 | I gave some math problems to Qwen 3.5 27B and Qwen 3.6 27B and they got all of them right, pretty smart models I would say, but very slow and electricity consuming, they took like 5 mins with my GPU at 120 W to solve a problem. The MoE models answer quite fast but their answers feel generic, I wouldn't use them for problem solving, but to study or to learn something new, they can work as a Wikipedia if i'm without Internet. Of those, the one that I most used is Qwen3-Coder-30B, I really like this one, but it's an old model. In the beggining of the year I also used a lot of GPT-OSS 20B. [link] [comments] |
Sorry if it's not the best place to ask this, of the models in the image, which is the best for (problem solving)/Coding and the best one for studying (ask LLM concepts) ? My PC build is RX 9060 XT 16GB + I3 12100F + 16 GB DDR4 + llama.cpp with Vulkan backend + Linux Mint.
Reddit r/LocalLLaMA / 4/29/2026
💬 OpinionSignals & Early TrendsTools & Practical Usage
Key Points
- The post compares several locally run LLMs (Qwen 3.5 27B, Qwen 3.6 27B, MoE models, Qwen3-Coder-30B, and GPT-OSS 20B) on a user’s Linux Mint setup using llama.cpp with a Vulkan backend.
- Qwen 3.5 27B and Qwen 3.6 27B are described as very accurate for math/problem solving, but they are also reported as slow and power-hungry (about 5 minutes at ~120W per problem).
- The MoE models are noted to respond faster, but the answers are perceived as more generic, making them less suitable for rigorous problem solving.
- For studying and learning LLM concepts offline, the user suggests using the faster MoE models as a “Wikipedia-like” resource when internet access is limited.
- Qwen3-Coder-30B is highlighted as the model the user most likes for coding, while noting that it is an older model.
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