vision-bench

name	vision-bench
description	Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.

Vision Bench — LLM Image Evaluation

Compare images by scoring them with one or more vision LLM judges against structured rubric criteria.

Quick Start

# Install dependencies
pip install pyyaml openai anthropic mistralai

# Score a single image
python bench.py image.png --criteria photorealism --judge gemini-2.5-flash

# Compare two AI-generated images
python bench.py img_a.png img_b.png \
  --criteria text_to_image \
  --prompt "a fox in a snowy forest" \
  --judge gpt-4o

# Multi-judge consensus
python bench.py img.png \
  --criteria portrait \
  --judges gpt-4o gemini-2.5-flash claude-opus-4-5-20251022

# OpenRouter models (any vision-capable model)
python bench.py img_a.png img_b.png \
  --criteria artistic_style \
  --judges "openrouter/meta-llama/llama-4-maverick" "openrouter/mistralai/pixtral-large-2411"

# List all presets
python bench.py --list-presets

# Save report to file
python bench.py img.png --criteria chart_analysis --save report.md

Presets

Preset	Use Case
text_to_image	Compare AI image generators (Midjourney, DALL-E, Flux)
photorealism	How convincingly an image looks like a photo
artistic_style	Style consistency, composition, color harmony
portrait	AI-generated portrait quality and realism
product_photo	E-commerce product image quality
document_ocr	Document text extraction and layout understanding
chart_analysis	Chart and data visualization comprehension
invoice	Financial document field extraction accuracy
ui_screenshot	App/web screenshot understanding
scientific	Scientific/medical image accuracy
alt_text	Accessibility image description quality

Custom criteria: pass any .yaml file as --criteria path/to/my.yaml.

Judge Providers

Prefix	Provider	Example
gpt-, o1, o3, o4	OpenAI	gpt-4o
claude-	Anthropic	claude-sonnet-4-5-20251022
gemini-	Google Gemini	gemini-2.5-flash
pixtral-, mistral-, ministral-	Mistral	pixtral-12b-2409
openrouter/	OpenRouter (any model)	openrouter/meta-llama/llama-4-maverick

API Keys

Keys are loaded from secrets.enc.yaml (SOPS + age encrypted) with fallback to environment variables.

Supported keys: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, OPENROUTER_API_KEY

To encrypt your own keys:

sops --config .sops.yaml --encrypt --input-type yaml --output-type yaml secrets.yaml > secrets.enc.yaml

Output Formats

--output markdown (default) · --output json · --output table

Files

• bench.py — CLI entry point
• judge.py — Multi-provider LLM judge logic
• report.py — Report generation
• vault.py — SOPS secrets decryption
• criteria/ — 11 YAML preset files
• .sops.yaml — Age key config for encryption
• secrets.enc.yaml — Encrypted API keys