Unverified50% confidenceTradeoffExact time
Trained classical models can run locally, avoiding the per-call billing costs of calling large model APIs.
1
Sources
50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
Sources
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Unverified使用自研模型可让Cursor避免向上游支付第三方API推理成本,并积累代码交互数据用于模型训练77% similarUnverifiedNot all local small models can reliably support tool calling; this capability requires the model to be specifically fine-tuned for the Function Calling format during training.73% similarUnverifiedThe advantage of a non-parametric design is that it requires no GPU cluster training, allows any API-accessible model to be used immediately, and avoids side effects of parameter updates such as catastrophic forgetting.70% similarUnverifiedWhen facing new domain tasks, one should first try running baselines with off-the-shelf open-source models, as usable results can often be obtained at very low cost and it is quite likely that training one's own model will not be necessary.70% similarUnverifiedSmall models obtained through distillation can be deployed locally, run offline, and used long-term after a single training run, with marginal cost approaching zero.69% similar
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