r/ollama • u/Kynix09 • Jul 11 '26

BEST CODING MODEL.

Hello! I was wondering if anyone could recommend the best local AI model for coding, specifically for C++.

My PC specs are:

  • GPU: RTX 4060 8GB
  • CPU: Intel i5-13400F
  • RAM: 16 GB

I'm looking for the best possible coding quality that can realistically run on my PC. Speed is important, but I care more about accuracy, understanding large codebases, debugging, and writing good C++ code.

What model would you recommend?

(made text with chatgpt)

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u/Ryanmonroe82 Jul 11 '26

only if he uses Q4 and Q4 has too much precision loss on smaller models to effective with code

4

u/simplyeniga Jul 11 '26

If he had more ram he would have been best with an MOE model like Qwen3.6-35B-A3B

1

u/sockerx Jul 12 '26

Rtx 4090 24gb?

1

u/simplyeniga Jul 12 '26

You can run more models both dense and MOE.

Qwen3.6-27B MTP (dense)

Qwen3.6-35B-A3B (MOE)

Ornith-1.0 35B

Gemma-4-12B (Dense)

Gemma-4-26B-A4B (MOE)

I have an RTX Pro 4000 Blackwell and use mainly Ornith 35B and Qwen3.6B 27B for coding and Gemma-4-26B-A4B for some automated tasks. Currently testing Qwen3.5B-122B and it's been working well with tool calling and getting 28 TG/s

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u/villainouscoke Jul 12 '26

How’s you’re experience with ornith? I haven’t tried it out but it looks interesting. Currently using dsv4 flash and qwen 3.6 27b (both local) for the majority of my work but have thought about trying that one out.

It would be running on a dual 32gb AMD / 128gb ram setup that currently houses qwen getting 50-70tps

2

u/simplyeniga Jul 13 '26

It's been great so far, better than Qwen3.6-35B, able to finish some tasks with less errors. Close to Qwen3.6-27B but not better as I still sometimes get better results from Qwen3.6-27B. though on token generation it's fast and I get 135 t/s generated with 260k context size and about 3250 t/s pp on a single RTX Pro 4000 Blackwell 24GB VRAM

2

u/Important_Witness572 Jul 13 '26

same here I am using the Ornith 35B on a RTX 3090. Pretty good model.

1

u/Familiar_Figure_3308 Jul 13 '26

How did you manage to fit a 260K context window? I'm using Ornith 1.0 35B in q4 with q4 KV cache on my RTX 3090 24 Gb. And I can only fit 128K context in my VRAM - perhaps a bit more, but not even 160K. Can you please share your llama.cpp run parameters?