openai fine tuning cost versus prompt engineering cheaper models like gpt three point five for our specific use case
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
openai fine tuning cost versus prompt engineering cheaper models like gpt three point five for our specific use case
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
OpenAI API pricing fine-tuning training inference GPT-4.1 mini GPT-4.1 nano official 2026 Google ↗
OpenAI fine-tuning guide when to use fine-tuning versus prompting official Google ↗
- Pricing · developers.openai.com
- Supervised fine-tuning · developers.openai.com
- GPT-4.1 nano Model · developers.openai.com
- GPT-4.1 Mini Model · developers.openai.com
- Model optimization · developers.openai.com
- Optimizing LLM Accuracy · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API pricing fine-tuning training inference GPT-4.1 mini GPT-4.1 nano official 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
OpenAI fine-tuning guide when to use fine-tuning versus prompting official OBSERVED | Google ↗ AI Mode ↗ | — | Yes / no | — | — | 1x of 1 |