> For the complete documentation index, see [llms.txt](https://docs.cherryai.com.cn/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cherryai.com.cn/docs/en-us/other/model_rank.md). # Model Rankings Model rankings are used to help compare the relative performance of different models and should not be used alone as the basis for model selection. Data comes from [Arena AI](https://arena.ai/), and is automatically updated daily by GitHub Actions. When choosing a model, you should also consider task type, context length, multimodality and tool-calling capabilities, speed, price, regional availability, and data policies. Rankings and prices change dynamically; please refer to the update time marked on the page and the official information from the model provider. ## Ranking directory * [Agent rankings](broken://pages/ac02831996a47ff086ebf348dc2dc92ef70fd4c5): This ranking evaluates models' overall performance on multi-turn tool-calling / agent tasks, covering metrics such as net improvement rate, confirmation success rate, thumbs-up/down ratio, controllability, Bash recovery rate, and tool hallucination rate. * [Text rankings](/docs/en-us/other/model_rank/text.md): This ranking is Arena AI's core text battle ranking, estimating models' relative strength based on human blind-vote battles. * [Search rankings](/docs/en-us/other/model_rank/search.md): This ranking evaluates models' performance on web-connected search / information retrieval tasks. * [Vision rankings](/docs/en-us/other/model_rank/agent.md): This ranking evaluates the performance of multimodal models on image understanding tasks. * [Code / Web development rankings](/docs/en-us/other/model_rank/code-webdev.md): This ranking evaluates models' practical performance in Web front-end development (HTML/CSS/JS) tasks. * [Text-to-image rankings](/docs/en-us/other/model_rank/text-to-image.md): This ranking evaluates the ability of text-to-image models to generate images from text prompts. *** ### 💡 Get help and submit feedback If you encounter any questions, bugs, or have suggestions for feature improvements during configuration or use, please refer to [Feedback and suggestions](/docs/en-us/question-contact/suggestions.md) for the official channels provided. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.cherryai.com.cn/docs/en-us/other/model_rank.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.