is it cheaper to fine tune a smaller open source model or use prompt engineering with a larger commercial model for text classification
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
is it cheaper to fine tune a smaller open source model or use prompt engineering with a larger commercial model for text classification
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
2026 commercial LLM API pricing input output tokens GPT-4.1 mini GPT-4.1 pricing Google ↗
open source model fine tuning cost GPU hosting LoRA text classification 2026 Google ↗
- GPT-4.1 Mini Model · developers.openai.com
- Introducing GPT-4.1 in the API · openai.com
- FinLoRA/docs/source/benchmark_results/resources.rst at main · Open-Finance-Lab/FinLoRA · github.com
- Fine-tuning cost estimation · c3x.dev
- LLM Fine-Tuning Cost Benchmark 2026 · yusmpgroup.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026 commercial LLM API pricing input output tokens GPT-4.1 mini GPT-4.1 pricing OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
open source model fine tuning cost GPU hosting LoRA text classification 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |