Is it cheaper to manage LLM token budgets using semantic caching or just switching to a lower cost model like GPT 3.5
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Is it cheaper to manage LLM token budgets using semantic caching or just switching to a lower cost model like GPT 3.5
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI API pricing current GPT-3.5 Turbo availability 2026 semantic caching cost tradeoffs Google ↗
semantic caching LLM cost savings cache hit rate vector database infrastructure pricing Google ↗
- GPT-3.5 Turbo Model · developers.openai.com
- Announcing vector search for Amazon ElastiCache · aws.amazon.com
- Prompt caching · developers.openai.com
- Prompt caching · 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 current GPT-3.5 Turbo availability 2026 semantic caching cost tradeoffs OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
semantic caching LLM cost savings cache hit rate vector database infrastructure pricing OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |