Apollo markets itself as the AI GTM System for Go-to-Market Teams, with 240 million contacts and 30 million accounts in its catalog. The pitch to buyers is coverage. If you want to find someone, Apollo probably has them. The 2026 GTMBench tested whether a list that reaches a rep actually matches the criterion the operator asked for, and that is a different question.
Across 26 natural-language list-building prompts, Apollo returned 36.0% precision on average, with the median prompt landing at 34.5%. Landbase returned 76.7%, with the median at 86.5%. The gap pays itself back on the sales floor, where SDR hours are the scarce resource. Salesforce State of Sales research has documented that reps spend only 28% of a working week actually selling, with most of the remainder lost to list hygiene and research. Harvard Business Review coverage of B2B selling reports similar patterns. This post compares the agent approach Landbase uses with the catalog-plus-AI approach Apollo uses, prompt by prompt.
What each system is built to do
Apollo is a catalog with a filter interface, extended with AI research agents and sales execution tools. Buyers select from firmographic, technographic and intent attributes, apply the filters, and export rows. The platform publishes four tiers (Free, Basic, Professional and Custom) and markets Apollo Anywhere, Apollo Profile and a GTM Harness training layer. Apollo's catalog scale is the headline number in most buyer evaluations.
Landbase is an agent that reasons over the dataset. The operator writes a prompt in plain language. The agent decides which criteria apply, pulls the records, and verifies each company against every condition before returning a row. The platform runs inside Claude Code and Codex, reasoning across more than 1,500 enrichment fields per company. The GTMBench methodology is documented on the GTMBench landing page.
The benchmark asked the same 26 natural-language prompts of each system and judged every returned row against the original prompt.
Precision across 26 prompts
Landbase averaged 76.7% precision. Apollo averaged 36.0%. On a 1,000-row list, Landbase returned 767 qualifying rows. Apollo returned 360.
The first 100 rows mattered more than the full list for operator economics. Landbase scored 76.1% on the first 100. Apollo scored 29.2%. The top of a file is what a rep works in week one, and a top-of-list that is already noisy stays noisy however long the file runs. McKinsey research on sales productivity has quantified how much of a rep's week vanishes to list cleanup when the top of a file is unreliable.
Across 26 prompts, Landbase won 18 outright. Apollo won 1. The remainder went to Clay, ZoomInfo or ties. Apollo's single win was on compliance leaders at manufacturing companies, a prompt that mapped cleanly to Apollo's compliance-title taxonomy.
Pool size tells a different story than precision
Apollo's filters matched a geometric mean of 13,519 records per prompt before the row cap, against Landbase's 1,304. The catalog is deeper. The question is what the catalog returns after the criterion is applied.
Multiplying precision by pool gives expected true matches: the qualifying companies each vendor reaches. Apollo delivered 3,764 expected true matches. Landbase delivered 846. On raw reach, Apollo wins. On rows that an SDR can work without re-verification, Landbase delivers four times the rate per row.
The operator decision depends on the next step. A list going to a scored outbound sequence benefits from higher precision at the top. A list feeding a broad brand awareness play benefits from reach. Gartner research on sales technology adoption has argued that buyers trade latency and reach for accuracy when the downstream cost of a bad list is high.
The three prompts Apollo answered weakly
Three prompts in the benchmark broke Clay and ZoomInfo entirely. Both platforms have no attribute for the criterion asked, so their builders returned empty filter sets. Apollo attempted all three. Apollo's scores were 0 on follower growth, 2.0% on 90th-percentile engineering tenure under 18 months, and 11.5% on AE-to-SDR ratio between 1.5 and 3. Landbase scored 100%, 96.1% and 100% on the same three.
Each is a derived measure that requires computing a statistic from underlying records. A catalog can only match what it already stores. Apollo's AI research layer attempted the computation and reached a double-digit precision in one case, which outperforms Clay and ZoomInfo but still trails the agent approach by 85 to 100 percentage points. For a deeper read on these three prompts, see our post on derived criteria.
Where filter-plus-AI falls short
Apollo's catalog depth is real. The company publishes 240 million contact profiles. The GTMBench methodology counted rows that passed the prompt's criterion after re-scrape from LinkedIn, and that is where catalog depth stops predicting the result.
On the firmographic family, where the criterion already sits in Apollo's schema, Apollo averaged 46.8% precision. Clay led the family at 72.7%. Landbase scored 68.9%. Apollo's filter interface returned rows that nominally matched the stored attribute, but the underlying record frequently failed verification against the sharper prompt. The gap suggests catalog freshness and attribute-level accuracy, not filter design.
On concept prompts (loose labels like sticky brands or creator economy startups), Apollo averaged 49.1% precision. Landbase averaged 65.6%. Clay averaged 40.0%. A fuzzy criterion forces interpretation; agent reasoning produced more defensible interpretations than filter composition on those prompts.
On lookalike prompts (a seed set plus find more like these), Apollo averaged 48.4%. Landbase averaged 88.9%. Similarity through filter composition approximates the seed set on firmographics. Similarity through agent reasoning handles non-firmographic patterns the seed set shares, including business model, go-to-market motion and signal patterns.
Pricing and the cost per qualifying contact
Apollo publishes four tiers: Free, Basic, Professional and Custom. Pricing scales with credits and features. Call recording, AI features and higher record selection limits sit on the Professional tier. All seats must be on the same tier.
Landbase prices per credit at a fixed per-contact and per-phone rate, with enrichment and verification wrapped in. Credits burn on verified rows only, because the agent verifies before enrichment. On GTMBench's measured precision rates, 1,000 credits of Apollo output deliver 360 qualifying rows. The same 1,000 credits of Landbase output deliver 767. Our pricing page carries the per-credit breakdown.
The buyer-facing question is cost per qualifying contact. Row count is a weaker proxy. Reach is the Apollo value proposition. For teams whose downstream funnel pays for verification separately, the Apollo cost compounds through the stack.
When to pick each
Apollo is the right tool when raw reach is the critical variable, when the operator wants a single integrated workspace for sourcing, outreach and dialing, and when the criteria map cleanly to attributes already in Apollo's schema. Apollo customers include tens of thousands of SMB and mid-market teams that benefit from the all-in-one bundle.
Landbase is the right tool when the criterion is derived, lookalike or concept-level, when precision at the top of the file determines whether the list gets worked, and when the operator wants to describe the audience in plain language. The 2 to 4x uplift in connect and meeting-booked rates that Landbase customers report comes from working lists where every top-100 row cleared verification.
Teams running both have reported sourcing broad universes through Apollo and running the output through Landbase as a precision pass before SDR handoff. For the vendor-by-vendor view, read our Landbase vs Clay comparison and Landbase vs ZoomInfo comparison.
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. HubSpot sales statistics have documented how much of an SDR week depends on list quality upstream of the dial.
Across GTM teams from agencies to enterprise revenue organizations, customers have reported a 2 to 4x uplift in connect and meeting-booked rates. Send Landbase a list already pulled from Apollo and the platform will qualify, expand and score it in one pass at no cost. Start a qualification pass here.
Frequently asked questions
How does Apollo's 240 million contact catalog translate into GTMBench results?
Apollo's pool size averaged 13,519 records per prompt in the benchmark, ten times the Landbase pool of 1,304. Catalog depth drives reach. GTMBench measured what reaches a rep after re-verification against the original prompt criterion, and Apollo's precision came in at 36.0% against Landbase's 76.7%. The catalog is deeper. The verified yield is lower.
Does Apollo's AI research layer close the derived-criteria gap?
Partially. On the three prompts Clay and ZoomInfo declined, Apollo returned 0 on follower growth, 2.0% on tenure percentiles and 11.5% on AE-to-SDR ratio. The AI research layer did more than a catalog filter alone could, but still trailed the agent approach by 85 to 100 percentage points on those three.
Is Apollo faster than Landbase?
Yes. Apollo takes 20 seconds to build a list on average. Landbase takes 134 seconds. The extra time is what the agent uses to verify each company against every criterion, which produces the precision gap. For webhook-timed workflows, 20 seconds is the better fit. For files that an SDR is going to work for the next week, the 2-minute wait is a smaller cost than the 2-week cost of chasing wrong accounts.
Can I use Apollo and Landbase together?
Yes. Teams have reported pulling a broad list from Apollo for coverage, then running that list through Landbase as a precision and derived-criteria pass before SDR handoff. Each system covers a different stage of the same workflow.
Where can I see the full GTMBench prompt-by-prompt results?
The full report, with every prompt scored across all four vendors, lives on the GTMBench page. The methodology is documented alongside the scores, and the per-prompt dot plot shows exactly where each vendor cleared or missed the criterion.
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