Anthropic overtakes OpenAI in enterprise AI adoption with 32% market share
Anthropic now leads the enterprise LLM market with 32% usage share, surpassing OpenAI's 25%, marking a significant shift from 2023 when OpenAI dominated with 50%.
Published: July 31, 2025 | 1:32 PM PDT
Key Findings
- Market Leadership: Anthropic now holds 32% of the enterprise large language model (LLM) market share by usage, overtaking OpenAI's 25%, according to a Menlo Ventures report.
- Historical Shift: This marks a dramatic reversal from 2023, when OpenAI dominated with 50% share while Anthropic trailed at 12%.
- Coding Dominance: Anthropic's lead is even more pronounced in coding applications, where it commands 42% of enterprise usage—double OpenAI's 21%.
Driving Factors
- Model Releases: Anthropic's growth was catalyzed by the June 2024 launch of Claude 3.5 Sonnet, with further acceleration from Claude 3.7 Sonnet in February 2025.
- Enterprise Preferences: Over half of enterprises exclusively use closed models (like Anthropic's and OpenAI's), while open-source adoption dropped to 13% of daily workloads (down from 19% in early 2025).
Competitive Landscape
- OpenAI's Strength: Despite losing enterprise ground, OpenAI remains a consumer favorite, processing 2.5 billion daily prompts for ChatGPT.
- Google's Growth: Google has seen steady enterprise adoption increases, though specifics weren't disclosed.
- Open Source: Meta continues to lead the open-source LLM segment.
Industry Implications
The shift underscores enterprises' prioritization of accuracy and specialized capabilities, particularly in coding—a sector where Anthropic's models have gained developer trust. Meanwhile, OpenAI's consumer-facing tools maintain mass appeal, highlighting divergent market demands.
"Anthropic's rise reflects enterprises valuing precision over scale," the report notes, suggesting the LLM market may continue fragmenting by use case.
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About the Author
Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.