Make themodel yours.
Switch the base URL to kick off auto fine tuning. We train on your traffic and ship new weights. Better results. Faster responses. 50–80% lower cost.
Tokens0.00B
Spend / hr$0
Token use streams past continuously while the cost of serving it compounds upward. With Gradiated, the illustrative serving cost drops by 80% while the volume carries on unchanged. Figures are illustrative.
Better
Fine tuning changes the weights, so the model learns your task, formats and edge cases instead of being reminded in every prompt.
Faster
A smaller model has fewer weights to read and less work to do for every token. Fine tuning lets that smaller model stay useful on a narrow job.
Cheaper
Using a smaller model cuts the compute behind every request. The saving repeats on every token, not just during training.
Question every result.
Every run compares the new model with the baseline you use in production. See what improved, what regressed and what stayed the same—along with the judge model, evaluation rubric, scores and confidence. Drill down from the summary to the prompts, responses and trace data behind individual results. Decide if the new weights earn their place in production.
Start trainingBase GLM-5.3-Flash$0.17 / task54%Post-trained GLM-5.3-Flash — SFT + GRPO$0.204 / task76%Sol$5.2 / task88%