A recent analysis on CSDN compares four leading large language models—DeepSeek v4 Pro, GLM 5.2, Kimi K2.6, and GPT 5.6 Sol—on DigitalOcean's serverless inference platform. The study evaluates cost per token and capability across common tasks, revealing significant differences in efficiency and performance. For developers and cloud architects, this comparison offers practical insights into selecting the right LLM for cost-sensitive, scalable applications. The findings suggest that while GPT 5.6 Sol leads in capability, DeepSeek v4 Pro offers competitive performance at a lower cost, making it a strong candidate for budget-constrained projects. As serverless AI inference gains traction, such benchmarks are crucial for informed decision-making.
This post compares four major large language models—DeepSeek v4 Pro, GLM 5.2, Kimi K2.6, and GPT 5.6 Sol—on DigitalOcean's serverless inference platform. It highlights cost per token and capability differences, providing valuable guidance for developers choosing an LLM for production. The comparison is timely as serverless AI inference becomes more mainstream.