12 ms·

There is minimal downside to switching to open models

• peter_retief 4mo ago
  What open models are "recommended"? I like the Linux analogy, I struggled with Linux way back.
• DANmode 4mo ago
  But, what model are you using? and what hardware are you using?
  • 0gs 4mo ago
    yeah, on a 96GB Mac Studio and Gemma+Qwen, it's definitely fully doable. fully doable but not really for coding on 16GB. but svelter models and cheaper (eventually) hardware are coming!
    • nezuzen 4mo ago
      "cheaper (eventually) hardware" Best case 2-3 years from now. Otherwise it will take a major global recession to get us anywhere near last year's prices.
    • Gigachad 4mo ago
      I suspect hosted and local will converge when hardware prices come down and API prices go up. The massive rate of datacenter build out will be unsustainable. Right now the hosted models are massively cheaper than buying the hardware and running it yourself which signals that hosted is very subsidized.
    • fluidcruft 4mo ago
      If you don't have that hardware thr math of buying a depreciating computer is challenging if you are satisfied with the $100/month plans ($1200/year). A 96GB Mac Studio is ~$4k. I think if you have the hardware already as a sunk cost then yes it makes sense. But I'm not sure it is worth spending $4k for today's hardware vs waiting for newer hardware in a few years.
    • marcus_holmes 4mo ago
      Macs are expensive hardware, but I'm always seeing people running LLMs on them. Is anyone running on cheaper generic hardware and Linux?
      • brucehoult 4mo ago
        A Mac is cheaper than a high end GPU with the same amount of RAM.
        • marcus_holmes 4mo ago
          ah, right, so it's about Apple Silicon being fast enough to use instead of a GPU?
          • brucehoult 4mo ago
            They use the GPU but an Apple Silicon GPU has the same high speed access to all the RAM on the machine as the CPU does, rather than having its own walled-off maybe 16 GB VRAM in mainstream gaming GPUs or 24 GB in RTX 4090 or RTX 5090 (MSRP $1999 but in practice $3000-$4000 at the moment). Nvidia A100 (80GB VRAM) apparently cost $15,000 or so. Not only does Apple's unified memory give the GPU more RAM to use, but it also eliminates copying things between CPU RAM and GPU RAM. A Mac Mini with 48 GB RAM costs $1799. A Mac Studio with 96 GB RAM is $3999 — until March you could get a Mac Studio with 512 GB RAM for $3999, all of which could be used for your AI model. https://www.tomshardware.com/tech-industry/apple-pulls-512-mac-studio-upgrade-option https://www.tomshardware.com/tech-industry/apple-pulls-512-m... Some are coming up used at silly prices. https://www.trademe.co.nz/a/marketplace/computers/desktops/apple-desktops/listing/5989987996 https://www.trademe.co.nz/a/marketplace/computers/desktops/a... NB NZ$44,999 is "only" US$25,772.
            • 0gs 4mo ago
              i believe you meant something like US$9999 for the 512GB. otherwise, i'm going to feel like QUITE the fool for choosing the 96GB variant at the same price
• PcChip 4mo ago
  Is it just me or is half the article missing? I enjoyed the first part though
• julianlam 4mo ago
  I think it's interesting that people write off open weight models because they're "a few months behind" proprietary models. I know LLMs move at the speed of light (especially these past few quarters), but if Opus and GPT "a few months ago" were really like open weight models, then there's really no reason to not switch, especially for those who were using these models a few months ago. Your codebase didn't change, so use the open weight model. Don't move the goalposts.
  • taormina 4mo ago
    For that matter, the new models are shit. If I’m using Opus 4.6 anyway to get anything actually done, then great, we’re actually entirely caught up then.
  • TacticalCoder 4mo ago
    > I think it's interesting that people write off open weight models because they're "a few months behind" proprietary models. The really interesting thing is that it's typically those very same accounts who were explaining, a few months ago, that thanks to their commercial model they were gaining so much time and producing so much fantastic code. A few months passes and suddenly the open-source model have caught up with the models that were gaining them so much time and that produced amazing code (in production everywhere for sure btw) but... It's impossible to work with these models. Rinse and repeat. The current models, according to them, are basically AGI and they can go fishing while paid subscriptions solve the world's problems. But when it six months there shall be new closed, pricey, models and when the open ones shall have reach the level of Fable, we'll hear how it's impossible to work in late 2026 on a model that is "only at the level of Fable". These people should have been snake-oil salesmen (and it could be what they actually are).
    • nemomarx 4mo ago
      My most charitable interpretation that there's some honeymoon effect for each release, and people genuinely feel very productive and useful for 2-3 months. By the time the next big model release happens they've seen some issues or run into something that makes them feel like the new model will fix all that and improve their flow so much, etc. Not unusual in the tech space, but this has been basically constantly happening for two years now? I can't imagine the improvements are more than incremental at this point.
• mdale 4mo ago
  I think the frontier will command premium for sometime just as slight better software developers were 10x's vs their peers as their architecture & development strategies and code approach compounded quickly. One less error per block of work compounds quickly. Sure, there may be some cases and reasons for local models and industry is so large they will continue to make progress and gather economic value and users for specific use case; but frontier will command vast majority of the economic value distinct from Linux and open source where the model created better than proriatary economic incentives around development
  • 4fffs 4mo ago
    Youre clutching at straws. Ultimately its a financial game. Open source is far cheaper so it already has an upper-hand. Frontier models have to justify financially why they are worth the additional spend.
  • byzantinegene 4mo ago
    10x developers were not slightly better than their peers, they were vastly superior and faster. OTOH, the lead of frontier llms is diminishing as training is getting diminishing returns. Also, on that note. Not every company needs 10x developers, just as not every task needs frontier llms. Ultimately, operating costs will be the largest contributing factor.
• radhitya 4mo ago
  Have you read about Opencode Go? They are great provider for open model, like GLM 5.2, Deepseek v4 Pro, Kimi 2.7 Code. You should give it shot to them :-)
  • 2muchtime 4mo ago
    The amount the HN community, at least from what I’ve seen, is sleeping on OpenCode Go (and zen) is kind of amazing. $10 a month gets you generous usage with the best open weight models and they claim to have zero retention and not to train on your usage. It’s unclear to me what the advantages of openrouter are but it seems to be a default I see many people talking about here.
    • johndough 4mo ago
      > It’s unclear to me what the advantages of openrouter are but it seems to be a default I see many people talking about here. The advantage of OpenRouter compared to using API providers directly is that you can switch between API providers without binding your money to a single provider. The advantage of OpenRouter compared to OpenCode Go is that the price for DeepSeek-V4-Pro and MiMo-V2.5-Pro is better on OpenRouter. For example, DeepSeek costs $0.435/0.87/0.003625 for 1M in/out/cached tokens (https://openrouter.ai/deepseek/deepseek-v4-pro https://openrouter.ai/deepseek/deepseek-v4-pro), compared to an equivalent of $1.74/3.48/0.0145 under the OpenCode Go plan (https://opencode.ai/docs/go/#usage-limits https://opencode.ai/docs/go/#usage-limits), almost exactly 4x. But since you get a monthly usage limit of $60 with the OpenCode Go plan for $10 (i.e. 6x), you might still come out ahead if you use it a lot (or use other models, where the pricing difference is smaller or non-existent).
      • 2muchtime 3mo ago
        So the cost makes sense I was unaware but “The advantage of OpenRouter compared to using API providers directly is that you can switch between API providers without binding your money to a single provider.” Opencode Go gives you a choice between “the best” open weight models and you’re not tied down to just GLM or MiniMax and Zen gives you an even longer list of providers including Claude and GPT? Is it that Openrouter gives you access to like… every model and provider?
• aussieguy1234 4mo ago
  >There was a time not too long ago when using Linux entailed some professional risk1. First there was compatibility: you may not have been able to render a Word document or PowerPoint correctly, and you might have had to trust Open Office’s export capability to render docs the way you wanted For a while during this era, I used to port my laptops windows installation into a virtual machine that can run on Linux. It took a bit of hacking away but I could usually do it in a day or two. Then its all Linux with the windows vm being used for the microsoft stuff.
• codelong888 4mo ago
  [flagged]
• linzhangrun 4mo ago
  Open source models are still not good enough for now, but with the current speed of one new SOTA every two months, by this time next year we will definitely have cheap open source models at least as good as Fable :)
  • sho 4mo ago
    I don't think we will. The open model labs are too resource constrained to approach Fable or even Opus on the general case and I don't see that changing within a year. Right now, due to profound shortfalls in both data and hardware compared to the US labs, the OSS models are IMO basically technology demonstrators that in practise are even more jagged than the US labs' efforts. The high points of the jaggedness are close - but number of happy paths is many times fewer, and their behaviour inside the harness is far less refined. Barring some incredible breakthrough I don't think that is changing without a much higher level of resources - which seems impossible given the current hardware environment. I have no reason to think that Anthropic or OpenAI are in possession of some secret sauce that the Chinese labs can't duplicate given the right resources, but the fact remains that absent those resources they'll remain behind. Barring some incredible bombshell reveal from Huawei I don't think this asymmetry resolves in a year. In three years it may well be a different story.
    • linzhangrun 4mo ago
      deepseek-v4-pro, probably the representative cheap opensouce LLM, was released in 2026.4 One year before, what OAI had in hand was gpt-4.1 and gpt-o3. I think it is not very controversial to say that deepseek is stronger than them, at most you can point to some post-training problems, basically the instability you mentioned. Also I am not sure if it is because the people who are best at using AI -- the people making AI -- get more development speed as the models get smarter, but my feeling is model progress is getting faster and faster. GPT-3.5 and GPT-4 were almost one year apart. The disadvantage from hardware limits and compute shortage is visible from the size of chinese models. glm-5.2, which is claimed to be around opus-4.6 level in coding, is only 744B. But Chinese engineers are obviously, how to put it, getting very effective results on "performance at the same size". And that is not even talking about the advantages from China's electricity, manpower, or even "national will" to compete against America. So saying it may take three years to catch up with a gap that is now only several months looks too pessimistic. ChatGPT itself was released only three and a half years ago, and today is already a completely different world.
• c_chenfeng 4mo ago
  [dead]
• causality0 4mo ago
  I know open models have gotten quite good in many tasks such as coding or composition, but are there any that can access the internet and retrieve data like ChatGPT, Claude, etc can? I do have to admit I have recently begun wishing I could pay five dollars a month for a "just answer the fucking question" plan that would give me results without the guardrails and without the constant simpering and ego-stroking. I keep finding myself going a quick evaluation of "is it faster for me to skim search results myself or to construct an elaborate narrative to make an AI give me a real answer".
  • linzhangrun 4mo ago
    You can let the AI solve it itself, and then it will provide two solutions: implement a local search service (easily blocked), or purchase a Web Search API service
  • JSR_FDED 4mo ago
    Just go to kimi.com and try for yourself (not affiliated, but happy user). First time I did this I realized in 5 seconds that the big players weren’t going to be carving up the market between them.
  • wilj 4mo ago
    > I know open models have gotten quite good in many tasks such as coding or composition, but are there any that can access the internet and retrieve data like ChatGPT, Claude, etc can? The things you describe are just tool calling, they're a feature of whatever harness you use. Use OpenCode, pi.dev, or maki.sh with any of the open models. > I do have to admit I have recently begun wishing I could pay five dollars a month for a "just answer the fucking question" plan that would give me results without the guardrails and without the constant simpering and ego-stroking. I keep finding myself going a quick evaluation of "is it faster for me to skim search results myself or to construct an elaborate narrative to make an AI give me a real answer". You can do most of this with some system prompts added to whatever agent you're using. You can do it from the settings on the claude/chatgpt websites too. (minus the no-guardrails thing)
    • 4mo ago
• cws_ai_buddy 4mo ago
  [flagged]
• pkulak 4mo ago
  Sure. But OpenAI is the same price. Why would I pay $18/month for z.ai when OpenAI is $20/month?
  • CJefferson 4mo ago
    One big advantage I’ve found — people get attached to models (including me). With open models if you find one that works perfectly for you but the next version doesn’t, you can run the old one forever (or someone will for you)
    • taytus 4mo ago
      This is a good point I never thought of. I appreciate it.
    • itake 4mo ago
      But… the models will fall behind. As libraries and languages and tool calling updates or the world knowledge changes, the models decay. Personally, I don’t like the change, but it’s just how technology works so I’d rather move with the flow than try to stick my foot down and freeze time.
      • OtomotO 4mo ago
        No problem, "AI" will just write its own frameworks and libs then!
      • hypfer 4mo ago
        > But… the models will fall behind. Yes but why does that matter? If I am happy with its capabilities now, I will continue being happy with its capabilities in the future. Yes, it cannot do the newest magic shit, but why does that matter? It can still do everything that existed up until that point, which is _a lot_. Eventually, you might also need something new, but it's not like the world shifts over all problems that exist from <old> to <new> and any tech for <old> problems suddenly becomes obsolete?
        • 4mo ago
• cpill 4mo ago
  I think once the hardware process comes down and these mini DGXs become cheaper, and by then open models still be smaller and better, there is going to be less and less reason to use the providers. CEOs are already complaining that they are costing too much. There are also large organisations like Banks which can't use external services and are already looking at internal housing. it's a good thing so the big AI companies just went IPO as once the self hosting trend kicks in they are going bust.
• Aurornis 4mo ago
  The headline says one thing, then the article text says this: > I’m hoping it’s going to be minimal. I have multiple subscriptions and I pay per token to try out different LLM providers through OpenRouter. I also run open weight models locally. I just can’t agree yet. The models from Anthropic and OpenAI really are that much better than anything else. The open weight models must be universally benchmaxxed across the board because my real world experience with them is very different than what the benchmarks imply. I get downvoted a lot for speaking about my experience because I don’t think it’s the reality that people want to hear right now, but it’s true for complex work. I do think there are a lot of easier tasks that can be handled appropriately by the open weight models in the hands of a skilled operator. If an entire job is simple enough that you wouldn’t hesitate to hand it off to a junior with a little supervision then any model will do. However for a lot of the work I do, even Opus 4.8 on Max requires a lot of attention and extra steering and review to keep it on track. Fable did, too, though to a lesser degree. When I try to use the big open weight models (hosted, because they’re not running at reasonable speeds locally at a quantization I can tolerate) it feels like I spend more time waiting while they burn tokens for output that I probably have to reject anyway, at least for the bigger tasks. I wish they were there, but that’s not the case yet.
  • iot_devs 4mo ago
    Do you have any example?
    • deleted 4mo ago
      [deleted]
  • justusthane 4mo ago
    The article also contradicts itself halfway through: > There remains a clear penalty for being an open LLM user. The conversation here _around_ the article is interesting, but the article itself boils down to “I’m going to try using open models and hope for the best.”
• bnj 4mo ago
  I’ve been wanting to get better acquainted with local inference but I don’t have the hardware, which has made me think about something I haven’t seen discussed, which is local collaboratives. The economics makes it seem like a group of people joining together to run good hardware and an open model might make sense, but I haven’t seen anything like this mentioned. Have I been missing it? I think it would be pretty neat to launch a service helping people who wanted to participate in something like that locate one another.
  • blackoil 4mo ago
    Open models hosted in Cloud???
    • pbgcp2026 4mo ago
      AWS Bedrock hosts Gemma 4 31B and this is The Best Deal – hands down. Try it. Vertex also has Gemma 4 MoE version. Not "lobotomised" by quants. There are also GLM (latest) and Qwen / DS (but these two are not latest versions)
  • markerz 4mo ago
    There are plenty of providers of open models that offer very affordable rates. Generally, I recommend looking at OpenRouter since they track various metrics for the various providers.
  • Aurornis 4mo ago
    The reason you don't see more of this is because everyone does the math, realizes it's not a good deal, and then gives up on the idea. There's a post at the top of /r/localllama about this exact math right now: https://www.reddit.com/r/LocalLLaMA/comments/1ubrcwj/tokenomics/ https://www.reddit.com/r/LocalLLaMA/comments/1ubrcwj/tokenom... TL;DR: Running GLM 5.2 is going to cost about $20K minimum, and that's going to be painfully slow compared to the cloud hosted versions. Even the estimates where the server is computing tokens 24/7 you can't break even for several years. The only reason to run locally is if complete data privacy is your top concern. You pay a high premium for that.
• blindriver 4mo ago
  As someone that has pretty powerful desktop that I've been using with local open weight models, people are far exaggerating the quality of them. Some of them are now useful. They don't compare yet to the online models of ChatGPT, Claude, Gemini, etc. They are still about 18 months behind. I have accomplished useful work with them, like image classification on Gemma4, but they are much much slower, much much more expensive and they don't scale at all. A $10,000 RTX 6000 Blackwell card will pay for 500 months of Claude or Codex, which is 40 years worth of compute. Obviously they are going to raise their prices, my prediction being to $200-500/month, but that still makes them at least years of compute and they scale very well with more traffic. Single GPUs do not, they are pegged at 100% and good luck getting it to answer multiple queries at the same time.
• root_axis 4mo ago
  Imagine taking 6 months longer to release your cookie cutter CRUD app.
• coffinbirth 4mo ago
  > Open models are served via various means, some by the companies that released them and some by third parties like OpenRouter. Unfortunately, both of these routes are dodgier in terms of privacy and data sharing, and I would not feel the same comfort sending API calls containing client or confidential data to them. That's why I'm using eurouter.ai with the following routing rule for all my requests: { "model": "glm-5.2", "models": [ "deepseek-v4-pro", "deepseek-v4-flash" ], "provider": { "allow_fallbacks": true, "data_collection": "deny", "data_residency": "EU", "max_retention_days": 0, "eu_owned": true } } Sure, it's quite expensive, but at least on a legal side data privacy is ensured. I trust them more than e.g. Anthropic, OpenAI or OpenRouter. Personally, I find it morally unacceptable to use U.S. AI tools, because I do not want to support them financially and thus support the crimes they are involved in[1]. [1]: https://news.ycombinator.com/item?id=48512339 https://news.ycombinator.com/item?id=48512339
  • ttoinou 4mo ago
    You dont care about which exact provider it is using behind the hood ?
    • Phlogi 4mo ago
      No, as long as they follow the requirements, especially the data privacy agreements. What would you? Price?
      • fredoliveira 4mo ago
        Output quality immediately comes to mind, of course. Models are converging, but they converge in bands, and frontier is frontier. I would not like to have any workflows in any area of my business where output is generated by an assortment of models from different providers. For trivial, mundane tasks that might be fine, but it certainly doesn't apply across the board.
        • MarceColl 4mo ago
• whatever1 4mo ago
  Claude started becoming useful for my coding purposes after it hit version 4.6. After that sure some nice to have additions but I think if I had 4.6 sonnet & opus as open weights, I would not need something more. Having played a bit with Fable, reinforced the above.
  • ch4s3 4mo ago
    I agree and I'd love for local models to hat the sonnet 4.6 level but nothing seems really all that close, and I'm not particularly excited about giving money to deepseek.
  • JeremyNT 4mo ago
    Yeah for me the coding inflection point was relatively recently (GPT 5.3 perhaps). There's just a threshold they have to hit to be consistent enough to avoid having to redo work and only the later models started delivering it. This certainly seems feasible for open weight models eventually, but I'm still extremely skeptical of the claims about reaching this level with any open weight model that can be run locally (nevermind the hardware costs to do so practically).
• OtomotO 4mo ago
  I am absolutely pro local and true open source models. Personally I haven't seen any productivity gain since Opus 4.5 times. But: I can't fully get behind the opinion that (so called) "open source models" are simply superior and will be in the future, because when I asked some models who they are, they answered with "I am Claude from Anthropic", which could mean they have been trained by exfiltrating Claude. I have NO moral objection to this, as Anthropic and "Open""AI".also trained their models on anything they could get their hands on. It's more about the question: can and will these models be updated, even if Anthropic et al fail. Who's gonna pay for training then? What's their incentive? Have we reached a plateau?
  • fuck_google 4mo ago
    [dead]
• arttaboi 4mo ago
  I guess this will happen soon. There are two catalysts needed for this to happen: 1. Evals that can quickly tell you how much downside there is to switching 2. Something like OpenRouter that can help you run those evals quickly Now #2 is starting to become popular, and I think we'll soon see more people adopting a model-agnostic approach. Of course, there will still be high-intelligence use cases where nothing comes close to Claude or GPT.
  • alexhans 4mo ago
    Exactly. I'm very happy the discourse has moved on from "but X model is the best" to "you can use open models". Whether you're using SDK or harness based agents, having evals means you're able to modify any part of your agent and still know what satisfies your "good enough". It's great for designing products that are easy to change as well.
• PeterStuer 4mo ago
  While I agree with some of the gist of the article, 2 remarks: 1. Unfortunatly in my tests the open models do not (yet?) rival, at least Claude Opus, for software development/engineering and adjacent tasks. 2. Enjoy while it lasts. I'll be genuinly amazed these open models will not be declared 'illegal' under some security pretense by the end of the year. And I say 'pretense' because the primary driver will be regulatory capture and industry protectionism.
  • mirekrusin 4mo ago
    Banning models in US just strengthens competing states, ie. China.
    • deleted 4mo ago
      [deleted]
• Animats 4mo ago
  OK, now what? Someone offers open models as a service? That's basically a time-sharing computing business - people at terminals sharing remote computing resources. If you buy your own H100 it will be idle while you're typing or reading or thinking. So sharing makes sense. But it doesn't have to be an "AI company". It's just a compute service. The companies that offer web hosting could get into this.
  • flexagoon 4mo ago
    > The companies that offer web hosting could get into this. They already do. DigitalOcean is one of the providers on OpenRouter, for example
  • HarHarVeryFunny 4mo ago
    There are lots of companies providing open models as a service. DeepInfra and Fireworks AI for example. Even Amazon for that matter.
• myzek 4mo ago
  Any tips on which model to use and how to use them? I have 64 RAM and 16 VRAM (I know it's not a lot, it's a gaming GPU) and I'm trying to find a good model to use but it's a bit of a struggle
• fabijanbajo 4mo ago
  [dead]
• ZeroGravitas 4mo ago
  It seems the best self-hosted and the worst models served by big providers has some considerable overlap in quality. Whatever reason people have to run those (cheaper? backwards compatibility once you get something running) surely applies to the open models too, maybe even more so.
• _pdp_ 4mo ago
  There are downsides depending on how good is your harness. Switching the model is easy enough. Ensuring that the harness continues working the way it did is a completely different thing. This is not just about the prompts but also general behaviour around the model and its infrastructure. So while it is not complicated and certainly something that can be solved, it is not plug and play. That being said, we switch to open weight models earlier this month and the results has been more than positive so far. The cost savings are also hard to dismiss.
• reacharavindh 4mo ago
  It was easy to be a rebel and use Linux when it was clearly competent, but needed hacks and extra elbow grease to get it polished for use. IME, the open models are “not there yet” in terms of capability or operational needs. Sure, GLM5.2 looks competent, but I will only be able to get it to run that competent if I had a huge cluster of GPUs.. if I am accessing an open model via hosted API, I might as well run a closed model via hosted API. The incentives fall apart in comparison to using Linux 15 years ago. Don’t get me wrong. I wish I could run a local model and be happy about it. At the moment, I’m not.
  • hypfer 4mo ago
    > if I am accessing an open model via hosted API, I might as well run a closed model via hosted API. uh.. no? The whole thing is that it cannot be enshittified, because there's not just a single party having control over it. As it has happened, is happening and will happen. With open weights, you cannot easily be rugpulled or locked out or any of that stuff. If the corp attempts that, someone else with an server farm will gladly take you as a customer with absolutely 0 changes to your workflow other than swapping out the API URL + Key. You'll be talking to the same model with the same personality and same knowledge.
• Atom_Foundry 4mo ago
  [flagged]
• DrScientist 4mo ago
  What's amazing about these models is they are effectively a distillation of the internet in something that can fit onto your local machine [1] and be queried via natural language. [1] It seems inevitable that decent local models will be possible as the technology and the hardware is improving at a rate beyond the growth of the knowledge base to be distilled.
• tumdum_ 4mo ago
  I find the attitude shown in this post very surprising. On the one hand, the post starts with a story of adopting Linux and other FOSS. The core of FOSS is giving its users the ability to understand and modify software they run. On the other hand, the rest of the post is about using a tool (LLM) that the author has no way to modify and no way to understand. Huge matrices of floats are at best comparable to compiled code. But the reality is even worse - it’s actually easier to decompile and understand proprietary software. Not to mention the fact the most of the time users can’t even run the “open” models since it requires hardware that most can’t afford. How did we get from prising software freedoms to this?
  • 542458 4mo ago
    I’d disagree wrt “modify”. There are all sorts of tools for modifying LLM weights (ie to remove refusals, remove layers or experts, merge models, finetune, and more) and a quick glance at huggingface or civit will show those in very active use. I don’t think the hardware requirements are relevant. If a research lab publishes the code their particle collider runs under the GPL, that doesn’t make it not OSS even though they’re the only ones on the planet with the hardware to run it.
    • tomjakubowski 3mo ago
      You can also edit binary distributions of models with means besides changing their weights. See "LLM Neuroanatomy: How I Topped the LLM Leaderboard Without Changing a Single Weight." On the spectrum of: careful engineering--hacking--mad science This kind of thing falls far towards the mad science end of the scale, but has proven effective. https://dnhkng.github.io/posts/rys/ https://dnhkng.github.io/posts/rys/
• petesergeant 4mo ago
  Headline: "The is minimal downside" Article: "I’m hoping it’s going to be minimal"
  • davidpapermill 3mo ago
    Should be top comment. I think there _are_ downsides to using open models. Quality won't be as good. That's a given, at least with SOTA. But there's more: switching between models means qualitative changes in replies, and in strengths and weaknesses on evals. You're also on the "open source track" and there's no guaranteed upgrade path - the creator may simply stop publishing new models. And the fact that they're mostly from China comes with political, provenance, and governance issues. I think NVIDIA's models are the most promising, because they can serve as a foundation for new model companies. That's a meta-feature that keeps the proprietary models in check. And NVIDIA's incentives are very likely to remain aligned with open-source: they want to encourage competing solutions that rely on their third party hardware.
• c-b 4mo ago
  What's confusing to me is that there is no discussion about the actual downside experienced it's just theoretical.
  • shever73 4mo ago
    There seemed to be no real discussion about anything! I was expecting more of a conclusion, but the article did not support the proposition in the headline.
• GL26 4mo ago
  What makes an open model worse is ultimately the budget : you have access to worse data, not SOTA models, less GPU compute time, and having a good fine tuning team is extremely expensive. Linux works because the entry barriers are purely on a software side : a lot of contributers all around the world can outclass any OS by contributing on their scale to Linux. All you need to contribute is a computer, and your brain. Open models don't have the same community push, they rely on core ressources that not anyone owns. And injecting them in the model costs too much money. If there are no public breakthroughs in the way we train large open models that makes community led models 10x better, the shift to open models will never happen on a large scale.
• impartshadow 4mo ago
  [flagged]
• anuramat 4mo ago
  there is zero downside to not switching though: just use claude while it's good and subsidized, switch if rugpulled
  • layer8 3mo ago
    The downside, apart from privacy concerns, is sending your money to parties you don’t want to support.
    • anuramat 3mo ago
      if you care about privacy, claude/gpt is strictly not an option and there's nothing to think about > sending your money akchyually if you do it right, you are sending negative money; fair enough otherwise
• spiralcoaster 3mo ago
  This can't be for real. The title asserts there is minimal downside to switching to open models, but the article provides zero evidence that this is true, and the author hasn't even attempted it yet. The end of the article states "I’m hoping it’s going to be minimal". I wonder if I can get a post to the front page with the title: "There are no real barriers to humans colonizing Mars next month". And at the end, "I'm hoping there are no real challenges."
  • TulliusCicero 3mo ago
    Yeah, I was thinking the exact same thing: > There remains a clear penalty for being an open LLM user. Every leaderboard consistently gets topped by proprietary models served over API. Today on June 21, 2026, Claude and GPT are at the top of the Artificial Analysis intelligence leaderboard. That’s from the performance side. The compatibility side is worse too. Claude code just works, and more generally, the big two provide nice APIs that make them easy to use, and, even if it’s a low bar, are “trustworthy” in the sense that we’ve largely all agreed we don’t mind sending them our LLM queries and trust them to handle them appropriately. > Open models are served via various means, some by the companies that released them and some by third parties like OpenRouter. Unfortunately, both of these routes are dodgier in terms of privacy and data sharing, and I would not feel the same comfort sending API calls containing client or confidential data to them3. > The other option or course is to run them yourself. This solves the privacy issue but is at least two of expensive, complicated, and comparatively slow. So...there's actually quite a bit of downside, then? Why the misleading title?
  • fracus 3mo ago
    I felt your exact experience. Felt like the title and article simply served to complain about Claude's impending ID rules.
  • anigbrowl 3mo ago
    The author explains why he thinks it's going to be minimal in the very next next sentence. It's a shallow piece of writing but since it's more of a personal diary blog that's not a big deal. This sort of nitpicking isn't helpful.
• epolanski 3mo ago
  I unsubscribed from Anthropic and our (EU-based) team is moving to an "ai-server" running opencode + GLM 5.2 and DS4. There are several benefits: - we cut AI spending by thousands - there is one AI server and starting different sessions for each user, one memory/skills/etc and everybody is involved into reviewing what went wrong and why. Harness finally makes sense and pays off more. - we can trust that the models are those that we run and not black boxes - no more money flowing to US narcissistic entrepeneurs and no more business being tied to US legislation Not gonna lie, GPT 5.5 Pro and Fable 5 were a tiny bit ahead, especially on longer vibecode-style tasks, but it's just not worth it.
  • ignoramous 3mo ago
    > moving to an "ai-server" running opencode What's the configuration of this server? How many do you need to deploy for your team size / estimated usage, if you're comfortable sharing that?
    • deleted 3mo ago
      [deleted]
• hungryhobbit 3mo ago
  What a stupid and pointless article. It's like OP decided "I might go for a walk today" and then wasted his own time writing an essay about how he might go for a walk ... and then wasted hundreds of people's time publishing it!
• snootypoot 3mo ago
  if you can afford to run one that is powerful enough for what you need it will be the superior choice. nobody should be feeding private data to the axis of evil (open ai, anthropic, palantir). i saw a job listing recently about an ai proficient data guy needed to work in a medical office. i thought to myself that there was very little chance they were cognizant of how medical records privacy laws conflict with non-local ai use. things are getting unhinged in the world.
• implexa_founder 3mo ago
  i'm tired of this narrative about switching to open source models. If people wanted to switch... well they would have switched! The amount of effort Anthropic and OpenAI are putting behind inference, harness, building great agentic applications on top of the frontier model... IS NOT A SMALL THING. Think about it, if you want to use an open source model to run an agent over long horizons, you need someone to offer it reliably. And if all compute goes to frontier models there is very little left for even startups to build agentic companies on top of these open source models.