Which AI models actually drive B2B growth?
We benchmarked leading AI models across text, reasoning, search, and agent categories to see how well they identify ideal clients, surface decision-makers, and craft high-impact LinkedIn outreach. Using a weighted scoring system centered on French heritage, APAC presence, and knowledge-intensive workforces, we ranked each model on portfolio analysis, target list quality, and message performance. The results favor agent and search-augmented approaches, with human validation still essential to keep signals sharp and targets relevant.
Points clés
- We evaluated AI models across four capabilities—text, reasoning, search, and agents—on two tasks: finding 20 lookalike prospects to Pernod Ricard, Lacoste, Saint-Gobain, and Lesaffre, and writing unique LinkedIn messages for each.
- Our scoring framework weighted French heritage (5 points), global APAC presence (3 points), and knowledge-intensive workforce (2 points) to align with our ideal customer profile.
- In portfolio analysis, Manus (Agent), Genspark (Agent), and Kimi K2 (Text) scored 10/10 by correctly capturing heritage, scale, APAC reach, and knowledge-worker intensity.
- Copilot (Quick) and Kimi K2 + Web (Search) scored 8/10, typically missing either explicit French heritage or underweighting knowledge workers.
- DeepSeek R1 (Reasoning) underperformed (2/10) by neglecting the French heritage signal and leaning on vague “human-centric” positioning.
- For target lists, Kimi K2 + Web led with a score of 69 (avg frequency 3.45), followed by Manus at 68 (3.40), Genspark at 67 (3.35), and Copilot (Quick) at 64 (3.20).
- Contact discovery showed trade-offs: Kimi K2 + Web delivered 90% real contacts but skewed to HQ; Kimi K2 hit 40% real contacts with better APAC relevance; Genspark reached 100% real contacts—but mostly global CEOs.
- In message quality, Kimi K2 + Web achieved a perfect 5.0 overall; DeepSeek R1 and Manus followed at 4.8; Kimi K2 scored 4.7; Copilot (Quick) trailed at 1.8.
- Search-augmented models excelled in personalization and relevance, while agents needed sharper CTAs; output consistency constrained some text models’ usability.
- Hybrid pipelines combining agents and search achieved about 90% precision versus roughly 60% for single-model approaches, with manual review remaining critical for cultural nuance and regional fit.
À retenir
Start with agents and search if you want signal over noise; think of them as your espresso shot and your filter—strong on synthesis and clean on facts. Aim below the C‑suite unless you fancy polite rejections from very busy CEOs, and insist on APAC roles that actually touch learning, innovation, or digital transformation. Finally, mandate CTAs with a time box and a pilot offer, then add human review—because while AI can spot a trend, it still can’t read the room (yet), and no one wants to book a 15‑minute meeting with “Dear [Name].”
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