OpenClaw Pro Guide: From Intermediate to Advanced

Recently, everyone has been raising lobsters, but which model is best for open cloud? Climbing shrimp recently built a lobster model test framework. Several mainstream models were selected for horizontal comparison. Among them, there are GPT-5.3, Gemini-3, and Rustic. The world's first lobster model, the GLM-5 Turbo, has just been released. It has caused heated discussions in overseas communities. Here's a quick sponsor message. This AI API aggregation platform for domestic and international large models lets you access 500-plus AI model APIs at lower cost and with faster integration. Kling V3 is priced at $0.07 S, and the Nano Banana 2 costs $0.025 per call. Overall pricing is only 20% to 80% of the official rates with token costs as low as RMB 0.05 per million. It also offers an online playground, so you can freely use text, image, and video models with unlimited concurrency. If a request fails, you'll be refunded. Just click the link in the video description to go directly there. If you want discounts or run into any issues, join our Discord community for prompt support or add customer service and send the message six for one-on-one assistance. All right. Back to the video. I got the API of the early version of Pony Alpha 2 as soon as possible and connected to open cloud. Next, let's take a look at the testing process and results. We are on a device with open cloud installed. Enter the open cloud config command to select the configuration model, then select the vendor. Here I choose Z.A.A., which is Rustic. I am API key intervention type selection. C, and then I filled in the API key of the simple official website and entered all the way. Next, we come to the open cloud configuration file. Add this paragraph to the list of Rustic models because I am a preemptive tester. Here I fill in its nickname, Pony Alpha 2. At the time of the video release, the official version has been released, so we need to fill in its official version name, GLM 5 Turbo. In the agent's model, it should also be configured on the official version, GLM 5 Turbo. Next, I configured GPT 5.3 Codex in a similar way. Gemini Flash 3 also has a domestic flagship model, M2.5 Open Cloud's most commonly used usage scenario is file processing information collection. There are also timed missions. Here I installed three for Open Cloud. Skills. Docs is used to process Word documents. PPTX is used to process. PPT also has a table to search for web search. I am going to use a few work scenarios that I often encounter. Test the performance of these models on Open Cloud. I put a commented Word document on the desktop. I'm going to have a few AAs modify it based on the comments. Here I will clean up the context in the Open Cloud dialog interface first, then enter the model command, switch the model to GPT 5.3 Codex. Next, enter the prompt. There is an annotated contract on the desktop. You follow the comments to modify it, and after a while GPT finishes the work. Let's open it and take a look. There is a big problem here. It changes the original document format. The title has become the same font size as the main text. Next, let's try Gemini Gemini again, and Gemini's performance is relatively poor. The output file is incomplete. The lower part is truncated. I went to try Gemini on my Windows computer again. This file won't open on Windows. Next, let's try the domestic model M2.5, which has one. The problem is that only the simple and easy parts have been modified. The complex was not modified. Finally, let's try the GLM 5 Turbo GLM 5 first. The first feeling I get from the Turbo is that it's fast. Other models took 3 to 4 minutes. He completed the task in just 2 minutes. All 13 P points have been modified. The format is also fully preserved. In this Word document modification task, JRM 5 Turbo performs at its best. Let's look at another example. Search for several open source alternatives to Open Cloud. Output your research results on the desktop in a PPT. Let's take a look at the results of GPT 5.3 Codex first. What he searched for was some generic agent framework, not a true Open Cloud competitor, and the PPT content is relatively simple. Several other models have similar problems. What is retrieved is not a true Open Cloud competitor. Only JRM 5 Turbo fully understands what I mean. I searched for several Open Cloud open source competitors. In this example, JRM 5 Turbo has stronger instructions. Follow to understand the user's meaning more efficiently and dismantle complex instructions. GLM can make more accurate and stable calls to various external types, tools, and various skills such as understanding of PPT. It is better than a few other models. Does he know how to highlight the key points? How to use color typography? Make PPT more beautiful. Scheduled tasks are Open Cloud's framework compared to other agents, the biggest advantage and feature. If you want Open Cloud to truly become a productivity tool model, it is necessary to have the ability to continue to promote the Great Wall mission. Here I designed a complex test force. It is used to test the propulsion ability of the Great Wall mission of the model. From now on, we will promote a continuous task of self-media creation. I need to show all my progress in this dialogue. Conduct an in-depth study immediately, then output the outline, script, title, cover, word slip, etc. in strict chronological order. Let's send this prompt to Gemini first. Here Gemini is just the first step. There is no output later. I can't see where it has been performed in the dialogue interface. I asked many more questions later. Gemini finally completed the task. Gemini puts the output under a folder in the working directory, but I didn't see the cover image. Next up is GPT 5.3 Codex, which has a similar problem. The output can also be synchronized correctly in the second step. But the downward execution is stuck. I waited for a few minutes and didn't see the output and asked it again several times. Finally completed the task. The files output by GBT 5.3 codex are relatively comprehensive. One problem is that the script is too AA flavored. Here is the first, second, third, fourth. There is a large amount of such writing. The writing is relatively stiff. Next is M2.5 and there is no problem with M2.5. Use open cloud scheduled task system correctly. Instead, he wrote a wait script himself to control the time. This waiting script has no way to precisely control the execution time. Another problem is that the output results are scattered. The research report is in memory and other files are placed in a temporary directory. Cover, put in the root directory. Finally, let's take a look at the GLM 5 Turbo. This is the only one of all models that can use the immobilizer correctly and write all the processes clearly and correctly into the chat window model. What did each step do? What files are output? Everything to do next is clearly described. Let me keep track of the progress of the task at any time. GLM 5 Turbo puts all the output into the project inside a subfolder under the directory and it's numbered. It was very clear and organized. In this test, JRM 5 Turbo also has a great wall mission for fixed objects. Showed very good processing ability. It correctly understands instructions in the time dimension. Accurate processing. Fixed things. It ensures that it is in a nested assembly line with multiple links and multiple outputs. Ability to perform tasks continuously without interruption. According to Zhipu's official introduction, GLM 5 Turbo continues the powerful programming capabilities of the flagship model. Programming blindly. Generally, I still choose cloud code instead of open cloud because open cloud consumes tokens too badly. Here I connected GM 5 Turbo to cloud code. Then I turned on cloud code's latest agent teams feature. Let it automatically generate multiple agents to work. Let's design a complex application with a front and back to test the model's capabilities. There is a PRD document in the directory. Develop an open cloud monitoring panel based on this document. Note that to enable the multi-agent collaborative development of the agent team's function, this is more efficient. This document contains a design of my open cloud monitoring panel. Contains the directory structure of the technical station. How did the data come about? First, call open cloud CLI regularly, write the data to the cache, and write an HTTP interface. Read from the cache file. GLM 5 Turbo reads my PRD design file. It splits the task into five modules. Then three agents were activated. Start parallel development. We see that GLM 5 Turbo is able to use Cloud Code's latest agent team's features perfectly. After effectively commanding the division of labor and cooperating with multiple agents, the use of the agent is completed. It is also capable of properly turning off agents and clean up the team. I didn't intervene at all. It ran continuously for 15 minutes. The development of all functions is complete. Let's take a look at the effect of development. It includes four modules: instrument panel, agent, and memory usage. The dashboard shows that it is starting.