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).
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).