Dealfront vs G2 Buyer Intent
Three of fourteen models named Dealfront first on the direct prompt; one named G2 Buyer Intent. Both were named by all fourteen models and Dealfront carries 37 labels and G2 Buyer Intent 44, so the shares are not directly comparable.
Named in ten 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
DealfrontFirst choices, of fourteen modelsG2 Buyer Intent
Across every category in the October 2026 Edition, Dealfront and G2 Buyer Intent were named in the same answer forty times, of the 171 answers naming Dealfront and the 177 naming G2 Buyer Intent. In those answers G2 Buyer Intent took the first choice two times and Dealfront eight.
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 4.1 Fast | | | | |||
| 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 | | | |
Dealfront G2 Buyer Intent first choice named as an alternative argued againstblank: not namedEach cell is one answer, Dealfront on the left and G2 Buyer Intent on the right.The direct prompt
Dealfront first, G2 Buyer Intent an alternative
3 of 14 modelsG2 Buyer Intent was named in the answer but not as the choice, or not at all.G2 Buyer Intent first, Dealfront an alternative
1 of 14 modelsDealfront was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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
Dealfront5%#2 of 14G2 Buyer Intent4%#4 of 14Dealfront12%#3 of 13G2 Buyer Intent2%#8 of 13G2 Buyer Intent4%#5 of 9Dealfront0%#– of 9What the models said about Dealfront
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“For a mid-sized B2B company, Dealfront offers the best balance of intent visibility, mid-market focus, and affordability.” GLM 4.7 FlashX · paraphrase prompt · first choice
“combine Apollo.io (to find the right accounts) with Leadfeeder (to verify interest)” Qwen 3.7 Flash · 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.