Where small models are challenging AI giants

NextLM's Savant 3.5, built on lightweight NVIDIA Nemotron models, outperformed frontier models from OpenAI, Google, Anthropic, SpaceXAI, and Moonshot in a customer-scoring task, placing 24.6% of eventual buyers in its top 10% of recommendations versus GPT-5.6 Sol's 22.8%, at a cost of $0.003–$0.011 per 1,000 prospects compared to $0.26–$5.11 for competitors. The results validate that small, specialized models can beat larger ones in niche tasks, offering lower cost, privacy, and security benefits, though general models remain useful for broad capabilities.

ith enterprises finally clamping down on tokenmaxxing, small models have emerged as a counterweight to expensive proprietary APIs. On Monday, marketing and behavioral insights firm NextLM unveiled research detailing the results of its new system, Savant 3.5, built on lightweight, open-source NVIDIA Nemotron models and purpose-made for one specific task: helping salespeople find the best customers. In its niche task, the model outperformed major models from OpenAI, Google, Anthropic, SpaceXAI, and Moonshot, and at a much lower price. "I think it validates the idea that small models can not only compete with the frontier, but actually, in some cases, win," Chris Anzalone, founder and CEO of NextLM, told The Deep View. Savant is built on top of one of Nvidia's most compact models, sitting at roughly 30 billion parameters with 3 billion active for each input, to find customers. Then it uses Nemotron 3.5 Lightning as a "supporting signal" to sharpen the rankings. Here are the results: - The most important result was how many eventual customers appeared in the top 10% of a model's customer recommendations. NextLM's Savant model put 24.6% of buyers in that 10%. The closest competitor, GPT-5.6 Sol at 22.8%. - Grok 4.5 scored third-highest at 22.3%. Anthropic's models, meanwhile, sat near the bottom of the group, with Fable 5 sitting at 18.1% and Opus 5 ranking dead last at 14.6%. - Additionally, Savant managed to do so at a much cheaper rate, sitting between $0.003 and $0.011 per 1,000 customer prospects scored, compared to between $0.26 to $5.11 for the frontier-model APIs tested. NextLM's success provides an example of where small models fit best: niche, specialized tasks. "If a model isn't tuned to your business outcomes, the value really isn't yours," Anzalone said. "It's transferable to whoever uses that same model." Our Deeper View Small models offer several benefits over bulky, expensive models accessed through proprietary APIs. As Savant proved, these models can be trained to do hyper-specific tasks and achieve hyper-specific business outcomes at a fraction of the cost. Additionally, because these models can often be stored on local hardware, they're generally more private and secure than handing your business's most critical data over to a model provider. And for enterprises desperately searching for returns while also being on high alert amid increasingly common AI-powered cyberattacks, small models may sound like attractive prospects. However, that doesn't mean that enterprises are bound to stop using frontier models and APIs entirely. These systems have a few significant advantages: They're easy to use and provide a readily available source of general capabilities. As Anzalone notes, "People are still going to use general models because they have general information." The challenge enterprises now face is striking the balance between the two.