The AI Model Most Favored by Enterprises Is Not What You Think

OpenAI’s ChatGPT took the world by the proverbial storm when it debuted in 2022, becoming synonymous with artificial intelligence (AI) in the public eye.

So it might come as a surprise that OpenAI’s AI models—ChatGPT is the chat interface atop AI models such as GPT-5—is not the one most companies use when they’re deploying AI in their operations.

The surprising winner? Claude from Anthropic.

According to a July report by venture capital firm Menlo Ventures, Anthropic has the top market share among enterprises, at 32%.

OpenAI’s AI models used to have the lead, holding 50% at the end of 2023. But its share has since declined to 25%. Google is at 20%, which experienced “strong growth” in recent months, the report said. (Microsoft, as OpenAI’s largest investor, uses and offers its AI models to clients.)

Among open-source models, Meta’s Llama has a 9% share while AI startup DeepSeek accounts for 1%.

Anthropic’s Claude began gaining momentum in June 2024, with the release of Claude Sonnet 3.5, Sonnet 3.7, Sonnet 4, Opus 4 and Claude Code.

Code generation is “AI’s first killer app,” the report said. This is an area where businesses saw the clearest return on investment early. AI coding assistants help developers write more and better code, faster.

Another boost to Anthropic is its development of model context protocol (MCP), an open set of rules and standards that describe how AI models like Claude and Gemini can connect to tools, application programming interfaces (APIs), data sources and other agents. It can be broadly used in many industries, such as finance.

For example, MCP is part of the new stack for intelligent commerce, according to an August PYMNTS report. Companies such as Visa are using it for intelligent commerce, enabling AI agents to interact with payments and other tools to autonomously and securely perform tasks.

Read the report: The Prompt Economy: How AI Agents Turn Conversation Into Commerce

Companies Value Performance Over Price

Menlo’s report also found is that enterprises prioritize performance over price and prefer closed, proprietary models over open-source ones. One reason is the performance of open-source AI models lags those of closed models by nine to 12 months. Another reason is the technical complexity of going at it alone and reluctance to use Chinese APIs.

On the other hand, CFOs are more cost conscious. A 2024 survey of CFOs by PYMNTS showed that the 46.7% cited “high costs” of generative AI as the second largest drawback of gen AI integration. The top concern was integration complexity (48.3%).

However, when developers choose an AI model, they tend to stick to it. They also quickly upgrade to the most advanced model within the same family: 66% of developers upgraded models within existing providers.

“Builders consistently choose frontier models over cheaper, faster alternatives,” according to the venture capital firm. “They prioritize and pay for performance. When new models come out, switching happens in weeks.”

That means even if AI companies drop prices for their older models, developers aren’t taking advantage of the savings because they upgrade to the latest and best.

Enterprise spending on AI model APIs has more than doubled to $8.4 billion as of mid-2025 from the end of last year, according to Menlo’s report. Moreover, most of what’s spent is on inference—when they apply the AI model to different use cases—instead of building and training models.

The shift reflects growing confidence that gen AI is no longer confined to pilot projects. AI models are actually running in production.

Read more:

High Impact, Big Reward: Meet the GenAI-Focused CFO

Enterprises Confront the Real Price Tag of AI Deployment

AI Model Training vs Inference: Companies Face Surprise AI Usage Bills