The AI Model War in Silicon Valley Enters the Dominance Phase of OpenAI and Anthropic
OpenAI and Anthropic have become the two core competitors in Silicon Valley's AI landscape, leading in hard-core areas such as agent execution and long-term planning (e.g., cybersecurity penetration).
OpenAI's GPT-5.5 remains the all-round champion, maintaining the highest market share and brand dominance in code, mathematics, and multimodal (text, audio, video) integration; Anthropic's Mythos focuses on an "elite safety" approach, capturing nearly 70% of new business contracts in high-security-demand sectors like finance and law.
Google's Gemini 3.1 Pro continues to dominate the long context (2M+ window) field, while Meta's Llama 4 attracts many enterprises to the open-source camp with its low cost and private deployment.
Source: Public Information
ABAB AI Insight
OpenAI has consistently emphasized general capabilities since the iteration of the GPT series, and the dominance of GPT-5.5 in multimodal and code continues its trajectory from the ChatGPT explosion to the Agentic platform transformation, having previously deeply integrated into enterprise workflows through products like Codex; meanwhile, Anthropic, starting from the safety alignment concept of Claude, has seen a rapid increase in the proportion of new contracts in highly regulated industries with Mythos, becoming the most direct threat.
In terms of capital pathways, OpenAI maintains its closed-source full-stack advantage through massive investments from Microsoft and others, while Anthropic gains a safety compliance premium through investments from Amazon and Google, both achieving high ARR growth in enterprise contracts; Meta lowers the deployment threshold for enterprises through Llama 4's open-source model, attracting large clients unwilling to be locked in, while Google secures heavy scenarios like document analysis with its ultra-long context moat.
Similar to the AWS-Azure duopoly in the 2010s cloud computing era or the iOS-Android rivalry in the mobile age, the AI model war in 2026 is currently in a mid-stage of differentiation from general model competition to vertical scenarios and deployment models, focusing on multi-line competition across four dimensions: agent execution, safety, cost, and context length.
Essentially, this represents a technological substitution and capital concentration: top-tier closed-source models replace traditional software and manual processes through agent long-term planning capabilities, while the open-source + long context route forms a complementary structure, driven by enterprises' differentiated demands for safety, cost, and customization, allowing AI pricing power to shift from a single general model to a dual-track structure concentrated in high-end closed-source models from OpenAI and Anthropic and segmented moats from Meta and Google.