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  • Hacker News
  • How would you best utilize a model like this for coding? I take it it's not meant for vibe coding a full app, and the reasoning probably makes it unsuitable for autocomplete. Would you use it to implement specific functions? I looked at one of the coding benchmarks used, Live Code Bench, and it seems to be problem descriptions with sample input and output, and then a solution with a single function or class.

    Seems like a really good model to use in an IDE when you still want control over the code structure then.

    by sorenjan
  • Not sure if it's suited for that. If you read the article it's stated that it is basically a research project to see how far they can push it with small models.
    by aswegs8
  • Notable:

      VibeThinker-3B is developed through a staged post-training pipeline built upon Qwen2.5-Coder-3B base, a compact 3B foundation model.
    Qwen2.5 is ancient by LLM standards.
    by nolist_policy
  • It's terrible at hunting security bugs (I expected it to be, but I wanted to be sure). I added it to a benchmark I made with a corpus of some Mythos-discovered bugs, and it found zero. The smallest pretty successful models remain Qwen 3.6 and Gemma 4 (but I haven't tested the very small variants of those yet).

    https://swelljoe.com/post/will-it-mythos/

    by SwellJoe
  • The lack of tool use will hinder it a lot I think, since bug hunting requires collecting context across a code base and stitching it together. It might be good in a more narrow sense, i.e "is there a bug in this block of code" and not considering how it interacts with the rest of the code base.

    That's also more aligned to its leetcode style training data, the code under test is fully in the context window. It might be interesting to have a bigger tool use model go through the effort of collecting the context, and feeding it into this kind of model for analysis only. It becomes more of a thinking tool, instead of the orchestrator.

    by nsingh2
  • Really enjoying seeing these really capable SMLs. Note that on HF they state: "This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents." - https://huggingface.co/WeiboAI/VibeThinker-3B So we can't just hook it up to a coding harness like pi.dev or something.
    by mvitorino
  • Beats Opus 4.5 on reasoning you say?

    Prompt: If A goes to B who then goes to C, can A send something to C?

    Response:

    We need to interpret best. The phrase "If A goes to B who then goes to C, can A send something to C?" could be a puzzle about the concept of sending something (like passing a ball) and the relationships.

    Scenario: A gives something to B, and B passes it on to C. Question: Can A also give the same thing to C? Answer: Only if A can obtain a second copy (e.g., the thing was duplicated). Otherwise, after handing it to B, A no longer holds it and cannot “send” it unless a copy exists.

    [Lots of other unnecessary commentary and "scenarios" that make even lesser sense]

    by achrono
  • If A goes to B who then goes to C does C know A?
    by postalrat
  • I am a human and I don't know how to interpret this prompt.
    by erdevs
  • > Multi-level Quality Control.

    > [...]

    > LLM-based Query Quality Filtering. We utilize capable LLMs to assess query quality, filtering out samples with incomplete descriptions, unreasonable conditions, invalid logic, or an inability to effectively assess target knowledge points.

    by nolist_policy
  • Ran the same query and there is a ton of stuff, but it looks like it's reasoning through the ambiguity of the sentence. It still gets the right answer. Moreover, if we consider the FLOPs expended to get to the answer, and compare that to opus, I think it's still a net win.

    My hunch is that Opus scale models probably have shortcuts encoded into the model that handle these ambiguities cases, wheres this model has learned a program to reason through the edge case (crystalized vs fluid intelligence). Remembering that probablity (frontier) vs calculating it on the fly (vibethink)

    by rapatel0
  • I tried generating the classic pelican svg, but it failed horribly just showing me a rectangle and a black circle...
    by aero2146
  • Its for reasoning not generating art?
    by physPop
  • That’s all I needed to hear
    by realitysballs
  • try it again but give a careful explanation of what a bicycle and a pelican is and how the pelican would sit atop the bicycle. Then give it a reference to the SVG tags you want it to use with documentation.

    Here's what I got

    https://9ol.es/tmp/pelican.png

    with https://9ol.es/tmp/prompt_pelican.txt

    using prithivMLmods/VibeThinker-3B-GGUF:Q4_K_M

    by kristopolous
  • I think this is predicted? Part of the story is how they were able to preserve core reasoning ability while cutting knowledge like "pelicans have wings."

    > these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios.

    by fwipsy
  • Having some success while testing this model out as a replacement for GPT-5 nano in source code security review. Running on RTX 3090 (24 GB VRAM) via vLLM. It's not great on structured output (as noted in the model card) but I'm working around that in my harness.
    by noperator
  • > but I'm working around that in my harness.

    How?

    by hypfer
  • Can't you just force it to do structured output via constrained generation?
    by dummydummy1234
  • The interesting thing about models this small is they should be able to be put on a single Taalas chip (the HC1 already runs a Llama 3.1 8B model). We're already at the point where half-decent reasoning could be run on an ASIC (and at mind-boggling speeds).
    by NotSuspicious
  • Yeah, if they can fit an 8B model that's really good at improving the output by thinking, running at 16K tok/s on Taalas would be mind-blowing.
    by pants2
  • Note that these are Python-only results, the model will not do as well with other languages.

    I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.

    by gslepak
  • Lots of confusion about what this model is actually focused on.

    It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar.

    "Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check.

    So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.

    by nsingh2
  • If it writes functional Python instead of cosplaying as a Java programmer and cramming code with classes and accessors, it's already better than Opus...
    by rcarmo
  • Looks like we are seeing small but mighty model breakthroughs, outpacing the pure capital firepower of SOTA providers. I love rooting for the little guy, but is it too soon to call it? To play devils advocate, could it just be the benchmarks are not efficient enough to capture success of real developer workflows?
    by rbbydotdev
  • It feels sometimes like optimizations are only starting.
    by j45
  • I've just started using qwen3.6:35b a couple days ago running on my framework desktop and rather impressed. It runs really well and reminds me of probably the first Claude model I used. It's the first local model that's actually working for me in a coding agent I've tried. Very exciting!
    by bakies
  • I think people are going to continue to be surprised by the capability of small models.

    Now, if you ask this model to have a conversation with you, it's gonna fail and be incoherent. But boy, does it sure reason through math problems well.

    by Catloafdev
  • There is some base level of intelligence any model needs to be useful, even in narrow tasks.

    Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge...

    Small models need to have enough base knowledge to be able to be good enough -- even in a seemingly narrow regime. Where is that? Obviously they don't need all the obscure knowledge of a frontier model but there is some base level which is probably more than it would first seem.

    by deftio
  • This is more of a question of the definition of "drive a car" than any specific issue about intelligence. Drive a car without errors? Impossible, and now we're into a subjective discussion about what feels intelligent. Pass the DMV test? Probably. How complicated are the conditions? There are plenty of drivers with bad judgement. It's a quicksand sort of discussion.
    by wilg