Same 1,000 live questions to both APIs — SERPdive Mako vs Tavily's default search, the two same-price tiers — payloads anonymized, independent LLM judge: SERPdive 475 wins, Tavily 307, 217 ties — 60.7% of decided duels — while returning 20.2% fewer tokens (1,001 vs 1,255 average) at 1–2 s per search. Pricing: $5/1k credits (launch) vs $8–16/1k pay-as-you-go (Tavily's advanced mode costs 2 credits; we haven't benchmarked it). Both ship LangChain/LlamaIndex packages and hosted MCP; Tavily keeps the edge on n8n, template footprint and non-search endpoints. Everything here: github.com/edendalexis/serpdive-benchmark.
How the duel works
The benchmark asks one question a normal comparison can't: given the same query, whose payload better answers it? The chain, for each of 1,000 live questions:
- Send the identical query to both APIs at the same time — SERPdive Mako on one side, Tavily's default (basic) search on the other: the two tiers that sit in the same price class, 1 credit each.
- Strip both responses of anything identifying, and alternate their A/B order.
- An independent LLM judge reads the question and both payloads, and picks the one that better answers — or calls a tie.
One scope note, because honest benchmarks state their limits: Tavily's advanced mode (2 credits, so twice the price per search) is not in this benchmark — we have not tested it, and we make no quality claim about it. The duel compares the tiers you'd actually cross-shop at the same budget.
Result: 475–307 with 217 ties. Counting only decided duels, SERPdive wins 60.7%. The judge prompts, the question set, every raw payload and every verdict are in the repo; re-running the whole thing with your own keys is a matter of hours, and the repo shows how.
The token economics under the quality number
The two numbers travel together, and that is the finding. It is easy to win quality duels by returning more text — dump enough of every page and the answer is in there somewhere. SERPdive's responses averaged 1,001 tokens against Tavily's 1,255 on the same questions: it wins the majority of duels while being the shorter payload. Extraction, not volume. Your LLM reads search output on every call; a 20.2% smaller input is a permanent discount on your model bill, compounding with every agent turn that re-reads context.
Pricing, feature for feature
| SERPdive | Tavily | |
|---|---|---|
| Free tier | 1,000 credits/mo · no card | 1,000 credits/mo |
| Pay-as-you-go per 1k searches | $5 launch · $7 list (Mako, 1 credit) | $8 basic · $16 advanced (2 credits) |
| Cheapest committed rate | $4.50/1k (100k plan, list) | $5/1k basic · $10/1k advanced ($500/mo) |
| Search models | Mako (lean, 1 cr) · Moby (full-page, 1.5 cr) | basic · advanced |
| Output | Extracted, answer-ready content | Snippets; raw content optional |
| Beyond search | — | Extract, crawl, map endpoints |
| MCP server | Hosted + npx | Hosted + npx |
| Framework packages | LangChain, LlamaIndex, REST, MCP | LangChain, LlamaIndex, n8n |
| Upstream footprint | Young — packages on PyPI/npm | In framework docs, tutorials, templates |
| Owned by | Independent | Nebius (acquired 2026) |
- You build in n8n or from templates — Tavily is cited in framework docs, tutorials and no-code nodes everywhere; our LangChain/LlamaIndex packages exist (
langchain-serpdive,llama-index-tools-serpdive) but the tutorial long tail is theirs, for now. - You need extract/crawl/map in the same vendor, not just search.
- Procurement wants a big-company parent behind the contract.
The converse case is one sentence: if what you buy from a search API is what your model reads per dollar, the public numbers currently say SERPdive — and when they stop saying it, the repo will show that too.
1,000 free credits a month, no card. Send your 20 hardest production queries to both and read the payloads side by side.
Get your free API keyFrequently asked questions
Is SERPdive better than Tavily?
On the measured axis — which API's response better answers a question, per token — yes against Tavily's default search, by 60.7% of decided blind duels across 1,000 public questions, with 20.2% fewer tokens. Its advanced mode (2 credits) is untested by us, so we claim nothing about it. On ecosystem breadth (tutorial and template footprint, n8n nodes, extract/crawl endpoints, enterprise backing), Tavily is ahead. Which axis matters more is your decision; only one of them is independently checkable.
How does the benchmark avoid favoring SERPdive?
The judge never knows which payload is which: both responses are anonymized and their A/B position alternates. The question set is fixed and public, the judge prompts are in the repo, and ties are excluded from the win rate rather than counted for either side. Anyone can re-run the chain with their own keys and their own judge model.
Why do the token counts matter?
Because search-API output is LLM input. Every token the search layer returns is billed again by your model on every call that reads it. At 1,001 vs 1,255 average tokens, a pipeline doing a million searches a month pushes ~254M fewer tokens through its LLM with SERPdive — before any quality consideration.
How do prices compare exactly?
July 2026, public pages: SERPdive pay-as-you-go is $5 per 1,000 credits at launch (list $7); Mako search = 1 credit. Tavily pay-as-you-go is $0.008 per credit with basic search at 1 credit and advanced at 2 — $8 or $16 per 1,000 searches — falling to $5–10/1k on the $500/month Growth plan. Both give 1,000 free credits monthly; SERPdive asks no card for them.
What does SERPdive not have that Tavily has?
Extract/crawl/map endpoints (SERPdive is search-only), n8n nodes and the long tail of tutorials and templates that cite Tavily by name, and a large corporate parent. Note what is not on that list: LangChain and LlamaIndex — SERPdive ships official packages for both (langchain-serpdive, llama-index-tools-serpdive). If the rest is load-bearing for you today, that may outweigh the measured differences — switch when it stops being true.