Model comparisons
Head-to-head write-ups: what the benchmark gap actually means, and which model fits which job.
GLM-5.2 与 Kimi K2.6 对比:10 项评测的胜负、许可差异与部署门槛
GLM-5.2 在双方共有的 10 项评测中赢下 9 项、平均分高 6.6 分,Terminal-Bench 2.1 一项就差 27.4 分;Kimi K2.6 的体量大出三成,且许可是 Modified MIT 而非标准 MIT。
GLM-5.2 相比 GLM-5.1 提升了什么?两代模型的评测数据与升级取舍
GLM-5.2 在双方共有的 11 项百分制评测中全部领先、平均分高 6.4 分,但增益高度集中——Terminal-Bench 2.1 从 58.7 跃至 81.0,而常识推理几乎原地踏步。
DeepSeek-V4-Pro 与 GLM-5.2 怎么选?两大开源旗舰的能力分野与部署成本
两款 MIT 许可的开源旗舰,总平均分只差 1.7 分,但强项几乎不重叠:GLM-5.2 在前沿数学上以 29.3 比 2.4 碾压,DeepSeek-V4-Pro 在仓库级编程上反超 18.7 分。
AI Model List FAQ
How often is this AI model list updated?
New models, version bumps, pricing changes, and benchmark results are added as soon as they are published — typically within hours of an official announcement. Once the data is in, the page refreshes within 5 minutes so visitors always see the latest information.
What do the "Open Source" filter options mean?
A model can be open source (weights publicly available) yet still restrict commercial use through its license — for example, some Llama variants prohibit large-scale commercial deployment. The three options reflect this: "Free for commercial use" means no restrictions for production; "Paid commercial" means a license fee applies; "Not for commercial use" means the model cannot legally be used in commercial products.
How do I pick the right model for my use case?
Start by filtering on capability (chat, coding, reasoning, multimodal) to narrow the field, then compare the top candidates on the benchmark closest to your workload. For production, also weigh API pricing, context length, and license — a model that wins on benchmarks may still lose on total cost of ownership.
Which organizations' models are included?
The list covers mainstream model publishers worldwide — including OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Alibaba (Qwen), Zhipu (GLM), Moonshot (Kimi), and many others. Use the publisher filter to narrow to a specific lab.
Can I see the full benchmark results for a specific model?
Yes — click any model card to open its detail page, where you will find the complete benchmark table, parameter sizes, context window, license, API pricing, and links to the official paper, Hugging Face, and GitHub repository.























