These four price a search between $1 and $16 per thousand, and the spread is not margin — it is how far down the retrieval pipeline each one reads. Price a whole research turn instead of a call and the ordering inverts: the cheapest rate card produces a turn costing three times the dearest one.
A reranker touches no index and holds no state, so swapping one is an afternoon — which finally makes chasing the leaderboard rational, except the leaderboard measures the axis where these four differ least. What differs by more than an order of magnitude is the billing unit and the licence, and both bite hardest at agent scale.
Swapping your LLM edits a prompt. Swapping your embedding model re-embeds the corpus, rebuilds the index and invalidates every retrieval number you have — vectors from two models are not comparable, so there is no gradual migration. That makes this the one choice in a RAG stack you make under lock-in, and the deciding numbers are bytes per vector and who controls the model lifecycle, not a leaderboard rank.
Every document-parser comparison is published as an accuracy leaderboard, and accuracy is the axis that transfers worst to your documents. Two things do transfer: a layout pipeline can drop a number but cannot invent one, and the cost curves of self-hosted and hosted parsing cross at a volume you can compute in five minutes.
List prices for agent search APIs cluster tightly around $5–8 per thousand queries, which makes the sticker the least interesting number in the comparison. What actually differs by an order of magnitude is how many tokens each one dumps into your context per result — and in an agent loop that re-sends its transcript every step, that is the bill.
Before you pick a local vector store, notice that Claude Code, Cursor and Codex deleted theirs — the leading coding agents retrieve with grep, not embeddings. If your corpus still needs an index, these four are not competing products but four different architectures: a search library with no storage, a SQLite extension, an embedded engine with a write-ahead log, and a columnar format on disk.
Four vector stores, four nearly identical feature lists — ANN, filters, hybrid search, all of it. The thing that actually decides which one survives the agentic-RAG stack at scale is invisible there: where the index sits relative to your primary data.