I did play with Qwen3 Coder Next a bit, but didn't try it in a coding harness. Will give it a shot later.

No, it is not cheaper. An M3 ultra with 512GB costs $10k which would give you 50 months of Claude or Codex pro plans.

However, if you check the prices on Chinese models (which are the only ones you would be able to run on a Mac), they are much cheaper than the US plans. It would take you forever to get to the $10k

And of course this is not even considering energy costs or running inference on your own hardware (though Macs should be quite efficient there).

There's an AMA happening on reddit and they said it will be fixed in the next release: https://www.reddit.com/r/LocalLLaMA/comments/1r8snay/ama_wit...

I think there are multiple ways these infinite loops can occur. It can be an inference engine bug because the engine doesn't recognize the specific format of tags/tokens the model generates to delineate the different types of tokens (thinking, tool calling, regular text). So the model might generate a "I'm done thinking" indicator but the engine ignores it and just keeps generating more "thinking" tokens.

It can also be a bug in the model weights because the model is just failing to generate the appropriate "I'm done thinking" indicator.

You can see this described in this PR https://github.com/ggml-org/llama.cpp/pull/19635

Apparently Step 3.5 Flash uses an odd format for its tags so llama.cpp just doesn't handle it correctly.

Haven't tried. I'm too used to llama.cpp at this point to switch to something else. I like being able to just run a model and automatically get:

- OpenAI completions endpoint

- Anthropic messages endpoint

- OpenAI responses endpoint

- A slick looking web UI

Without having to install anything else.

Is there a reliable way to run MLX models? On my M1 Max, LM Studio seems to output garbage through the API server sometimes even when the LM Studio chat with the same model is perfectly fine. llama.cpp variants generally always just work.

Both gpt-oss are great models for coding in a single turn, but I feel that they forget context too easily.

For example, when I tried gpt-oss 120b with codex, it very easily forgets something present in the system prompt: "use `rg` command to search and list files".

I feel like gpt-oss has a lot of potential for agentic coding, but it needs to be constantly reminded of what is happening. Maybe a custom harness developed specifically for gpt-oss could make both models viable for long agentic coding sessions.