Apollo.io vs G2 Buyer Intent
Zero of fourteen models named Apollo.io first on the direct prompt; one named G2 Buyer Intent. Apollo.io was named by thirteen of the fourteen models and G2 Buyer Intent by fourteen and Apollo.io carries 36 labels and G2 Buyer Intent 44, so the shares are not directly comparable.
San Francisco, United States, founded 2015. Named in twenty-six categories this edition.
By G2, Chicago, United States, founded 2012. Named in seven categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the B2B intent data providers page.
By framing
Apollo.ioFirst choices, of fourteen modelsG2 Buyer Intent
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | | ||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | |||
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | |
Apollo.io G2 Buyer Intent first choice named as an alternative argued againstblank: not namedEach cell is one answer, Apollo.io on the left and G2 Buyer Intent on the right.The direct prompt
G2 Buyer Intent first, Apollo.io not the choice
1 of 14 modelsApollo.io was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Apollo.io65%#1 of 14G2 Buyer Intent4%#4 of 14Apollo.io27%#1 of 13G2 Buyer Intent2%#8 of 13G2 Buyer Intent4%#5 of 9Apollo.io2%#7 of 9What the models said about Apollo.io
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Apollo.io - Best All-in-One for Budget Teams ... probably the most accessible entry point for teams that want intent data without enterprise pricing” Claude Haiku 4.5 · budget prompt · first choice
What the models said about G2 Buyer Intent
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“But check the actual package and price first: G2 says signal access depends on your plan, and its public pricing doesn't clearly show Buyer Intent as included in the low-cost Starter plan.” GPT-6 Luna · budget prompt · soft negative
“G2/TrustRadius/TechTarget-style intent can be useful, but it is often best for product-category research and may be less useful if you need broad, person-level buying intent” GPT-5.4 mini · negative prompt · soft negative
“Each appears in comparison charts but shares recurring pain points—stale data, opaque predictive models, steep learning curves” Qwen 3.7 Flash · negative prompt · soft negative
“Bombora layered with G2 Buyer Intent is the pragmatic starting point.” Claude Haiku 4.5 · direct prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.