I like that they acknowledge that. For working with ai as a tool open models are already great. For millennium problems and doomermaxxing they are not there yet.
Writing incoherently wastes your time, since you are generating an idiolect that nobody bothers to actually engage with.
Is this the kind of quality verbiage I’m missing out on, not using Discord?
but…why did this hit you so hard?
> For game development, we partnered with professional developers at Tencent Interactive Entertainment Group (IEG) to curate high-quality training data and build evaluations. We focused on feature implementation, gameplay logic, state tracking, and asset pipelines. We also extensively adapted Hy4 for major engines such as Unreal Engine, Unity, and Godot, and expanded its capabilities to multiplayer game development. Compared with Hy4 preview, Hy4 improves by 19.1% on GameDev and 9.8% on GameLogic.
Here's more about that game development studio - https://en.wikipedia.org/wiki/Tencent_Games - they've been around since 2004.
They are the Berkshire Hathaway of everything, to be honest. They invest in and acquire minority stakes in hundreds of companies.
https://en.wikipedia.org/wiki/Berkshire_Hathaway https://en.wikipedia.org/wiki/Tencent
I wonder if that has anything to do with the latest vibecoded slop releases and shilling from Tim Sweeney.
edit: apparently im wrong on that
Verrrry thorough in its work and therefore verrrrry slow. It spent a long time with each question, running about 26 queries for each question, and in some questions it ran 64 queries. One of the queries that it wrote also took about an hour to run, which affected its overall exam time.
Last year the open weight models were the cheap & good alternatives. Then in September/October that cost story got rewritten by the GPT-6 series and Claude's 5 series.
Hy4 brings open models back on the cost/accuracy frontier, but its behavior is too thorough + slow.
Another thing we're seeing is that post training open models is starting to see results. Particle-1 from last week is also on our frontier, which is post-trained on DeepSeek V4.1 and Fireworks Ember-1 from a few weeks ago was post-trained on Kimi K3.
Take these numbers with a grain of salt: this exam is just for short horizon, low-context data analysis tasks, not full-fledged software projects.
If the gap closes, we do zero austerity, and interest rates are high, I cannot see a way out of a major economic crisis, even if we do start to see dramatic productivity wins.
More on-topic (and maybe more positive?), but this model feels a bit more expensive than I would have expected!
The only thing worse for the economy than failure would be success.
OpenAI/Anthropic remind me of Silcon Graphics. Sure, lots of people bought those workstations before MP3 / MPEG video were a thing, and before Voodoo came out with the first cheap 3D accelerators. All the tech trends (software and hardware) back in the 1990's were against them, but their valuation stayed high for a bit longer than rational.
Unless there's some second step function change in AI, LLMs comparable to today's state of the art are going to be running on $1-2K machines in a few years, and they'll be doing it fast and with low wattage. There are already a dozen companies that know how to build that, including in the US, Europe and China.
Unlike SGI: Hopefully, given all the political corruption and fraud in this space, we'll see jail time for the guilty parties.
But AI labs are datacenter businesses now with the AI part being the driver. Pivoting to selling AI weights for on-premises hardware is a much bigger change. Given the money tied up in data centers, this will be very tough times.
This is a huge claim. What makes you think this will be the case?
Seems like he's just drawing that line into the future. Which seems sensible enough.
But yeah, I assume that when the economy gets back to normal-ish, in 2024-equivalent dollars, we'll be at 500B-1T for < $2000. That'll be 3-4 Moore's Law doublings since computer prices were sane. 125B * 2 ^ 4 = 2T.
Ant/OAI/Google are way ahead when it comes to the smartest, largest-scale and most compute-intensive models. By Jevons' paradox, if you make inference for leaner models cheaper (as the Chinese models do, being broadly comparable to GPT Terra or Claude Sonnet; barely approaching Opus/Sol at their very best) that only increases demand for the complementary service of very smart inference.
On the other side, I am still shocked at how good Qwen 3.8 Flash Next is on my GB10 Spark-like, even if it is only 45 tk/s decode!
You have to look at the effect in the aggregate, not just what any single actor is doing. This is the core of Jevons' paradox: it's based on surfacing additional demand for the same product (here, expensive model inference) that wasn't previously viable.
Jevons Paradox is not a rule its just a sometimes observed phenomenon
It says nothing one way or the other whether that would happen or not
If any important bank fails because of this, nationalize it and let their customers exit using money from the federal reserve. Then try and recover as much of that as possible using the banks investment portfolio. Costs a lot less than bailouts.
What money are you expecting to get int hat scenario? If I had to guess it wouldn't be a fraction of the cost.
> buy out stakes of pension and index funds
Index funds are part of the reason the top companies stock gets so bloated compared to those just outsid of their frame.
>If any important bank fails because of this, nationalize it and let their customers exit using money from the federal reserve. Then try and recover as much of that as possible using the banks investment portfolio. Costs a lot less than bailouts.
I agree. It worked here in Belgium tho they did spin off toxic elements of the bank in a not so great way prior to doing this.(some regular people still got shafted)
Modern financial system is less „finite amount of money you can move around in a logical way“ and more confidence based definitely totally not a ponzi scheme
I’ve said it for a while now both here and to friends - by Christmas next year, US AI labs will be trying to catch up with Chinese open models.
… and I think once Qwen’s latest open model beats every US labs, it’s over.
If there is an economic boost, it will come from broad productivity improvements and deflation of costs for services. If OpenAI or Anthropic can capitalize on that or not is a separate question.
From biggest to smallest, the top three:
AIG controlled $1.05T and market cap crashed from $180B to $3.8B
Wachovia controlled $782B and market cap crashed from $110B to $1.8B
Lehman Brothers controlled $639B and market cap crashed from $60B to $200M.
Massive companies accounting for lots of money in people's retirment accounts. The companies made mistakes leading to what would have resulted or did result in defaulting on their debts. Suddenly billions of dollars vanished from retirement accounts.
We could very easily see a similar cascade of corrections if we get a few more "Alphabet missed earnings" news calls.
... What on earth. Please go outside for awhile.
Model | Cached Input | Input (Miss) | Output
---------------|--------------|--------------|----------
Tencent Hy4 | $0.042 | $0.83 | $2.50
DeepSeek (peak)| $0.006 | $0.30 | $1.20
DeepSeek (off) | $0.003 | $0.15 | $0.60I really want to see some comparison on deepseek 4.1 flash 1017 providers, like, what's more trustworthy to not quantize without disclosing it, etc
also I can't find the official DeepSeek provider on openrouter https://openrouter.ai/deepseek/deepseek-v4.1-flash-1017
Their coding page shows DeepSeek Max as Pareto optimal: https://artificialanalysis.ai/agents/coding-agents Costing 1/2 of Terra Xhigh. It would also be great to see Sol and Opus not on max there.
Not a useful benchmarking site for coding unless you only want to run your models on Max.
OAI / Ant are in for a reckoning.
So, yeah. Back to GLM 5.3.
The character's name is 老大哥. That is if we are talking about 1984.
Also a very caring Model.
Personally, the thing I'm most excited about is the FrontierCode 2.0 score, which is a benchmark I trust a bit more - 31 seems to be roughly on par with 6.1 Sol and Opus 5.5 medium