OpenAI has launched three versions of its ChatGPT 5.6 frontier AI model: Sol, the flagship model; Terra, which is balanced for everyday work; and Luna, a fast and affordable variant.
The release, following closely behind Anthropic’s Fable 5, has sharpened an increasingly important question for customer experience leaders around whether enterprises should keep chasing the most powerful frontier large language models (LLMs), or focus on models that are smaller, cheaper, more controllable, and better aligned to specific CX workflows.
The answer appears to be both, but not for the same jobs.
Frontier models are advancing quickly, and their reasoning, multimodal capabilities, coding performance and agentic potential continue to stretch what enterprises believe AI systems can do. For CX operations, that raises the prospect of more sophisticated virtual agents, richer knowledge retrieval, better personalization and AI systems capable of handling complex, multi-step customer journeys.
Yet the practical reality inside many contact centers is that much of customer service does not require the most powerful model available. It requires fast, reliable, compliant execution of repeatable tasks from classifying intent to summarizing conversations, retrieving policy information, checking answers against approved knowledge, routing cases, detecting sentiment and escalating when needed.
For those use cases, a smaller language model that has been specifically trained or tuned for a particular workflow or customer service domain may be not only sufficient, but more effective.
As Ashish Nagar, Founder and CEO of enterprise AI software firm Level AI told CX Today:
“If I want to go from Mountain View to San Francisco Airport, I don’t want to take a Boeing 747 to do that. I just want to take an Uber. Using a GPT-5 for a simple CX task is like taking a Boeing 747 to go to the airport, which is 30 minutes away. You need an Uber, which is a small language model that’s specifically trained to navigate this path.”
Many CX applications are narrow, high-volume and cost-sensitive, and in those environments the question is not “what is the most intelligent model?” but “what is the right level of intelligence for this job?”
That distinction is becoming more significant as enterprises encounter the operational costs of large-scale AI deployment.
The Token-Burn Problem
As GenAI moves from experimentation into production, token consumption has become a board-level concern. A proof of concept may look compelling when usage is limited. But once an AI agent begins handling thousands or millions of interactions, inference costs can rise quickly.
In a recent CX Today interview, Rebecca Wettemann, CEO and Principal Analyst at Valoir, highlighted two fears now shaping enterprise adoption.
“A lot of them are hearing the stories about two things,” she said. “Either AI running off in the middle of the night and doing something deleterious, or waking up in the morning and finding that they have a huge token bill that they didn’t expect.”
But many of the most dramatic examples today come from coding use cases, rather than customer service.
“Most of the time that those two things are happening today are with things like using AI for coding, where it’s deleting files or databases or that sort of thing. It’s not in the customer service context necessarily,” Wettemann noted. “But we hear that and people say, ‘wait a minute, I want to make sure before I put this in production that it’s actually going to do a positive interaction with my customer if it’s customer facing, and that it’s not burning through tokens.’”
That anxiety is significant because customer service is a high-risk environment for AI, with models often acting directly in front of customers. Regulated industries in particular have a low tolerance for improvisation.
At the same time, customer experience is also one of the areas where AI has the clearest ROI potential. Reducing handle time, automating common queries, improving agent assist and increasing self-service containment can all deliver measurable value. The challenge is implementing AI in a way that is economically viable and operationally safe.
Salesforce Emphasizes “Precision Over Power”
That is why model right-sizing is moving from an engineering detail to a strategic CX issue.
As Jayesh Govindarajan, EVP of Software Engineering at Salesforce AI, wrote in a blog post, most enterprise work needs “the right intelligence for each job—not the most intelligence for all of them.”
Govindarajan described how Agentforce previously relied on a single rented model, causing token bills to grow linearly with traffic. Rather than simply passing those costs on to customers, the company rebuilt its architecture, breaking tasks apart and tuning specific open-source models for defined jobs.
The company’s conclusion was that a general-purpose frontier model can perform many tasks, but often “more slowly, more expensively, and less precisely” than a model designed for a specific function.
Salesforce’s architecture now uses targeted models for steps such as safety screening, intent detection, grounding and answer validation, while still reserving a frontier model for core multi-step reasoning.