What this is
A practical guide for anyone comparing AI models — pricing, capabilities, and benchmark results.
VENICE ATLAS · COLOPHON · THE GUIDE
About Venice Atlas
A guide to
model evidence
Venice Atlas is an independent guide to the models available through Venice AI and the published evidence that helps put them in context.
What this is not
It is an independent project, not an endorsement from Venice AI or LLM-Stats. Benchmark results do not guarantee how a model will perform in your work.
Where the information comes from
Venice AI provides model details and prices. LLM-Stats provides benchmark results and category ratings. Atlas brings them together so you can compare the models available through Venice.
Reading the showcase
Showcase examples show what each model can produce under specific settings. Prompt, model, and parameters all affect the result — always compare with that in mind.
How to use the comparisons
Use the catalog to check availability and price, rankings to narrow your choices, benchmarks to understand each model's strengths, and model pages for full details.
Independent project
Venice Atlas is independently maintained. It links to the official sources and presents their information for comparison, but it is not operated, endorsed, or affiliated with Venice AI or LLM-Stats.
Private by design
Venice Atlas respects your privacy. It uses anonymous analytics with no cookies or tracking, runs as a static site with self-hosted fonts, and shares no data with ad networks. The only external services are the Bunny video player (when you press play) and links you choose to follow.
How rankings work
Which models are included?
A category ranking includes Venice text models that can be linked to an LLM-Stats model with a published category rating. Models without that evidence — including Venice's end-to-end encrypted endpoints — stay visible in the catalog but do not appear in rankings or the comparator. Missing results are never treated as zero.
What does the score mean?
Category ratings use LLM-Stats’ conservative rating: higher is better within that category. These are not percentages, and a score in one category should not be compared directly with another. Individual benchmarks retain their own scales.
How is price compared?
The chart estimates a price per million tokens using Venice’s input, output, and cache-read prices. Balanced weights input and output equally. Longer prompts uses 80% input and 20% output; longer answers reverses those weights. Repeated context uses 80% cache reads, 10% new input, and 10% output. Models without the required prices cannot appear in that view.
What does value for money mean?
Atlas’s value-for-money index combines category performance and the selected pricing profile. Higher values indicate a better performance-to-cost trade-off within that profile. The index is separate from the original benchmark score and is not a prediction of your bill. Your prompts, response lengths, and cache usage determine actual cost.
Why can models share a score?
Some Venice endpoints use the same underlying model and share its benchmark evidence. Their prices and settings can still differ, so their value indices may differ too.
What do position changes mean?
Leaderboard arrows compare positions with the previous data update. A model can move when other models or new results enter the ranking; movement does not necessarily mean the model itself changed. Prices and results reflect the data available when the site was last built.
Questions visitors ask
Context before
comparison
Why are some models missing benchmark results?
LLM-Stats may not have a published score for that model. It still appears in the catalog, but Atlas does not create a score or rank.
Why do two Venice AI endpoints share the same benchmark results?
Some endpoints use the same underlying model and share its benchmark results. They still keep their own Venice AI pricing and settings.
What about Venice's encrypted (E2EE) endpoints?
Encrypted endpoints run the same base model as the public version, but your data stays private. LLM-Stats benchmarks the public model, so Atlas uses those results as a reference. Note: encryption and reduced-precision (quantization) can change real-world behavior slightly. E2EE endpoints stay in the catalog with their privacy level marked.
How should I attribute the data?
Venice AI provides the catalog, endpoint information, and prices. LLM-Stats provides benchmark data and methodology. Their names, marks, and data remain subject to their original terms. Atlas is independent and links to the official sources.
Does Venice Atlas use analytics?
Yes. We use Umami for anonymous, aggregate usage insights. It does not use cookies or cross-site tracking.