Is Open Source Better Than Closed Source AI?

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The narrative in tech circles is that open source AI is better and that DeepSeek is proof of it. After all, China built competitive models cheaper than OpenAI, the code is public, and anyone can download it. The closed-source era might be outdated.

But that is a misreading of what actually happened. Open and closed source AI are not competitors in a race toward a winner. They are structural positions in an arms race that will both grow simultaneously.

Let’s start with the numbers. Open-source LLM releases nearly doubled compared to closed-source models since early 2023. On-premises solutions control more than half of the LLM market, with projections indicating continued growth (n8n, 2025). These trends are only part of the story, however .

The other part is that OpenAI, Google, Anthropic, and Meta are all still operating at the frontier of capability. GPT still outperforms most open-source models on complex reasoning tasks. Gemini still handles multimodal problems better than available open alternatives. The closed-source companies are still investing aggressively in capabilities that exist nowhere else.

What has changed is not the competitive outcome. What has changed is the role of open source in the ecosystem.

Open-source models now serve a different function. They democratize access to AI capabilities that already exist in closed-source versions. They allow organizations to fine-tune models for specific use cases without paying per-API fees. They provide transparency that closed-source systems cannot match (Aquent, 2025). They enable companies to reduce vendor lock-in by deploying models locally as well.

These are proper advantages, but they are not advantages in a race toward the frontier. They are advantages in operationalizing the frontier that others have already reached.

A startup training a specialized model using Llama 3, for example, is not competing with OpenAI to build the next breakthrough. The startup is building a production system using models that OpenAI research has proven are possible. The innovation already happened, and so the startup is capturing value from that innovation by adapting it efficiently (The Ninja Studio, 2025).

This distinction is crucial to explain why both open and closed source will grow together. The closed-source companies need funding and market justification for their massive R&D investments. That justification comes from having capabilities that open-source models do not have. OpenAI had once released GPT-4 and started charging for it. OpenAI had also trained GPT-2 and released it for free when it was already outdated. The strategy is that frontier capability stays proprietary. Previous-generation capability becomes open source once it no longer justifies the R&D spend.

Meanwhile, open-source models create a stable platform on which organizations can build. They reduce the cost of deployment. They eliminate the vulnerability of depending on a single commercial provider. But they require maintenance, security patching, and ongoing optimization. Organizations using open-source models still pay. They just pay for infrastructure and engineering instead of per-API fees.

But the future is not open source coming

out on top. The future is both models coexisting in a stable equilibrium. Closed-source platforms drive frontier capability development and capture premium value from early adoption. Open-source models democratize access to previous-generation capability and reduce operational costs for organizations that do not need the frontier. The companies releasing open-source models get brand positioning as generous and community-focused. They also get to maintain pricing power for their proprietary offerings.