DefaultModel is a new inference service that hosts one model: the single best open source model, picked by the community.
Open source models are catching up to their closed source counterparts, but inference for them is scattered across general GPU providers. Those providers constantly swap models, and pricing, reliability and support differ from one to the next. You don't get the consistency of a provider like OpenAI.
Large labs get around this by aggregating their own GPU resources, pointing most of the capacity at their latest model, and migrating users there when a new one ships. We want the same for open source models.
So we're taking a different approach. We host one model, the best one, voted by you, the community. We host it for a quarter, and every quarter you vote to keep it or replace it based on the latest releases.
- All GPU capacity goes into one deployment instead of a catalog
- Serving is tuned for that one model, not averaged across fifty
- Performance stays consistent across every request, with no degradation in quality
- When a better model wins the vote, you migrate by changing one model ID instead of rewriting an integration
DefaultModel is a new inference service that hosts one model: the single best open source model, picked by the community.
Open source models are catching up to their closed source counterparts, but inference for them is scattered across general GPU providers. Those providers constantly swap models, and pricing, reliability and support differ from one to the next. You don't get the consistency of a provider like OpenAI.
Large labs get around this by aggregating their own GPU resources, pointing most of the capacity at their latest model, and migrating users there when a new one ships. We want the same for open source models.
So we're taking a different approach. We host one model, the best one, voted by you, the community. We host it for a quarter, and every quarter you vote to keep it or replace it based on the latest releases.
- All GPU capacity goes into one deployment instead of a catalog
- Serving is tuned for that one model, not averaged across fifty
- Performance stays consistent across every request, with no degradation in quality
- When a better model wins the vote, you migrate by changing one model ID instead of rewriting an integration
I had the impression that GLM 5.3 is just open weight and not open source.