Compare the token-level cost efficiency of fine-tuning GPT-3.5 Turbo versus prompt engineering Claude 3 Haiku for sentiment analysis.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Compare the token-level cost efficiency of fine-tuning GPT-3.5 Turbo versus prompt engineering Claude 3 Haiku for sentiment analysis.
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 GPT-3.5 Turbo fine-tuning pricing training input output tokens official pricing Google ↗
Anthropic Claude 3 Haiku pricing input output tokens official Google ↗
- GPT-3.5 Turbo Model · developers.openai.com
- Pricing · developers.openai.com
- Pricing \ Anthropic · anthropic.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI GPT-3.5 Turbo fine-tuning pricing training input output tokens official pricing OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
Anthropic Claude 3 Haiku pricing input output tokens official OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |