5 ms·

Gemma4 in my view is good enough to do things similar to Gemini 2.5 flash, meaning if I point it code and ask for help and there is a problem with the code it’l

• slopinthebag 7mo ago
  Yep, and to be honest we don't really need local models for intensive tasks. At least yet. You can use openrouter (and others) to consume a wide variety of open models which are capable of using tools in an agentic workflow, close to the SOTA models, which are essentially commodities - many providers, each serving the same model and competing with each-other on uptime, throughput, and price. At some point we will be able to run them on commodity hardware, but for now the fact that we can have competition between providers is enough to ensure that rug pulls aren't possible. Plus having Gemma on my device for general chat ensures I will always have a privacy respecting offline oracle which fulfils all of the non-programming tasks I could ever want. We are already at the point where the moat for these hyper scalers has basically dissolved for the general public's use case. If I was OpenAI or Anthropic I would be shitting my pants right now and trying every unethical dark pattern in the book to lock in my customers. And they are trying hard. It won't work. And I won't shed a single tear for them.
• colechristensen 7mo ago
  Local models seem somewhere between 9 and 24 months behind. I'm not saying I won't be impressed with what online models will be able to do in two years, but I'm pretty satisfied with the prediction that I won't really need them in a couple of years.
  • Gigachad 7mo ago
    We still aren't going to be putting 200gb ram on a phone in a couple years to run those local models.
    • mh- 7mo ago
      A lot of people are making the mistake of noticing that local models have been 12-24 months behind SotA ones for a good portion of the last couple years, and then drawing a dotted line assuming that continues to hold. It simply.. doesn't. The SotA models are enormous now, and there's no free lunch on compression/quantization here. Opus 4.6 capabilities are not coming to your (even 64-128gb) laptop or phone in the popular architecture that current LLMs use. Now, that doesn't mean that a much narrower-scoped model with very impressive results can't be delivered. But that narrower model won't have the same breadth of knowledge, and TBD if it's possible to get the quality/outcomes seen with these models without that broad "world" knowledge. It also doesn't preclude a new architecture or other breakthrough. I'm simply stating it doesn't happen with the current way of building these. edit: forgot to mention the notion of ASIC-style models on a chip. I haven't been following this closely, but last I saw the power requirements are too steep for a mobile device.
      • am17an 7mo ago
        Don’t underestimate the march of technology. Just look at your phone, it has more FLOPS than there were in the entire world 40 years ago.
        • kuboble 7mo ago
          And I think it's very likely that with improved methods you could get opus 4.6 level performance on a wrist watch in few years. You needed supercomputer to win in chess until you didn't. Currently local models performance in natural language is much better than any algorithm running on a super computer cluster just few years ago.
        • root_axis 7mo ago
          Yeah, but that's the current state of the art after decades of aggressive optimizations, there's no foreseeable future where we'll ever be able to cram several orders of magnitude more ram into a phone.
• swazzy 7mo ago
  similar vibes as "640k ought to be enough for anybody"
  • shermantanktop 7mo ago
    Well you can do a lot with 640k…if you try. We have 16G in base machines and very few people know how to try anymore. The world has moved on, that code-golf time is now spent on ad algorithms or whatever. Escaping the constraint delivered a different future than anticipated.
    • throwaw12 7mo ago
      > you can do a lot with 640k…if you try. it is economically not viable to try anymore. "XYZ Corp" won't allow their developers to write their desktop app in Rust because they want to consume only 16MB RAM, then another implementation for mobile with Swift and/or Kotlin, when they can release good enough solution with React + Electron consuming 4GB RAM and reuse components with React Native.
      • gerdesj 7mo ago
        My Commodore 64 begs to differ.
      • KerrAvon 7mo ago
        Strangely enough, AI could turn this on its head. You can have your cake and eat it too, because you can tell Claude/Codex/whatever to build you a full-featured Swift version for iOS and Kotlin for Android and whatever you want on Windows and Mac. There's still QA for the different builds, but you already have to QA each platform separately anyway if you really care that they all work, so in theory that doesn't change. Of course, it's never that simple in reality; you need developers who know each platform for that to work, because you must run the builds and tell the AI what it's doing wrong and iterate. Currently, you can probably get away with churning out Electron slop and waiting for users to complain about problems instead of QAing every platform. Sad!
    • jstummbillig 7mo ago
• blitzar 7mo ago
  > it’s not great at using all tools Glad it wasnt just me - i was impressed with the quality of Gemma4 - it just couldnt write the changes to file 9/10 times when using it with opencode
  • seaal 7mo ago
    https://huggingface.co/google/gemma-4-31B-it/commit/e51e7dcdb6febd74c182fe0cb41c236363ae2ac5 https://huggingface.co/google/gemma-4-31B-it/commit/e51e7dcd... There was an update to tool calling 3 days ago. I haven't tested it myself but hope it helps.
    • sroussey 7mo ago
      Hmm.. is there an updated onnx?
    • blitzar 7mo ago
      Wow, that is so much better! I didnt exactly test it extensively but my issues are gone.
  • erichocean 7mo ago
    > it just couldnt write the changes to file 9/10 times when using it with opencode You might want to give this a try, it dramatically improves Edit tool accuracy without changing the model: https://blog.can.ac/2026/02/12/the-harness-problem/ https://blog.can.ac/2026/02/12/the-harness-problem/
• stbenjam 7mo ago
  There is a cognitive ceiling for what you can do with smaller models. Animals with simpler neural pathways often outperform whatever think they are capable of but there's no substitute for scale. I don't think you'll ever get a 4B or 8B model equivalent to Opus 4.6. Maybe just for coding tasks but certainly not Opus' breadth.
  • charcircuit 7mo ago
    I think you are underestimating the strength a small model can get from tool use. There may be no substitute for scale, but that scale can live outside of the model and be queried using tools. In the worst case a smaller model could use a tool that involves a bigger model to do something.
    • sroussey 7mo ago
      Small models are bad at tool use. I have liquidai doing it in the browser but it’s super fragile.
      • dd8601fn 7mo ago
        I don’t really understand this, but I hear it a lot so I know it’s just confusion on my part. I’m running little models on a laptop. I have a custom tool service made available to a simple little agent that uses the small models (I’ve used a few). It’s able to search for necessary tool functions and execute them, just fine. My biggest problem has been the llm choosing not to use tools at all, favoring its ability to guess with training data. And once in a while those guesses are junk. Is that the problem people refer to when they say that small models have problems with tool use? Or is it something bigger that I wouldn’t have run into yet?
  • zarzavat 7mo ago
    The only thing that we are sure can't be highly compressed is knowledge, because you can only fit so much information in given entropy budget without losing fidelity. The minimal size limits of reasoning abilities are not clear at all. It could be that you don't need all that many parameters. In which case the door is open for small focused models to converge to parity with larger models in reasoning ability. If that happens we may end up with people using small local models most of the time, and only calling out to large models when they actually need the extra knowledge.
• gorgmah 7mo ago
  When that happens, you'll have fomo from not using opus 5.x. The numbers that they showed for Mythos show that the frontier is still steadily moving (and maybe even at a faster pace than before)
  • random2021 7mo ago
    I would be surprised about that behavior even for 10% people doing real AI usable work. Very few people buy new motherboard or CPU or gfxcard every 3 months? Even now just because the latest Anthropic is super great doesn't mean people are not using other models. Not everyone is subscribed to only the best.
• vasco 7mo ago
  But that difference atm is the difference between it being OK on its own with a team of subagents given good enough feedback / review mechanisms or having to babysit it prompt by prompt. By the time gemma6 allows you to do the above the proprietary models supposedly will already be on the next step change. It just depends if you need to ride the bleeding edge but specially because it's "intelligence", there's an obvious advantage in using the best version and it's easy to hype it up and generate fomo.
  • oblio 7mo ago
    > But that difference atm is the difference between it being OK on its own with a team of subagents given good enough feedback Do people actually build meaningful things like that? It's basically impossible to leave any AI agent unsupervised, even with an amazing harness (which is incredibly hard to build). The code slowly rots and drifts over time if not fully reviewed and refactored constantly. Even if teams of agents working almost fully autonomously were reliable from a functional perspective (they would build a functional product), the end product would have ever increasing chaos structurally over time. I'd be happy to be proven wrong.
• logicallee 7mo ago
  > if I point it code and ask for help and there is a problem with the code it’ll answer correctly in terms of suggestions could I ask how you do that? I installed openclaw and set it to use Gemma 4 but it didn't act in an agent mode at all, it only responded in the chat window while doing nothing, and didn't read any files or do anything that you wrote (though I see you do mention that it's not great at using all tools). What are you using exactly?
  • data-ottawa 7mo ago
    I had the same issues. I had to tell it to use sub agents explicitly, and instead of saying set a cron say set an openclaw cron. I generally do like the model, it’s not a great agent though. It’s good for summarization tasks, small tool use, and has pretty good world knowledge, though it does hallucinate.
• mark_l_watson 7mo ago
  I agree. At first I was really turned off by the Gemma 4 line of models because they didn’t function with coding agents as well as the qwen3.5 line of models. However, I found that for other use cases Gemma 4 was very good. EDIT: I just saw this: “”Ollama 0.20.6 is here with improved Gemma 4 tool calling!”” I will rerun my tests after breakfast.
• docstryder 7mo ago
  The economy is, more or less, a competition. If someone gets a really great axe and are happy with it, that’s great for them. But then, other people will be on bulldozers. They can say they are happy with the axe, but then they are not in the competition at that point.
  • ectospheno 7mo ago
    I think the article was wondering how many billion dollar bulldozers the world needs. My local hardware store sells a variety of axes. I myself am a happy ax user. I even replace them.
• phantomoc 7mo ago
  [dead]
• shaz0x 7mo ago
  Even Gemma 4 E2B is more useful than you'd think if you give it the right harness. I've been running it on Android via llama.rn and it handles function calling natively — the model outputs structured tool calls without any prompt engineering. Won't replace Opus for hard reasoning but for a mobile app that needs to pick a tool and run it, the cost math is hard to argue with. $0/query forever.
• pojzon 7mo ago
  Like you say, Google is not playing the game to win. They play it to make others lose. Looking at current advancements - this is the horse I would bet my money on.
• rldjbpin 7mo ago
  > If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally on a Mac, I won’t really bother with the cloud models. > That’s a problem. While improvements should continue rolling in, and might even match current SOTA in benchmarks down the line, is it "good enough"? Hard to believe we have reached that stage with current models, which would continue to stretch beyond what we can economically run. Call it skill issue, or try to fix it with a revolutionary harness, it seemingly takes a village to get it all working. Maybe by then we will have good enough ecosystem in these layers too, but if current capabilities is the benchmark, it might need more time in the oven.