A user has stopped using API-based LLMs after finding that Alibaba Cloud's local model, Qwen-3.8-27B, is capable of completing complex software refactoring tasks unsupervised.

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  "detected_at": "2026-09-24T12:59:28+00:00",
  "company": {
    "name": "Alibaba Cloud",
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    "stage": "switched",
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    "post_id": "1wp0z3i",
    "summary": "A user has stopped using API-based LLMs after finding that Alibaba Cloud's local model, Qwen-3.8-27B, is capable of completing complex software refactoring tasks unsupervised.",
    "category": "features",
    "comments": [
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbrf765/",
        "depth": 0,
        "score": 145,
        "author": "caenum",
        "excerpt": "It is good, but only for \"smaller\" projects. I notice that its sometimes a lot of hustle (\"but wait\", \"Now lets check again\"..) and so on. The last project i have done with Qwen-3.8-27B took about 10 hours - with Claude this would have been finished in 30 minutes or something. So yes - its good, but in terms of speed the frontiers are still worth it.",
        "posted_at": "2026-09-24T13:12:34.000Z",
        "author_url": "https://www.reddit.com/user/caenum/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbremm1/",
        "depth": 0,
        "score": 84,
        "author": "tsangberg",
        "excerpt": "Hear hear. I'm \"GPU poor\" (16GB) but I agree. I'm using ByteShape's IQ4_XS together with Raymond's KV cache streaming (context > 160k) and DFlash2 (at low context) and it's faster than any MoE I've used as well as good enough for me to use it as my only model for software dev, cybersec and IT adm.\n\n https://github.com/troed/llama.cpp-adaptive-kv-streaming\n\n Podman containers for sandbox using a se",
        "posted_at": "2026-09-24T13:09:43.000Z",
        "author_url": "https://www.reddit.com/user/tsangberg/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbresxo/",
        "depth": 0,
        "score": 41,
        "author": "Express_Quail_1493",
        "excerpt": "Yes qwen3.8-27b is the first local model that i can run that i can turn away and let if do work without worrying that i will do something stupid.",
        "posted_at": "2026-09-24T13:10:36.000Z",
        "author_url": "https://www.reddit.com/user/Express_Quail_1493/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbrd9b5/",
        "depth": 0,
        "score": 19,
        "author": "Great_Guidance_8448",
        "excerpt": "qwen3.8-27b-nvfp4-mtp has been outstanding for me (with Cline) when hosted in LM Studio with temp set to .1 and the \"Reasoning Budget\" to 1024. The latter definitely eliminated the annoying over thinking.",
        "posted_at": "2026-09-24T13:02:50.000Z",
        "author_url": "https://www.reddit.com/user/Great_Guidance_8448/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbriymd/",
        "depth": 0,
        "score": 12,
        "author": "ailee43",
        "excerpt": "at those level of quants, its pretty solid. You can minimize the overthinking to some degree without much a hit with SWIFT variants.\n\n The problem, as with many local models though, is context. Unless you're GPU rich, you're stuck in the 80k-120k at best context range, and thats with q4 context. Its painful, and you have to setup your harness right so it plans, and breaks the work into small chunk",
        "posted_at": "2026-09-24T13:30:45.000Z",
        "author_url": "https://www.reddit.com/user/ailee43/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbse3b9/",
        "depth": 0,
        "score": 7,
        "author": "relmny",
        "excerpt": "\"the model often has to retry edits, because it messed up the indentation\"\n\n that might be the result of quantizing KV and/or a low q4 quant. If you can try not quantizing KV and/or a higher quant, that might fix most of the issues.",
        "posted_at": "2026-09-24T15:47:48.000Z",
        "author_url": "https://www.reddit.com/user/relmny/"
      },
      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbrowux/",
        "depth": 0,
        "score": 6,
        "author": "bakatristan",
        "excerpt": "27b being this good is actually fucked lol. at this rate the only thing keeping me on API models is 24/7 uptime bc hosting it sometimes is a pain",
        "posted_at": "2026-09-24T13:58:23.000Z",
        "author_url": "https://www.reddit.com/user/bakatristan/"
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      {
        "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wp0z3i/comment/pbsc5iz/",
        "depth": 0,
        "score": 6,
        "author": "MindfulMan1984",
        "excerpt": "Same, it passed the good enough on my use cases. Bye, OpenAI/Anthropic/xAI; you guys can \"slow down\" your \"frontier\" AI at any time. Soon everyone will realize that no one needs \"doomsday AI\" for everyday use.",
        "posted_at": "2026-09-24T15:39:43.000Z",
        "author_url": "https://www.reddit.com/user/MindfulMan1984/"
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    "evidence": [
      "[post] Qwen-3.8-27B is good enough that I stopped using API",
      "[post] it really is able to complete complex refactors on its own, making good decisions along the way. Not perfect, but hey, neither is API.",
      "[comment u/Express_Quail_1493] Yes qwen3.8-27b is the first local model that i can run that i can turn away and let if do work without worrying that i will do something stupid."
    ],
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    "post_date": "2026-09-24T12:59:28.000Z",
    "post_kind": "text",
    "post_text": "Many a praise have been sung on Qwen-3.8, but here is mine.\n\nQwen-3.8 and I had a rocky start, because it thinks so much. Watching it working is painful, so you have to stop doing that. You have to let it work unsupervised. And that's okay, because it really is able to complete complex refactors on its own, making good decisions along the way. Not perfect, but hey, neither is API.\n\nThe model quant is Q4_K_S, context is quantized to Q8_0, which seems to be okay, quality wise. I use the official Qwen. Briefly tried Swift-Qwen, which is indeed faster, but I found it getting trapped in loops, which is very rare in vanilla Qwen.\n\nI am using Qwen-3.8 in the Pi agent without MCP and with the minimum amount of tools. Bash is all you need, but I keep the read, write, and edit tools. The edit tool in Pi is the weakest link, the model often has to retry edits, because it messed up the indentation. I am waiting for someone to come up with a more fault-tolerant edit in Pi. Probably I have to make one myself some day.\n\nAs a sandbox I use docker. My Pi agent is running on a Raspberry Pi, which seems fitting.\n\nOn my hardware and where I live, 1M tokens cost 2.4 cent (input) and 70 cent (output) which is comparable to the cheapest providers on nano-gpt.com.",
    "sentiment": "positive",
    "subreddit": "LocalLLaMA",
    "event_date": "2026-09",
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    "post_title": "Qwen-3.8-27B is good enough that I stopped using API",
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    "subreddit_url": "https://www.reddit.com/r/LocalLLaMA/",
    "total_upvotes": 641,
    "comments_total": 267,
    "total_comments": 266,
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