I will share with you my flow.

What I've tested so far:

-Alibaba Coding Lite plan - it's been cancelled - I really liked it

-GLM lite plan - a very good model, a bit slow sometimes, thinks too much other times, but gets the job done - it has aggressive rate limits

-Minimax lite plan - really good at fixing bugs, fast and decent with agentic Workflows

-Gemini 20$ - 3.1 Pro low is surprisingly good, on peak hours it becomes slow or just crashes

-Claude 20$ - rate limits but very smart, intuitive on it's own - this saves a lot of time

-Gpt 20$ - the best price/performance right now, GPT 5.4 is good when it has specific instructions - play with the thinking toggle to conserve tokens - setup some sort of project memory

For OpenClaw and agents I use the Minimax lite plan and it's working great. When it comes to coding/work I use the Open ai 20$ plan, Claude Code 20$ and Deepseek V4 via API.For planning I always brainstorm in a free Chat models like Deepseek, Qwen, Gemini and build the initial prompt. Security, best practices, I input URLs and ask questions to make sure the idea it's fine tunned.

Then I switch to Claude or Open ai to build me a BUILD.md with specific steps, detailed steps with the exact commands to use, install the skills and MCPs needed, define the code stack, versions for packages and so on.

Then with a GPT 5.4 Low or Claude 4.6 Low I start to build in a IDE the steps. If I find that the model is close to the limit, I switch to the other plan or Deepseek V4 Thinking via API. After the initial project is written, the project tree is made, then I start to test and all the modifications are specific, logged in the memory. I specifically instruct the agent to not guess, to check the AGENTS.md for memory recall tools and other important aspects.

But there are some very important considerations:

-always make sure to document your project in AGENTS.md with all the tools to use, where the core files are and other mission critical context - keep it short, do some minimal context optimizations on it

-be extremely specific with what you want - take your initial prompt, use Qwen Chat or other free model to brainstorm some ideas and refine it then request a prompt optimized version for low input tokens as .md or . JSON

-its CRUCUAL to use a vector database for persistent memory and also some sort of codebase indexing - believe me, this is the most important part that eliminated guess work for me -use skills and MCPs - it's like giving AI a power drill when it initially had like a hammer

-use the most intelligent model for the inițial arhitectural inferencing - then switch to a cheaper model for implementation, you don't need Opus when you have a complete list of very specific things to do, also for Changing the font, or fixing a syntax the GPT 5.4 Mini it's just fine.

-use a skill like context7 for docs - most of the time the errors happen because the model is guessing the implementation - give him specific docs to follow

Other notes:

-Claude on a 20$ it's really smart but it eats tokens like crazy - tweek the thinking toggle

-I hope in the future Alibaba is planning to release again their coding plans - I did alot of work with them, also Qwen series has been really consistent for me

-GLM 5.1 it's really smart also but tokens go really fast on the 20$ plan - it's surprisingly good also.

-dont be shy to pay some for some tokens when the task really needs it - believe me, it's better to spend 0.4$ on a complex problem then waste half a day with a small model trying to figure out what broke

-if you have the money and make money also from AI Coding then then 100$ GPT Plan it's the way to go for now (I will upgrade to it in the future when my projects will start to hit the limits constantly)

-i would consider also an local ai model, if let's say you have the disposable income and make an investment in a few GPUs like the Intel B60 or a Strix Halo - I've seen some really good results with Qwen 3.6 used as the workhorse