Landbase takes 134 seconds to build a list on average. Clay takes 17 seconds. Apollo and ZoomInfo take 20. Across the 26 prompts in the 2026 GTMBench, the agent approach ran roughly seven times slower than the catalog alternatives. The gap is the clearest cost of handing list-building to a reasoning agent.
The question for buyers is what those extra seconds buy. Precision across the same 26 prompts came in at 76.7% for Landbase against 47.9% for Clay, 36.0% for Apollo and 23.2% for ZoomInfo. Salesforce State of Sales research reports that reps spend only 28% of their week actually selling, with most of the remainder absorbed by list hygiene. Harvard Business Review coverage of B2B selling documents the same pattern. The latency trade-off reduces to a single calculation: is the two-minute wait at build time worth the hours saved at dial time. The full GTMBench methodology lives on the GTMBench landing page.
Where the 134 seconds go
Catalog systems resolve a prompt to a stored query, run the query, and return rows. Clay's workflow builder composes multiple catalog queries and layers lightweight AI research steps, which lands at 17 seconds on average. Apollo and ZoomInfo run single catalog queries, closer to 20 seconds.
Landbase runs a different sequence. The agent writes its own criteria from the prompt, pulls records, and checks each company against every condition before returning a row. Verification is the heaviest step. For a 1,000-row list, that is roughly 1,000 per-company checks against the full criteria set, instead of a single catalog query that returns matching rows.
The per-company reasoning is where the 29-point first-100 precision gap comes from. Landbase scored 76.1% on the first 100 rows. Clay 40.6%. Apollo 29.2%. ZoomInfo 18.3%. A catalog that returns 1,000 rows in 17 seconds is faster. The 1,000 rows contain fewer verified matches at the top.
What a rep does with a slower list
SDR productivity is bounded by connect rate, not file size. HubSpot sales statistics have documented how much of a dialing week is spent on contacts who no longer match the criterion the file was built against. A file that is 48% precise at the top of 1,000 rows contains 480 rows a rep works and 520 rows a rep works and discards. The discards are not free. Each discard is a research cycle, a dial, and a disposition note in the CRM.
A file that is 76% precise at the top contains 760 workable rows and 240 discards. Over 1,000 rows, the difference is 280 fewer discards. At three to five minutes per discard, including research and CRM update, the time cost is 14 to 23 hours of rep calendar on the 48%-precise file.
The 117 seconds that Landbase adds at build time saves 14 to 23 hours at dial time. The accounting is one-sided for any file that a rep is going to work.
When the latency does matter
Three use cases make the latency a real cost. Webhook-triggered sequences that react to a sign-up or an event often need a list in under a minute. Inbound lead routing often needs a scored match in seconds. Real-time ad audience sync often needs a push on the order of a minute.
Clay's 17-second runtime, Apollo's 20 seconds and ZoomInfo's 20 seconds all fit those use cases. Landbase's 134 seconds does not. For teams whose outbound depends on real-time triggers, the latency band is the first filter in a vendor evaluation.
The use case match matters more than the headline runtime. Most SDR dialing files are built once per week or once per cycle, not in real time. For those files, the 134 seconds is a one-time cost against a week of dial time.
Latency and the precision ceiling
Catalog systems cap at the precision their stored attributes can reach. Clay scored 47.9% average precision. That number reflects the share of catalog rows that still matched the prompt criterion after verification against the current LinkedIn profile. Adding more pipeline steps in Clay does not raise the precision past the ceiling the underlying attributes support.
Reasoning systems do not have the same ceiling in principle. Landbase scored 76.7% average precision. The ceiling is lower than 100% because some criteria are ambiguous (concept prompts like sticky brands averaged 65.6%) and some have inherent noise in the underlying LinkedIn record. The ceiling moves with how precisely the prompt specifies the criterion and how clean the source signal is.
The 29-point precision gap, from 47.9% to 76.7%, is what the 117 seconds of extra runtime buys. Gartner research on sales technology adoption has argued that buyers trade latency for accuracy when the downstream cost of a bad list is high. The trade is honest when the dial-time cost of a 48% file exceeds the build-time cost of a 134-second wait.
What this means for the stack
Teams running both patterns have reported a two-stage architecture. Catalog systems source broad universes at low latency. Reasoning systems run a precision pass on the output before SDR handoff. The sourcing stage absorbs the catalog's reach and speed. The precision stage absorbs the agent's latency once per file instead of once per lead.
The two-stage architecture respects the strength of each system. Catalogs are built for query throughput. Agents are built for row-level reasoning. Running them in series instead of in competition captures both.
For the vendor-by-vendor view of what each returns, read our Landbase vs Clay, Landbase vs Apollo and Landbase vs ZoomInfo comparisons. For the methodology that produced the latency and precision numbers, see why we built GTMBench.
What Landbase delivers
Landbase is an agent that reads a plain-language prompt, reasons across more than 1,500 enrichment fields per company, verifies each row before returning it, and dial-tests the file before handoff. The platform runs inside Claude Code and Codex, and connects to Salesforce, HubSpot and CSV export. The 134-second runtime is the mechanism behind the first-100 precision of 76.1%.
Across GTM teams from agencies to enterprise revenue organizations, customers have reported a 2 to 4x uplift in connect and meeting-booked rates. For teams whose files are worked over the next week or more, the latency is a one-time cost against sustained dial-time savings. Start a precision-pass qualification here.
Frequently asked questions
Is 134 seconds the median or the mean?
Mean. The median prompt took 120 seconds on Landbase. Clay's median was 17 seconds, Apollo 18, ZoomInfo 18. The mean and median are close for each vendor, which suggests consistent runtime across the prompt set rather than a few outliers skewing the average.
Can Landbase run faster if the prompt is simpler?
Yes, modestly. Firmographic prompts with fully stored attributes complete faster than derived prompts that require computation across underlying records. The runtime floor remains in the 60 to 90 second band because the per-company verification pass still runs on each row before return.
Does Clay's speed advantage degrade on complex workflows?
Yes. Multi-step pipelines that chain several enrichment calls can run to 2 or 3 minutes depending on the number of steps and the external provider latency. Clay's 17-second number reflects single-query flows. Complex workflows close part of the latency gap but do not change the precision ceiling.
What about real-time inbound use cases?
Real-time inbound routing favors catalog systems. The 134-second runtime is a dealbreaker when the business logic needs a scored match before a webhook timeout. Teams that run inbound routing often use a catalog for the real-time pass and a reasoning agent for the overnight batch pass.
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