We justlaunched a partnershipwithOpenRouterthat lets us offer two new pieces of functionality:
We call it Agent Runners because we run a full coding agent inside, not a pared-down one. Until now, we’ve supported Claude Agent, OpenAI Codex, and Gemini CLI which are optimized to run models from these providers.
We provide these agents with extra skills, and context about the current project, so that the agent will know exactly which Netlify capabilities are available for use (e.g.,Netlify Database, the AI Gateway, orIdentity), when to use them, and how. But to effectively drive a whole variety of new models, we’ve added the popular open-sourceOpenCodeas a new choice of agent.
But with more choice come the inevitable questions: How do I know which model is right for me? Am I missing out on something that’s materially better, or more cost-effective (so I can do more with my credits), or is going to blow my mind like the internet says? There’s a lot of FOMO going around these days.
To provide you with some insights, here’s what we learned when running identical prompts across a range of models… all of which are now available for you touse today on Netlify.
You can see the results of all the models we tested onthis site we created with the full report.
Internally at Netlify, we use AXIS for automatically evaluating models, a tool that we’verecently open-sourced.
We provide AXIS with a variety of test cases: prompts for building a new site and then iterating on it. We instruct AXIS on which agents and models to test these prompts, and define the checks that AXIS should then perform and score the generated site with.
These checks are very much focused oncorrect functionality of the generated site rather than its design, e.g.: does it use a database when a user’s needs call for it? Does it properly use Netlify Database in that case? In those cases where a simple static site will do, we also ensure that the generated site is not over-engineered, and no database is set up.
If a certain model is behind on its test scores, we don’t offer it in Agent Runners. If models too often fail at correctly applying one of our skills, or things do work but the credit cost seems inflated, then the problem is probably with the skill (in which case we optimize that skill).
But this time, we want to provide you with something much more immediately useful: when you go and build your dream using different models that each use wildly different amounts of credits, what do you get? What do the resultlooklike?
We tested three relatively straightforward use-cases: