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Comment by probst | Hacker News Reader

Would you please elaborate on "really decent"? Really decent for what use exactly? I want to love it (and use it as a cheaper research model in my harness), but I really cannot get any real development done with it? It stumbles and fumbles and over-complicates, where astra just plows ahead (often in less overall time) with excellent solutions.

I really want to love this model, particularly since it's good to have an open weight model on my team so to speak, but I think I might be holding it wrong.

What are you using it for?

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I use that specific model as my primary LLM for coding and diagnostics on projects in Python, C++, CUDA, and for web research.

I'm also using it with my own harness (you mentioned "in my harness"), so I'll share two things I found about using open weight model providers. The first is that getting the configuration right isn't a per model thing, its a per model per provider per point in time thing. The point in time thing comes from middleware updates as those introduce or fix defects that can very seriously impact inference quality. The second thing is inference quality varies a lot between providers, even when they are ostensibly using the same quant of the same model.

If you want to try it something else for comparison I open sourced my harness a couple of weeks ago: https://fluximp.org

I recommend trying deepseek v4.1 flash from together.ai (not the 1017 one the one released last month). deepinfra.com is cheaper for that but its noticeably slower and the inference from together.ai feels like it might be better (not quantified; just vibes).

Thank you for the link to your harness! Looks quite interesting.

What type of ongoing tuning do you do for your model+provider+harness combo, and when do you know it's time to redo it? Or more specifically, when you do the tuning, how do you actually go about doing it?

I am mostly using omp (omp.sh) which is very tunable, and I did some tuning for one of the GLM models in the past (mostly based on vibes because people said it was getting dumb like a brick after some context lengths so I had omp compress sooner).

I can have 2-3 sessions running at the same time for pretty much the entire working day with DSF4.1 where the responses are fast, cost is pretty negligible, and the results are good enough 95% of the time.

On an overall speed/cost/quality comparison, it seems unmatched in my experience these days.

I've been playing with the fast mode on BaseTen - clearly a different cost bracket, but WOW - the speed is phenomenal! A real game changer to be able to stay in flow.

If you have well-defined, long running tasks that are relatively disposable but requires constant ML monitoring and overwatch e.g. web crawlers, monitoring prices, logs etc. DS4 Flash is perfect for those.

For tasks like crawling and real time log analysis etc., the costs really add up at scale.

Ah, interesting use cases for DS! I haven't had these needs yet, which maybe also explains why I haven't considered using DS for them. Thanks!

Coding. What were you using last year? It's better than that.

Last year I didn't use any of the model (other than for chat and some snippets here and there), exactly because I didn't think them good enough. It should be said that I think DS is vastly better than that, but I think the closed source frontier models are just so good by now, that stepping down to "N-X" months just feels all the more painful.

Opus 4.6 was descent last year already

Anthropic released Claude Opus 4.6 on February 5, 2026. What year are you in, man?

thank you for nitpicking. Whatever was in Dec 2025 was very good already.

For agentic coding, Qwen 3.8-27B is not far from that, and Qwen 3.8 Next is there. Never used DeepSeek 4.1 flash but I'd be surprised if it wasn't better than this.

Yes, people should say if they are using it for HTML/CSS/Javascript or Rust/C++; it makes a huge difference.

Yeah, good point. For HTML/CSS/JavaScript it seems to do fine. Maybe I am just asking too much of it, with a codebase spanning Rust and Elixir and doing some more low-level stuff in-between...

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