# Bombora vs G2 Buyer Intent: which do AI models recommend for intent data, October 2026

GTM AI Recommendation Index, October 2026 Edition, B2B intent data providers. Six of fourteen models named Bombora first on the direct prompt; one named G2 Buyer Intent. Page: https://gtm-ai-index.com/sales/b2b-intent-data/bombora-vs-g2-buyer-intent/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Bombora | 27% | #2 of 13 | 27% | 66 | 14 of 14 |
| G2 Buyer Intent | 2% | #8 of 13 | 11% | 44 | 14 of 14 |

## The direct prompt, model by model

- Claude Haiku 4.5: both first (first choices: Bombora, G2 Buyer Intent) (alternatives: Apollo Intent, Dealfront, Factors.ai, Intentsify)
- GPT-5.4 mini: bombora first (first choices: Bombora) (alternatives: 6sense, G2 Buyer Intent)
- Grok 4.20: bombora first (first choices: Bombora) (alternatives: AdRoll ABM, G2 Buyer Intent, Intentsify, Warmly)
- DeepSeek V4 Flash: bombora first (first choices: Bombora) (alternatives: G2 Buyer Intent, Intentsify, ZoomInfo)
- Qwen 3.7 Flash: bombora first (first choices: Bombora) (alternatives: 6sense, Datanyze, Demandbase, ZoomInfo)
- GPT-6 Luna: bombora first (first choices: Bombora) (alternatives: 6sense, G2 Buyer Intent)
- Gemini 3.5 Flash: neither first, one named (first choices: ZoomInfo) (alternatives: Bombora, Cognism, Factors.ai, RB2B)
- Kimi K2: neither first, one named (first choices: Dealfront, ZoomInfo) (alternatives: Bombora, G2 Buyer Intent)
- GLM 4.7 FlashX: neither first, one named (first choices: Factors.ai) (alternatives: Bombora, Dealfront, G2 Buyer Intent)
- MiniMax M2.5: neither first, one named (first choices: 6sense) (alternatives: Bombora, Dealfront, Demandbase, G2 Buyer Intent, Prospeo, TrustRadius, ZoomInfo Streaming Intent)
- Perplexity Sonar: neither named (first choices: 6sense) (alternatives: Apollo.io, Dealfront, Demandbase)
- Mistral Small: neither named (first choices: Dealfront, Factors.ai) (alternatives: 6sense, Lusha)
- Llama 4 Maverick: neither named
- Muse Glimmer 30B: neither named (first choices: Dealfront, Factors.ai) (alternatives: Warmly)

## What the models said about Bombora

- "Bombora ($25K+/year) and 6sense ($60K+/year) are enterprise-level and not suitable for limited budgets" (MiniMax M2.5, budget prompt, hard negative)
- "Why Skip for Mid-Sized ... standalone (needs extra enrichment tools); too pricey." (Grok 4.20, paraphrase prompt, hard negative)
- "Bombora is the most recognized pure-play intent provider... Choose Bombora if you already have a strong sales/marketing stack and simply need the most trusted third-party intent signals" (Kimi K2, comparative prompt, first choice)
- "I would generally recommend Bombora if your priority is reliable third-party intent data and broad market coverage" (Perplexity Sonar, paraphrase prompt, first choice)
- "Practical recommendation for a typical mid-sized B2B company: start with Bombora Company Surge for topic surge intent" (Muse Glimmer 30B, paraphrase prompt, first choice)

## What the models said about G2 Buyer Intent

- "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)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.