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Shy of an algo breakthrough, open source isn't going to catch up with SOTA, their main trick for model improvement is distilling the SOTA models. That's why the
by Workaccount2 10mo ago
Shy of an algo breakthrough, open source isn't going to catch up with SOTA, their main trick for model improvement is distilling the SOTA models. That's why they they have perpetually been "right behind".
- impulser_ 10mo agoThey don't need to catch up. They just need to be good enough and fast as fuck. Vast majority of useful tasks of LLMs has nothing to do with how smart they are. GPT-5 models have been the most useless models out of any model released this year despite being SOTA, and it because it slow as fuck.
- nineteen999 10mo agoBullseye.
- dontwannahearit 10mo agoConfused. Is ‘fuck’ fast or slow? Or both at the same time? Is there a sort of quantum superposition of fuck?
- ThrowawayTestr 10mo agoIt's an intensifier
- 867-5309 10mo agowell, it's not slow as fuck! it's quick as lightning and speedy as hell
- deleted 10mo ago[deleted]
- aschobel 10mo agoFor coding I don’t use any of the previous gen models anymore. Ideally I would have both fast and SOTA; if I would have to pick one I’d go with SOTA. There a report by OpenRouter on what folks tend to pay for it; it generally is SOTA in the coding domain. Folks are still paying a premium for them today. There is a question if there is a bar where coding models are “good enough”; for myself I always want smarter / SOTA.
- wyre 10mo agoFWIW coding is one of the largest usages for LLM's where SOTA quality matters. I think the bar for when coding models are "good enough" will be a tradeoff between performance and price. I could be using Cerebras Code and saving $50 a month, but Opus 4.5 is fast enough and I value the piece-of-mind I have knowing it's quality is higher than Cerebras' open source models to spend the extra money. It might take a while for this gap to close, and what is considered "good enough" will be different for every developer, but certainly this gap cannot exist forever.
- KronisLV 10mo agoI just use a mix of Cerebras Code for lots of fast/simpler edits and refactoring and Codex or Claude Code for more complex debugging or planning and implementing new features, works pretty well. Then again, I move around so many tokens that doing everything with just one provider would need either their top of the line subscriptions or paying a lot per-token some months. And then there's the thing that a single model (even SOTA) can never solve all problems, sometimes I also need to pull out Gemini (3 is especially good) or others.
- nl 10mo agoGPT 5 Codex is great - the best coding model around except maybe for Opus. I'd like more speed but prefer more quality than more speed.
- Demiurge 10mo agoI get GPT 5.2 responses on copilot faster than for any other model, almost instantly. Are you sure they’re slow as fuck?
- Aurornis 10mo ago> They don't need to catch up. They just need to be good enough The current SOTA models are impressive but still far from what I’d consider good enough to not be a constant exercise in frustration. When the SOTA models still have a long way to go, the open weights models have an even further gap distance to catch up.
- echelon 10mo agoThis. You can distill a foundation model into open source. The Chinese will be doing this for us for a long time. We should be glad that the foundation model companies are stuck running on treadmills. Runaway success would be bad for everyone else in the market. Let them sweat.
- gejose 10mo ago> just need to be good enough and fast as fuck Hard disagree. There are very few scenarios where I'd pick speed (quantity) over intelligence (quality) for anything remotely to do with building systems.
- jameshush 10mo agoI agree with you for many use cases, but for the use case I'm focused on (Voice AI) speed is absolutely everything. Every millisecond counts for voice, and most voice use cases don't require anything close to "deep thinking. E.g., for inbound customer support use cases, we really just want the voice agent to be fast and follow the SOP.
- nkmnz 10mo agoIf you have a SOP, most of the decision logic can be encoded and strictly enforced. There is zero intelligence involved in this process, it’s just if/else. The key part is understanding the customer request and mapping it to the cases encoded in the SOP - and for that part, intelligence is absolutely required or your customers will not feel „supported“ at all, but be better off with a simple form.
- jacquesm 10mo agoAs a customer when confronted with such a system I hang up and never ever do business with that company again. Regardless of polish, they're useless.
- nkmnz 10mo agoWhat do you mean by "such a system"? One that uses AI to funnel your natural language request into their system of SOP? Or one that uses SOPs to handle cases in general? SOP are great, they drastically reduce errors, since the total error is the square root of the sum of squares of random error and bias – while bias still occurs, the random error can and should be reduced by SOPs, whenever possible. The problem is that SOPs can be really bad: "Wait, I will speak to my manager" -> probably bad SOP. "Wait, I will get my manager so that you can speak to them" -> might be a better SOP, depending on the circumstances.
- AmazingTurtle 10mo agoI'd prefer a 30 minute response from GPT-5 over a 10 minute Response from {Claude/Google} <whatever their SOTA model is> (yes, even gemini 3) Reason is: while these models look promising in benchmarks and seem very capable at an affordable price, I *strongly* felt that OpenAI models perform better most of the times. I had to cleanup Gemini mess or Claude mess after vibe coding too much. OpenAI models are just much more reliable with large scale tasks, organizing, chomping tasks one by one etc. That takes its time but the results are 100% worth it.
- _fizz_buzz_ 10mo ago> their main trick for model improvement is distilling the SOTA models Could you elaborate? How is this done and what does this mean?
- MobiusHorizons 10mo agoI am by no means an expert, but I think it is a process that allows training LLMs from other LLMs without needing as much compute or nearly as much data as training from scratch. I think this was the thing deepseek pioneered. Don’t quote me on any of that though.
- tickerticker 10mo agoYes. They bounced millions of queries off of ChatGPT to teach/form/train their DeepSeek model. This bot-like querying was the "distillation."
- SirMaster 10mo agoWhy would OpenAI allow someone to do that?
- qcnguy 10mo agoThey don't anymore. They introduced ID verification shortly after, but it's hard to stop completely while also scaling fast.
- MadnessASAP 10mo agoThey didn't, but how do you stop it? Presuming the scale that OpenAI is running at?
- orbital-decay 10mo agoThey definitely didn't. They demonstrated their stuff long before OAI and the models were nothing like each other.
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- stx5 10mo ago[dead]
- mistercheph 10mo agoToo bad, so sad for the Mister Krabs secret recipe-pilled labs. Shy of something fundamental changing, it will always be possible to make a distillation that is 98% as good as a frontier model for ~1% of the cost of training the SOTA model. Some technology just wants to be free :)
- blackoil 10mo agoWe trust in our lord and savior China and Zuck to keep the peasants fed.