage:
    width: int          # Image width in pixels
    height: int         # Image height in pixels
    tokens: int         # Estimated Claude vision token cost
    size_bytes: int     # PNG file size in bytes

    def png_bytes(self) -> bytes: ...
    def base64(self) -> str: ...
    def to_content_block(self) -> dict: ...  # Anthropic API format
    def save(self, path: str) -> None: ...

Prompt Helpers

def optimize_prompt(prompt: str, style: str = "concise") -> str: ...
def image_query(question: str, *, style: str = "concise", context_instruction: str | None = None) -> str: ...

Performance

Net cost savings by content type (Opus 4.6, with optimized prompts):

Content Type	Net Savings	Notes
JSON/Structured	80%	Compact JSON + high density
Long Prose	69%	Minification + word wrap
Source Code	59%	Smaller font, preserve formatting
Config Files	39%	Moderate density

Rendering time: ~100-200ms per image on modern hardware.

License

MIT License — see LICENSE file for details.

Citation

If you use PixelPrompt in research, please cite:

@software{pixelprompt,
  author = {Venturi, Gabriele},
  title = {PixelPrompt: Compress LLM Context by Rendering Text as Images},
  year = {2026},
  publisher = {Sinaptik GmbH},
  url = {https://github.com/sinaptik-ai/pixelprompt}
}