Cache hit rates of Inference are more meaningful than the headline costs
The article discusses the significance of cache hit rates in the context of long multi-turn conversations with language models. It highlights that agents push the entire conversation history into context, making cache efficiency crucial for cost management. An analysis of over 60 providers reveals that cache hit rates are often overlooked yet play a vital role in determining overall processing costs.
- ▪Agents in multi-turn conversations are extremely read heavy due to the full conversation history being pushed into context every turn.
- ▪The analysis utilized data from over 60 providers and 398 data points sourced from openrouter.ai model pages.
- ▪Cache hit rates significantly impact the costs associated with processing input tokens in long conversations.
2 outlets in our directory ran this story. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Inference provider tiers by Cache-hit rates, using openrouter data — r/LocalLLaMA
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Story provenance
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Record
| Original publisher | Dirac |
| Canonical URL | https://dirac.run/posts/cache-hit-rates-agents |
| Publication time | Sat, 23 May 2026 18:30:55 +0000 |
| Retrieval time | 2026-05-23T18:37:27.620Z |
| Last seen | 2026-05-23T18:37:27.620Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Y1dMxU_9vy5V · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
.pct-high { color: #10b981; font-weight: 700; } .pct-mid { color: #f59e0b; font-weight: 700; } .pct-low { color: #ef4444; font-weight: 700; } .callout { @apply p-6 rounded-2xl border mb-8; } Tl;Dr: Agents push the full conversation history into context every turn; hence, over a large number of turns, they are extremely read heavy, which in turn is why cache hit rates are an important factor. This post is an analysis of 60+ providers and their cache hit rates using 398 data points. All data sourced from openrouter.ai model pages. Agentic workflows are different from most human-LLM conversations in one key characteristic: the number of turns on average are far higher. Context processing over multi-turn conversation grows quadratically.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Dirac.
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