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

vision-bench