[tarruda]

This is probably one of the most underrated LLMs releases in the past few months. In my local testing with a 4-bit quant (https://huggingface.co/ubergarm/Step-3.5-Flash-GGUF/tree/mai...), it surpasses every other LLM I was able to run locally, including Minimax 2.5 and GLM-4.7, though I was only able to run GLM with a 2-bit quant. Some highlights:

- Very context efficient: SWA by default, on a 128G mac I can run the full 256k context or two 128k context streams. - Good speeds on macs. On my M1 Ultra I get 36 t/s tg and 300 t/s pp. Also, these speeds degrade very slowly as context increases: At 100k prefill, it has 20 t/s tg and 129 t/s pp. - Trained for agentic coding. I think it is trained to be compatible with claude code, but it works fine with other CLI harnesses except for Codex (due to the patch edit tool which can confuse it).

This is the first local LLM in the 200B parameter range that I find to be usable with a CLI harness. Been using it a lot with pi.dev and it has been the best experience I had with a local LLM doing agentic coding.

There are a few drawbacks though:

- It can generate some very long reasoning chains. - Current release has a bug where sometimes it goes into an infinite reasoning loop: https://github.com/ggml-org/llama.cpp/pull/19283#issuecommen...

Hopefully StepFun will do a new release which addresses these issues.

BTW StepFun seems to be the same company that released ACEStep (very good music generation model). At least StepFun is mentioned in ComfyUI docs https://docs.comfy.org/tutorials/audio/ace-step/ace-step-v1

[sosodev]

Have you tried Qwen3 Coder Next? I've been testing it with OpenCode and it seems to work fairly well with the harness. It occasionally calls tools improperly but with Qwen's suggested temperature=1 it doesn't seem to get stuck. It also spends a reasonable amount of time trying to do work.

I had tried Nemotron 3 Nano with OpenCode and while it kinda worked its tool use was seriously lacking because it just leans on the shell tool for most things. For example, instead of using a tool to edit a file it would just use the shell tool and run sed on it.

That's the primary issue I've noticed with the agentic open weight models in my limited testing. They just seem hesitant to call tools that they don't recognize unless explicitly instructed to do so.

[tarruda]

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

[ipython]

Curious on how (if?) changes to the inference engine can fix the issue with infinitely long reasoning loops.

It’s my layman understanding that would have to be fixed in the model weights itself?

[tarruda]

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...

[sosodev]

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.

[petethepig]

Is getting something like M3 Ultra with 512GB ram and doing oss models going to be cheaper for the next year or two compared to paying for claude / codex?

Did anyone do this kind of math?

[tarruda]

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).

[terhechte]

Did you try an MLX version of this model? In theory it should run a bit faster. I'm hesitant to download multiple versions though.

[tarruda]

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.

[KerrAvon]

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.

[lostmsu]

gpt-oss 120b and even 20b works OK with codex.

[tarruda]

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.