u/-GeneX-

We benchmarked eight company enrichment providers - People Data Labs, Apollo, Parallel, PredictLeads, Explorium, CompanyEnrich, ZoomInfo and Exa — against a cohort of 282 companies whose reference fields were manually verified from live LinkedIn company pages.

Resolution rate is a solved problem, so stop shopping on it

Provider           resolved    rate%   cov%   p50 latency
---------------------------------------------------------
People Data Labs    277/282    98.23  94.33        274 ms
Apollo              273/282    96.81  93.05        654 ms
Parallel            281/282    99.65  94.68     21,317 ms
PredictLeads        282/282   100.00  87.94        556 ms
Explorium           268/282    95.04  75.96        616 ms
CompanyEnrich       278/282    98.58  93.69        309 ms
ZoomInfo            267/282    94.68  88.72      1,132 ms
Exa                 282/282   100.00  77.09      8,143 ms

Every provider resolves between 94.7% and 100% of domains. That spread is not a buying signal. Latency spans 274 ms to 21,317 ms — a 78x range — and that one probably is.

How is the ground truth verified?

Human labellers verified every scored Ground Truth field across official company sites, filings, registries, news, reputable reference sources, redirects, and canonical or alternate LinkedIn URLs. Invalid or unreachable candidates were removed; the frozen cohort contains 282 reachable companies before provider scoring. The resulting judgments and audit artifacts are public and reproducible.

Reproducing it

• Results: https://openbenchmarks.com/company-enrichment
• https://openbenchmarks.com/api/benchmarks/company-enrichment

Do contribute and star it ~ it's open-source!

• https://github.com/openbenchmarks-labs/company-enrichment

Happy to add a provider or extend the cohort or add more metrics that you care about.

We placed 500 real phone calls across five voice agent platforms — Telnyx, ElevenLabs, Bland AI, Vapi and Retell AI — and measured time to first audio byte on every turn. 499 calls were usable, four scripted turns each, 1,883 usable turns total. Same caller, same carrier (Plivo), same script for all five.

The p50 ranking and the p95 ranking are not the same ranking

Platform      p50    p90    p95    p99   turns
---------------------------------------------
Telnyx       1302   1724   1839   2164     379
ElevenLabs   1430   1686   1772   2269     389
Bland AI     1528   2010   2273   2860     389
Vapi         1562   1854   2014   2677     345
Retell AI    1738   2096   2224   2764     381

Time to first audio byte, milliseconds. Lower is better.

Telnyx has the fastest median at 1302 ms. From p90 onward it's ElevenLabs — 1686 at p90, 1772 at p95. Bland AI is third at the median and last at p95, at 2273 ms.

The tail is where they separate

ElevenLabs   1430 → 2269    +839
Telnyx       1302 → 2164    +862
Retell AI    1738 → 2764   +1026
Vapi         1562 → 2677   +1115
Bland AI     1528 → 2860   +1332

Milliseconds added between p50 and p99. Lower is a flatter tail.

Bland AI adds 1332 ms between its median turn and its 99th-percentile turn. ElevenLabs adds 839. On a four-turn call, the p99 turn is the one the caller remembers.

What the numbers are and are not

Recording-path overhead sits inside every figure on this board. We have not characterised the current measurement path against a known-delay reference, so we quote no overhead figure and subtract none. These numbers are comparable to each other — same path, same caller, same carrier — and only approximately comparable to figures produced by a different instrument. If you're looking for each platform's true absolute latency, this board does not give it to you, and neither does anything else we've seen.

Three caveats

• Each platform ran its own defaults. Telnyx on Kimi-K2.6, ElevenLabs on gemini-2.5-flash, Retell and Vapi on gpt-4.1. So this is platforms as shipped, not a model-controlled comparison — some of the gap is model choice. Every config is published, hashed, so you can see exactly what ran.
• Vapi's discard rate was 12.0% against 0.8–3.3% for the others — 46 turns where our two voice-activity detectors disagreed on where speech ended, so we dropped them. Its n is 345 rather than ~385. We'd rather show you a thinner sample than a guessed one, but it's a real asymmetry and it may flatter or penalise Vapi.
• Turn 4 is consistently faster than turns 1–3 on several platforms, which suggests warm-path effects we haven't isolated. Per-turn splits are in the data.

Why phone calls rather than websockets

Most published voice latency numbers benchmark TTS or STT models in isolation. This measures the whole platform over the PSTN — carrier, telephony stack, endpointing, model, voice — because that's the number a caller actually experiences. It's the less flattering way to measure and the harder one to reproduce, which is why the configs and per-turn data are published.

Reproducing it

• Results: https://openbenchmarks.com/voice-agent-latency
• Method: https://openbenchmarks.com/voice-agent-latency/how-voice-agent-latency-is-measured

Happy to add a platform or rerun with a controlled model if that's the comparison you want. If you've measured any of these yourself and got something different, post your numbers — the method page says exactly what we did, so the disagreement should be locatable.

We ran a lookalike / similar-company benchmark across 7 vendors: Parallel, Extruct, Ocean.io, Exa, PredictLeads, Discolike and CUFinder.

Here - https://openbenchmarks.com/lookalikes

48 seed companies across 13 categories — b2b-saas, devtools, ecommerce, healthtech, home services, trades, real estate, fintech, cybersecurity, industrial, logistics, hospitality, energy. We asked each API for up to 100 lookalikes per seed, then had an LLM judge (gpt-5.6) score every returned company on whether it's genuinely a lookalike of that seed.

Different vendors win at different K

Vendor          P@10    P@25   P@100
-------------------------------------
PredictLeads   95.83   77.17       -
Exa            95.00   80.83   52.40
Parallel       74.79   75.00   67.54
Extruct        74.79   70.00   61.21
Ocean.io       73.19   67.06   56.53
Discolike      42.98   41.53   35.19
CUFinder       37.45       -       -

Average precision, 48 seeds, judged by gpt-5.6. Higher is better. A - means unscored, not zero — see caveats.

At K=10, PredictLeads leads at 95.8 and Exa is second at 95.0. At K=100, Exa is fourth at 52.4 and Parallel — third at K=10 — is first at 67.5.

Precision drop from P@10 to P@100

Exa        95.0 → 52.4    −42.6
Ocean.io   73.2 → 56.5    −16.7
Extruct    74.8 → 61.2    −13.6
Discolike  43.0 → 35.2     −7.8
Parallel   74.8 → 67.5     −7.3

Exa drops 42.6 points between K=10 and K=100. Parallel drops 7.3.

Which one to use

• A list of 10–25 accounts: PredictLeads (95.8 at K=10) or Exa (95.0 at K=10, 80.8 at K=25). The drop-off never reaches you.
• A list of 100+ accounts: Parallel, at 67.5. A P@10 comparison would point you at Exa, which scores 52.4 at that depth.

Two caveats

• PredictLeads and CUFinder return fewer than 100 results per seed, so they have no P@100. Read the - as no data, not as a low score.
• "Lookalike" is judged, not ground truth. An LLM decided what counts. The judge prompt is published, so you can read it and disagree with it.

Reproducing it

Every cell is backed by the HTTP request and response we sent to each vendor, plus the judge prompt and its response, stored per seed and per vendor.

• Results: https://openbenchmarks.com/lookalikes
• Raw artefacts: https://github.com/openbenchmarks-labs/lookalikes
• Licence: CC-BY-4.0

Happy to add a vendor or run more seeds. If you think the judging is wrong on a specific pair, the raw file shows what the judge saw.

Disclosure: I run Openbenchmarks. We run independent benchmarks and publish the raw artifacts and results for agents.

No vendor paid for placement or inclusion.