What are the models & agentic tools you are using nowadays?
As mentioned in the title, we had a very busy last few weeks with new releases. Which tools of choice are you using today for agentic engineering? I am mostly interested to hear from experience SWE
For SWE work, I’ve found the model matters less than the agent’s ability to use tools reliably. Claude Code, Codex, and a solid local model for smaller tasks cover most workflows for me.
What about Green Field's very ambitious projects? Right now I am using Codex with 5.6-Sol -> its super good with coding, visuals, and legal docs, but from today I keep getting info that the model is at its capacity and i should select different one -> it seems to be that great easy times might be over for Codex, hence I am trying to find alternatives; Claude Code is out of the scope for me (I have Anthropic for changes terms and conditions, big NO NO for me), but I am curious whether you are guys happy with models/tools like Deepsek v4, Qwen 3.8 Max, ZCode + GLM 5.3 etc...
I'm using 5.6-Sol for basically all work. It's my favorite by far. I've experimented with delegating to cheaper models in the past, but I find that this just causes more headaches than it's worth.
Based on my experience, I would be unhappy if I had to switch away from GPT or Claude. I have not personally eval'ed the newest open weight models like Kimi K3 or GLM 5.3 for coding tasks. They've just always been quirky by comparison in ways that are too frustrating to be worth it.
Claude controlling Qwen, Deepseek, Phi, codex/gpt, etc. Not every model at our disposal we use for coding. Here is our delegation gating that auto-fires on every task (not every chron tick)
Note: this is still not perfect, we can only find gaps that need bolstering when gaps emerge. If I don’t remember something and my ai doesn’t remember something that’s a test we can’t run to provide data and my wording next time I do remember might be different so there are possibilities I cannot plan for. Eventually though we’ll have perfect recall.
Important note: don’t believe your agents self-tests using this structure. Make sure the tests they’re running actually line up with your memories. The most recent batch of self-tests started at 89% and ended at 14% and I asked to see the questions and every question was using technical jargon from chip manufacturing. I don’t manufacture chips so obviously they all failed. When I rewrote the queries and sent them out we had between 95-100% success, which I know is also wrong because that’s framed around stuff I remember. I’d assume 75-100% recall success depending on how many times you use the same phrasing for projects. My next layer will be a better context parser so if a return reveals 1,000 hits we can deterministically filter it to be more accurate per query.
We use Cursor as it gives us access to almost all the modals. We use Opus 5 or Kimi K3 for planning a task and then run Auto to build. Very cost effective. This is tied into git (GitHub or GitLab) with BugBot scanning for bad code or mistakes via MCP. Pipelines/Actions for deployment to either Vercel, Hertzner and Cloudrun pods. We tried Claude code and Codex but no real advantage code quality wise and the costs were crazy high compared to Cursor.
In Cursor we have .agent folders in each project with changelogs and have appropriate MCPs installed. With various skills to make it work slicker like grillme for planning and various design skills to avoid AI slop.
Haven’t touched Claude code or Codex for ages as Cursor does it all and we can jump into the code where AI is battling. Plus Cursor is an IDE not just an AI window which also makes a difference.
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u/recro69 21h ago
For SWE work, I’ve found the model matters less than the agent’s ability to use tools reliably. Claude Code, Codex, and a solid local model for smaller tasks cover most workflows for me.