I had Gemini train its own replacement for $9
A developer used Gemini 3.1 Pro to label 4,290 Reddit comments for $9, then fine-tuned GLiNER 459M to 0.83 F1 for product NER.
The author replaced per-comment Gemini 3.1 Pro API calls with a GLiNER large v2.5 (459M parameters) model fine-tuned on 4,290 Reddit comments that Gemini labeled for $9 via OpenRouter. Zero-shot GLiNER scored roughly 0.65 F1 against Gemini's labels; the fine-tuned model reached 0.83 F1 after 24 minutes on a Tesla T4, with about $2.50 of GPU cost. Key techniques included asking Gemini for exact substrings rather than character offsets, adding negative examples, and locking a 225-comment validation set; five of ten training runs failed on configuration and words_mask bugs.