{
  "video_id": "reddit_1uh8von",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Ornith 35B  is great so far",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1uh8von/ornith_35b_is_great_so_far/",
  "external_url": null,
  "upload_date": "20260627",
  "published_at": "2026-06-27T17:39:19+00:00",
  "transcript": "Tried creating a quick 3d game with it, after 3 prompts, it got me this(checkvideo). If I compare this with qwen3.5-35b-a3b, it was not able to successfully generate this and was failing even after multiple prompts.\n\nHarness: Claude Code\n\nHow is your experience so far ?\n\nhttps://reddit.com/link/1uh8von/video/csrnhpwy2v9h1/player\n\n\n\n--- Top Comments ---\n\n\n[27 upvotes] Interesting. I tested the q6 quant against the Qwen3.6 35b q6 quant. Used llama-cpp as the backend with OpenCode as harness. In terms of tool calls it did fine (so did Qwen) but Qwen managed to beat it in terms of task/token efficiency across all the 3 tasks I tested.\n\nFor context it was an architectural refactor on a large Kotlin codebase. \n\nBoth the models managed to complete the task but Qwen's solution was better designed, more feature complete and introduced lesser bugs. \n\nOrnith also struggled with writing code. A lot of syntax and compile time errors that Qwen had no problems with.\n\nIt also seems to output lesser reasoning tokens compared to Qwen but the tokens saved because of this gets offset by the amount of tokens it then has to generate to correct all of the failed/incorrect edits it make. QWEN on the other hand seems to reason a lot more, and is less confident in general, but it also helps it get things done on the first try.\n\n[12 upvotes] I tested the q8_0 of 9B and it was worse than qwen3.5 9B.\nI don't know what's happening.\n\nI'm using in llama.cpp.\n\nIf someone know about something about why it's so worse than qwen3.5 9B and could help me just explain.\n\n[6 upvotes] I'm using it in Q8 with FP16 Kvcache on two R9700 GPUs. I built a Go project with about 100K context using OpenCode, and I compared the results to Opus and GPT, both rated the development a 9/10. \n\nAs for performance, I haven't tracked it extensively but it's holding steady above 60 tokens/second during generation, and the prefill hasn't felt slow. GPUs are capped at 210W\n\nVRAM usage is around 42GB for a 262K context window.\n\n[3 upvotes] I've used Ornith 397B 3bpw EXL3 quant with OpenCode a few hours yesterday. I can't tell it apart from Qwen 3.5 397B yet but I'm not done giving it a chance yet.",
  "transcript_chars": 2171,
  "ingested_at": "2026-06-28T01:30:24.599992+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 54,
    "upvote_ratio": 0.85,
    "num_comments": 41,
    "author": "anubhav_200",
    "is_self": true
  }
}