Is fine-tuning open source models cheaper than using the OpenAI API for a startup processing one million requests daily?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Is fine-tuning open source models cheaper than using the OpenAI API for a startup processing one million requests daily?
3 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 (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
OpenAI API pricing GPT-4.1 GPT-4.1 mini official pricing 2026 Google ↗
GPU cloud rental pricing H100 A100 hourly RunPod Lambda Labs 2026 Google ↗
vLLM production serving throughput benchmark H100 tokens per second Google ↗
- Pricing · developers.openai.com
- GPU Cloud Pricing | Per-Second H100, A100, RTX · runpod.io
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API pricing GPT-4.1 GPT-4.1 mini official pricing 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
GPU cloud rental pricing H100 A100 hourly RunPod Lambda Labs 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
vLLM production serving throughput benchmark H100 tokens per second OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |