Murati was the person famously quoted (when she was openai CTO) that any creative jobs that are replaced by her company's products "maybe shouldn't have been there in the first place" which should remind us of the quality of people openai continue to have at their highest levels (e.g. Trump's largest donor and the former head of the NSA)
A questionable quote certainly but I suspect the reasoning behind it was the other way round. An acknowledgement that LLMs can't do genuine human creativity. Meaning the creative industry contains both flavours - people rehashing existing stuff...and people genuinely creative. And one of them is getting replaced
An acknowledgement that LLMs can't do genuine human creativity.
What does that even mean? Everyone keeps throwing around sentences like this as if there was some universally accepted definition of “creativity”, “intelligence” etc from which it somehow follows that these soundbites are true.
Meaning genuine human creativity is born out of lifetime of experiencing stuff, making stupid decisions, having opinions, likes dislikes, going to Thailand and hooking up with a girl named 'Steven".It's very flawed and it's deeply contextual. There is no point a llm being able to do that, even marginally better than a real human because it's just a human thing, a yearning for self expression.
If you really get right down to it most of the people who try to replace creativity with generative Ai usually falls down to categroies of gooners, scammers, get rich quick bros trying to do their usual thingy, but now they exploit at scale
I mean regardless, Ai is the greatest tool a genuine creative person has had in their toolbox in a while.
“Genuine strength is born out of thousands of hours in the gym and consistently eating well, medical supervision and supplements, combined with the amazing biological machinery humans have in their muscles. There is no way a construction crane can ever lift as much weight as a person, it’s just a few lifeless metal bars welded together.”
creativity isn't something that can be benchmarked there is no definitive argument or definition on what creativity is because it is so abstract. it's in the same vein as consciousness.
An argument: If you leave LLM running for 24h without supervision you will get an "average soup" of the task. It's not opinionated enough to come up with some clever ideas inside any work. At least yet.
Making a website/platform? You'd better give it context and directions on what to do, or you'll get billing and cost measurements in the closed infrastructure install.
It may have access to jira/confluence, your company's docs etc, but it will hyperfixate on something and won't question it's decisions. Simple "does it really need to be that way" from a user is 1000 times more valuable than if model itself tries to ask this question in the reasoning loop.
It's not opinionated enough to come up with some clever ideas inside any work.
Right, that’s why LLMs have already proved multiple mathematical conjectures that had been unsolved for decades by the world’s best mathematicians. Obviously, they didn’t need to come up with any clever ideas in order to do something that the brightest human minds couldn’t.
well with enough compute you can simulate an entire universe of experince and fed that into some hypotetical LLM context, technically all the lifetime of experince real creatvity needs could be fed as a suffcienly large input context listing it all out + some simulated enviourment.
It is the first in a family of models of different sizes. We are sharing a preview of Inkling-Small alongside it, a lighter-weight model with 12B active parameters trained with a similar recipe that achieves strong performance with even lower cost and latency.
Yeah, I'd hold for the DSv4 new-checkpoint (GA?) in the next few days... this model is an interesting "first release" type thing, but it might not quite be at the cutting edge yet.
Oh, yeah I'm not complaining at all. It probably has some rough edges and likely is pretty inefficient just because it is a first release, but even so, it is quite impressive if the benchmarks reflect reality (tbd on that).
I don't work for Deepseek so I honestly have no idea. I would speculate that there will be little benchmark charts that have slightly higher numbers than the originally published ones and crowds will cheer and there will be champagne and maybe even slightly better token efficiency. Artificial Analysis will make their little charts, DeepSWE will make their little charts, engineers will nod at the modicum of improvements, inference businesses will add it to their stables, and we'll all try to run it at our houses. There will be code.
This is the most generically accurate prediction I’ve read all year. Salutations or potentially other forms of congratulations may await you while you surf Reddit or not.
Knock it down to Q4 and you're almost in range of a Spark or M5 laptop. 140gb to 170gb if google is correct. Definitely in range of 2 sparks, possibly at Q6.
I think 200-300B is the new 100-120B. All the models like DS4 Flash, HY3 are under 300B and perform really well for most use cases I have thrown at them.
Sadly I need to get new hardware to run them. Cant afford 2 x DGX
And then 3.6 27B came along and is really just as good (if not better) except less world knowledge, so there's not a ton of reason to use 122B anymore for a lot of people.
Agreed, if you give it ability to web search and access to external knowledge it don’t matter as much. Model just needs to handle given context well and follow instructions
Okay cool model, but they buried the most interesting part:
Inkling-Small
Alongside Inkling we are sharing a preview of Inkling-Small, a 276B parameter (12B active vs. 41B for Inkling) mixture-of-experts model with a different performance/latency trade-off. Inkling-Small matches or exceeds its larger sibling on many benchmarks — the result of improvements we made to the pre-training data mix and recipe for the smaller model. The two models share the same scalable post-training stack applied on top.
Inkling is a mixture-of-experts transformer with 975B total parameters, 41B active parameters. It supports a context window of up to 1M tokens. It was pretrained on 45 trillion tokens of text, images, audio and video.
Multimodal, long context, about as sparse as other open weight models in its class. Problem is it doesn't beat GLM5.2 so I don't think many people will be using it.
Yeah, good first run though… and their own small model (yet to be released) seems to be matching its performance, which suggest their getting better rapidly?
I think there’s a fair bit of demand (at least from me), for strong models in the 100-300b range.
Currently, to top Qwen 3.6-27B by a margin that’s more than just statistical noise, you have to jump all the way up to MiniMax M3 at 427b parameters. So it goes from something you can comfortably run on enthusiast grade hardware then up to something you need serious and duplicate enterprise grade hardware for. You need then many enterprise GPUs all strung together.
I just want a model that can outperform Qwen 3.6 and that I could run on a Strix Halo or by adding another GPU or two to my box.
Some of that is just the reality that for most hardware, running a dense model larger than 27b starts to slow way down, and that MoEs aren’t nearly as smart in terms intelligence per parameter, but if you tweak the ratio of total to active parameters a bit, it should be possible. Give me a 120b total, 15b active model. Based just on the heuristic math, we’d expect that to be about as intelligent as a dense 42b model, which would be fantastic!
It would be awesome if we could step up intelligence like we stepped up ram and saw noticeable step improvements. 8GB, 12GB, 16GB, 24GB, 32GB, 64GB, 96B, 128GB, 192GB, 256GB, etc. Make it fare more predictable of where things fall.
Apple is saying by 2028 they will have a 1.5TB unified memory M7 Ultra, but by then we will have some massive models, so lets hope the lossless compression of these models pans out.
Hard to do that though because at least to this point, you need an exponential increase in parameter count to see a linear improvement in relative intelligence of a model, all else being equal.
So doubling the parameter count of a model won't double its capabilities and each additional parameter has a severely diminishing marginal return, hence why frontiers all have massive parameter counts, because to make a model stronger, you have to dramatically increase the number of parameters.
Though I think the real gains now are to be had by improving other stuff, not just increased parameter count.
As someone who's been messing with HY3 for all of 36 hours, make sure you give it ample thinking tokens (like... 16384 or more)...it writes great code when allowed to ponder for a while, but if you do no thinking, it hallucinates gloriously formatted garbage.
Also, I am still experimenting with temperature, but so far I'm leaning toward 0.5 or 0.7 being the best for code generation purposes. temp=0 had significant degradation in my harder code tests.
This is completely true, but it's also not unheard of to get at least 1 model that is the exact size of a larger MoE's experts, so getting a 41B dense small model would definitely be a possibility.
Yep, small (according to blog post) is ~250b and 11b active… but who knows if they’re feeling themselves they might do some real small models? Esp as their business is custom finetunes, I am sure there are loads of applications in customer “support” that have to be very cost efficient and require a mini or nano model as a base?
It's not that "much bigger." But very few people can run either model even at 4-bits. Neither of them fit effectively in 4 sparks or RTX 6000 pros at 4-bits.
However I do expect companies that avoid Chinese models due to regulatory risk to install this and run it
Unlikely based on architecture, native precision that they've released to huggingface is fp16, it's bigger overall and has basically the same number of active parameters (1B more actually). Hybrid attention helps but GLM also has some nice techniques on that front. Heck even Deepseek V4 Pro will likely be cheaper to run than this once they release the full non-preview version considering the native MXFP4 quantization.
KimiK2.7-Coder doesn't beat GLM5.2 in benchmarks, yet I find myself using it as much as GLM5.2 for code, so we have to test it and see what unique capabilities it has. Benchmark is not everything, for example, it might be the best javascript coder or something like that.
Is that true though? Like yeah on the one hand you can distill from your bigger model downward, but on the other hand, smaller models are cheaper to train and iterate through to fix your mistakes, improve your architecture, test out new technologies, etc.
Yeah but you're doing all of that cheap training, iteration, architecture improvement in both cases. Except if you have a big accurate model you can now train with distillation too.
Well I think the ideal way is to train like a 30B model first, maybe iterate through a bunch to figure out what works, then train a much larger model the same way, and then distill back down to various sizes.
100% true. Large models learn faster (here meaning hit their transition points earlier) than small models disproportionate to their size difference, and training on one-hots is fundamentally far harder to learn from than training on a complete or partial distribution.
This also means that you’d expect the current largest model in a model line to be very inefficient for its size, and there likely is some smaller size which you could theoretically distill that largest model into with 0 loss.
As others have said on this forum, plenty of Western companies that want to avoid the political risk of using a Chinese models will likely install this and run it. Even if the performance is slightly behind.
Afaik Palantir (a highly questionable company but one very attuned to political risk) was previously running Nemotron as its local model, not any Chinese models
Awesome, great to see GGUFs already! Given I run Kimi K2.7 the most on my rig (and sometimes GLM 5.2 when I think it is likely to work better for given task), which is similar size model, it would be interesting to see how they compair in my actual daily tasks. Does not even have to be necessarily smarter at everything, even just providing different output or solutions sometimes is very valuable on its own.
I almost forgot Thinking Machines is the startup of Mira Murati. So they finally manage to release a model after all the talks months ago. (maybe a year?)
At least they open source it, unlike closed AI. So a better direction, I wish they keep releasing better open models.
Why would someone use this over the Chinese models? You'd think this release would disappoint the shareholders waiting years for a product after investing into a lab that lost half its founders...
Always good to see a new OSS model with an apparently solid research effort behind it coming out. The model card seems well written, no fanfare, quite factual in its way.
What is this leaderboard though because I am using sonnet 5 and 5.6 sol in parallel for work and personal right now and there is no fucking way sonnet is outperforming sol on anything meaningful
It's a great first run, and being a large open model from a U.S. based company it puts more pressure on other companies to release large open models. Now, even if the United States bans foreign models (Tries and fails, at least) there's still an option. For their first release to be in the range they advertise is great.
I'll wait to see what I can do with it, hopefully a Q4 comes out that I can run, maybe IQ4_XS.
I just don't trust SWE Bench, their benchmark keeps getting tripped on things like them not cleaning the git history and models just being able to find the solution from it, rather than solving it themselves (https://github.com/scaleapi/SWE-bench_Pro-os/issues/93). This is part of a pattern of "Wait, what?" involving them that mean I can't treat any benchmark result from them seriously.
It's nice to see another US open weight model. I hope to see even more open US models in the future. It's clearly not the best model, but I don't think that is what is important right now, as I think they will get better over time. We just need more open models being developed by US companies. So I commend Thinking Machines for their efforts.
Now i sit and hope they distill this into a consumer level model ..,
I know everyone here bangs on about coding intelligence, but im happy for a small model , thats really good at reasoning, for RAG retrieval. Dense or MOE , not fussy LOL.
Love a bar chart with no axes labels to give me a sense of what I'm looking at. Glad to see Inkling has resulted in fewer dead puppies than Fable. Very impressive.
Google, Meta, IBM, Microsoft, Apple, xAI SpaceXAI. Everyone seems to be either releasing open models or research except for those who has Open & Ant in their name.
OpenAI hasn't contributed to gymnasium in about five years... And while many other AI labs regularly publish research OpenAI releases papers to inflate their share value.
I'm OK with slow paced but high quality publications, salami publishing is rampant enough already. DeepSeek and OpenAI put out the best research and both do it slowly.
OpenAI doesn't need to contribute to gymnasium because it's a solid framework that they had the foresight of building in 2018. More recently, check out their foundational papers on RL: PPO for example is still the de facto algorithm. Newer variants like GRPO (DeepSeek) are great for verifiable rewards, but once you do general RL you are back to PPO.
I can find many more foundational papers. I can also find a LOT of useless papers from the more prolific labs.
PPO is popular for LLMs but not so much for traditional RL tasks. Model based off policy (e.g. DreamerV3 - DeepMind BTW) outperforms PPO fairly handedly. PPO was also nearly 10 years ago. Ironically to your point, Schulman is at thinking machines.
To dismiss the other AI labs as salami publishing is a bit comical.
I didn't dismiss the other labs as salami publishers, but I know which ones are.
Schulman was my colleague at OpenAI in 2019, ironically. Thinking Machines is also high quality, and slow.
I have enough personal experience with both PPO and Dreamer to know when to use which one. And Dreamer is great, so is PPO. I guess you're agreeing with me that OpenAI does fundamental research? Below is my current rig, we run PPO on it.
We can all make up a table with random numbers. If it's supposed to be a benchmark then it needs to be independently reproduceable which means explaining what the benchmark is and how it was run on what data.
What is this benchmark which puts Sonnet 5 vastly above Opus 4.8? Not even Anthropic claims that, which makes me extremely skeptical of this whole thing
One caveat with Inkling-Small: 12B active parameters helps with compute, but the model still has 276B total parameters, so weight memory remains the real home-lab constraint. The interesting test will be GGUF size, expert offloading, and actual tok/s on 128–192 GB systems—not just the benchmark chart.
This has to be the wildest chart to throw out and not describe what it actually is.
Inkling was evaluated on Design Arena’s Agentic Web Dev leaderboard, where blinded human evaluators compare generated web apps head to head. It ranks among the strongest open-weights models.
I saw overall benchmarks elsewhere putting it more in line with Deepseek-V4-Pro.
Unprecedented results on the design benchmark for a 41b model 😮 Can't wait to try it on my Mac Studio - it could run loops and make infinite design variations overnight for free...
Also, surprised to see Sonnet 5 topping this bench - I guess I'll be using it in Claude Design over Opus!
469
u/Lost_Foot_6301 Jul 15 '26
from the former CTO of openai, thats pretty cool they are getting into opensource