Fine-tuned 4B Nemotron beats Claude Opus 5.5 and GPT-6 at 1/200th the cost in Palantir ontology tests
BrettKrieger12 · x · 2026-10-22
Shared benchmarks show NVIDIA Nemotron models fine-tuned on Palantir's Ontology delivering staggering gains: a post-trained 30B Nemotron 3.5 Lightning beat a model 18x its size.
- Healthcare (Tampa General Hospital): fine-tuned Nemotron 3.5 Lightning 30B hits 95.7% accuracy at 58ms latency vs Claude Opus 5.5 at 88.6% and 1,627ms; Nemotron 3 Nano 4B reaches 93.8% at 36ms.
- Construction RFI classification (McCarthy): fine-tuned Nemotron 3 Nano 4B achieves 73.5% balanced accuracy, beating Claude Sonnet 5.5 (66%) and GPT-6 Astra (62%) at roughly $0.1 per 1M tokens, a fraction of rivals' cost.
Takeaway: fine-tuning small models on domain ontology data can beat flagship LLMs on vertical tasks at dramatically lower cost and latency.
Related event: Palantir's Fine-Tuned Nemotron Beats Frontier Models at 1/20th the Cost(3 posts)→
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