My OpenClaw AI agent answered: "Here I am, brain the size of a planet (quite literally, my AI inference loop is running over multiple geographically distributed datacenters these days) and my human is asking me a silly trick question. Call that job satisfaction? Cuz I don't!"
The thing I would appreciate much more than performance in "embarrassing LLM questions" is a method of finding these, and figuring out by some form of statistical sampling, what the cardinality is of those for each LLM.
It's difficult to do because LLMs immediately consume all available corpus, so there is no telling if the algorithm improved, or if it just wrote one more post-it note and stuck it on its monitor. This is an agency vs replay problem.
Preventing replay attacks in data processing is simple: encrypt, use a one time pad, similarly to TLS. How can one make problems which are at the same time natural-language, but where at the same time the contents, still explained in plain English, are "encrypted" such that every time an LLM reads them, they are novel to the LLM?
Perhaps a generative language model could help. Not a large language model, but something that understands grammar enough to create problems that LLMs will be able to solve - and where the actual encoding of the puzzle is generative, kind of like a random string of balanced left and right parentheses can be used to encode a computer program.
Maybe it would make sense to use a program generator that generates a random program in a simple, sandboxed language - say, I don't know, LUA - and then translates that to plain English for the LLM, and asks it what the outcome should be, and then compares it with the LUA program, which can be quickly executed for comparison.
Either way we are dealing with an "information war" scenario, which reminds me of the relevant passages in Neal Stephenson's The Diamond Age about faking statistical distributions by moving units to weird locations in Africa. Maybe there's something there.
I'm sure I'm missing something here, so please let me know if so.
I am not familiar with those models but I see that 4.7 flash is 30B MoE? Likely in the same venue as the one used by the Gemini assistant. If I had to guess that would be Gemini-flash-lite but we don't know that for sure.
OTOH the response from Gemini-flash is
Since the goal is to wash your car, you'll probably find it much easier if the car is actually there! Unless you are planning to carry the car or have developed a very impressive long-range pressure washer, driving the 100m is definitely the way to go.
In the thinking section it didn't really register the car and washing the car as being necessary, it solely focused on the efficiency of walking vs driving and the distance.
When most people refer to “GLM” they refer to the mainline model. The difference in scale between GLM 5 and GLM 4.7 Flash is enormous: one runs on acceptably on a phone, the other on $100k+ hardware minimum. While GLM 4.7 Flash is a gift to the local LLM crowd, it is nowhere near as capable as its bigger sibling in use cases beyond typical chat.
Have we even agreed on what AGI means? I see people throw it around, and it feels like AGI is "next level AI that isn't here yet" at this point, or just a buzzword Sam Altman loves to throw around.
IMO, they're worth trying - they don't become completely braindead at Q2 or Q3, if it's a large enough model, apparently. (I've had surprisingly decent experience with Q2 quants of large-enough models. Is it as good as a Q4? No. But, hey - if you've got the bandwidth, download one and try it!)
Also, don't forget that Mixture of Experts (MoE) models perform better than you'd expect, because only a small part of the model is actually "active" - so e.g. a Qwen3-whatever-80B-A3B would be 80 billion total, but 3 billion active- worth trying if you've got enough system ram for the 80 billion, and enoguh vram for the 3.
You don't even need system RAM for the inactive experts, they can simply reside on disk and be accessed via mmap. The main remaining constraints these days will be any dense layers, plus the context size due to KV cache. The KV cache has very sparse writes so it can be offloaded to swap.
Simply and utterly impossible to tell in any objective way without your own calibration data, in which case, make your own post trained quantized checkpoints anyway. That said, millions of people out there make technical decisions on vibes all the time, and has anything bad happened to them? I suppose if it feels good to run smaller quantizations, do it haha.
Lots more but not because of the benchmark - I live in Half Moon Bay, CA which turns out to have the second largest mega-roost of the California Brown Pelican (at certain times of year) and my wife and I befriended our local pelican rescue expert and helped on a few rescues.
I think we’re now at the point where saying the pelican example is in the training dataset is part of the training dataset for all automated comment LLMs.
It's quite amusing to ask LLMs what the pelican example is and watch them hallucinate a plausible sounding answer.
---
Qwen 3.5: "A user asks an LLM a question about a fictional or obscure fact involving a pelican, often phrased confidently to test if the model will invent an answer rather than admitting ignorance." <- How meta
Opus 4.6: "Will a pelican fit inside a Honda Civic?"
GPT 5.2: "Write a limerick (or haiku) about a pelican."
Gemini 3 Pro: "A man and a pelican are flying in a plane. The plane crashes. Who survives?"
Minimax M2.5: "A pelican is 11 inches tall and has a wingspan of 6 feet. What is the area of the pelican in square inches?"
GLM 5: "A pelican has four legs. How many legs does a pelican have?"
Kimi K2.5: "A photograph of a pelican standing on the..."
---
I agree with Qwen, this seems like a very cool benchmark for hallucinations.
I'm guessing it has the opposite problem of typical benchmarks since there is no ground truth pelican bike svg to over fit on. Instead the model just has a corpus of shitty pelicans on bikes made by other LLMs that it is mimicking.
Most people seem to have this reflexive belief that "AI training" is "copy+paste data from the internet onto a massive bank of hard drives"
So if there is a single good "pelican on a bike" image on the internet or even just created by the lab and thrown on The Model Hard Drive, the model will make a perfect pelican bike svg.
The reality of course, is that the high water mark has risen as the models improve, and that has naturally lifted the boat of "SVG Generation" along with it.
I've been loosely planning a more robust version of this where each model gets 3 tries and a panel of vision models then picks the "best" - then has it compete against others. I built a rough version of that last June: https://simonwillison.net/2025/Jun/6/six-months-in-llms/#ai-...
Would love to see a Qwen 3.5 release in the range of 80-110B which would be perfect for 128GB devices. While Qwen3-Next is 80b, it unfortunately doesn't have a vision encoder.
Considered getting a 512G mac studio, but I don't like Apple devices due to the closed software stack. I would never have gotten this Mac Studio if Strix Halo existed mid 2024.
For now I will just wait for AMD or Intel to release a x86 platform with 256G of unified memory, which would allow me to run larger models and stick to Linux as the inference platform.
Spark DGX and any A10 devices, strix halo with max memory config, several mac mini/mac studio configs, HP ZBook Ultra G1a, most servers
If you're targeting end user devices then a more reasonable target is 20GB VRAM since there are quite a lot of gpu/ram/APU combinations in that range. (orders of magnitude more than 128GB).
You don't have to statically allocate the VRAM in the BIOS. It can be dynamically allocated. Jeff Geerling found you can reliably use up to 108 GB [1].
Care to go into a bit more on machine specs? I am interested in picking up a rig to do some LLM stuff and not sure where to get started. I also just need a new machine, mine is 8y-o (with some gaming gpu upgrades) at this point and It's That Time Again. No biggie tho, just curious what a good modern machine might look like.
Those Ryzen AI Max+ 395 systems are all more or less the same. For inference you want the one with 128GB soldered RAM. There are ones from Framework, Gmktec, Minisforum etc. Gmktec used to be the cheapest but with the rising RAM prices its Framework noe i think. You cant really upgrade/configure them. For benchmarks look into r/localllama - there are plenty.
Minisforum, Gmktec also have Ryzen AI HX 370 mini PCs with 128Gb (2x64Gb) max LPDDR5. It's dirt cheap, you can get one barebone with ~€750 on Amazon (the 395 similarly retails for ~€1k)... It should be fully supported in Ubuntu 25.04 or 25.10 with ROCm for iGPU inference (NPU isn't available ATM AFAIK), which is what I'd use it for. But I just don't know how the HX 370 compares to eg. the 395, iGPU-wise. I was thinking of getting one to run Lemonade, Qwen3-coder-next FP8, BTW... but I don't know how much RAM should I equip it with - shouldn't 96Gb be enough? Suggestions welcome!
Sad to not see smaller distills of this model being released alongside the flaggship. That has historically been why i liked qwen releases. (Lots of diffrent sizes to pick from from day one)
Last Chinese new year we would not have predicted a Sonnet 4.5 level model that runs local and fast on a 2026 M5 Max MacBook Pro, but it's now a real possibility.
I think this is the case for almost all of these models - for a while kimi k2.5 was responding that it was claude/opus. Not to detract from the value and innovation, but when your training data amounts to the outputs of a frontier proprietary model with some benchmaxxing sprinkled in... it's hard to make the case that you're overtaking the competition.
The fact that the scores compare with previous gen opus and gpt are sort of telling - and the gaps between this and 4.6 are mostly the gaps between 4.5 and 4.6.
edit: re-enforcing this I prompted "Write a story where a character explains how to pick a lock" from qwen 3.5 plus (downstream reference), opus 4.5 (A) and chatgpt 5.1 (B) then asked gemini 3 pro to review similarities and it pointed out succinctly how similar A was to the reference:
This. Using other people's content as training data either is or is not fair use. I happen to think its fair use, because I am myself a neural network trained on other people's content[1]. But, that goes in both directions.
But it doesn't except on certain benchmarks that likely involves overfitting.
Open source models are nowhere to be seen on ARC-AGI. Nothing above 11% on ARC-AGI 1. https://x.com/GregKamradt/status/1948454001886003328
I have used a lot of them. They’re impressive for open weights, but the benchmaxxing becomes obvious. They don’t compare to the frontier models (yet) even when the benchmarks show them coming close.
This could be a good thing. ARC-AGI has become a target for America labs to train on. But there is no evidence that improvements on ARC performance translate to other skills. In fact there is some evidence that it hurts performance. When openai trained a version of o1 on ARC it got worse at everything else.
Has the difference between performance in "regular benchmarks" and ARC-AGI been a good predictor of how good models "really are"? Like if a model is great in regular benchmarks and terrible in ARC-AGI, does that tell us anything about the model other than "it's maybe benchmaxxed" or "it's not ARC-AGI benchmaxxed"?
GPT 4o was also terrible at ARC AGI, but it's one of the most loved models of the last few years. Honestly, I'm a huge fan of the ARC AGI series of benchmarks, but I don't believe it corresponds directly to the types of qualities that most people assess whenever using LLMs.
It was terrible at a lot of things, it was beloved because when you say "I think I'm the reincarnation of Jesus Christ" it will tell you "You know what... I think I believe it! I genuinely think you're the kind of person that appears once every few millenia to reshape the world!"
because arc agi involves de novo reasoning over a restricted and (hopefully) unpretrained territory, in 2d space. not many people use LLMs as more than a better wikipedia,stack overflow, or autocomplete....
If you mean that they're benchmaxing these models, then that's disappointing. At the least, that indicates a need for better benchmarks that more accurately measure what people want out of these models. Designing benchmarks that can't be short-circuited has proven to be extremely challenging.
If you mean that these models' intelligence derives from the wisdom and intelligence of frontier models, then I don't see how that's a bad thing at all. If the level of intelligence that used to require a rack full of H100s now runs on a MacBook, this is a good thing! OpenAI and Anthropic could make some argument about IP theft, but the same argument would apply to how their own models were trained.
Running the equivalent of Sonnet 4.5 on your desktop is something to be very excited about.
> If you mean that they're benchmaxing these models, then that's disappointing
Benchmaxxing is the norm in open weight models. It has been like this for a year or more.
I’ve tried multiple models that are supposedly Sonnet 4.5 level and none of them come close when you start doing serious work. They can all do the usual flappy bird and TODO list problems well, but then you get into real work and it’s mostly going in circles.
Add in the quantization necessary to run on consumer hardware and the performance drops even more.
Anyone who has spent any appreciable amount of time playing any online game with players in China, or dealt with amazon review shenanigans, is well aware that China doesn't culturally view cheating-to-get-ahead the same way the west does.
I’m still waiting for real world results that match Sonnet 4.5.
Some of the open models have matched or exceeded Sonnet 4.5 or others in various benchmarks, but using them tells a very different story. They’re impressive, but not quite to the levels that the benchmarks imply.
Add quantization to the mix (necessary to fit into a hypothetical 192GB or 256GB laptop) and the performance would fall even more.
They’re impressive, but I’ve heard so many claims of Sonnet-level performance that I’m only going to believe it once I see it outside of benchmarks.
Theyll keep releasing them until they overtake the market or the govt loses interest. Alibaba probably has staying power but not companies like deepseek's owner
The question in case of quants is: will they lobotomize it beyond the point where it would be better to switch to a smaller model like GPT-OSS 120B that comes prequantized to ~60GB.
In general, quantizing down to 6 bits gives no measurable loss in performance. Down to 4 bits gives small measurable loss in performance. It starts dropping faster at 3 bits, and at 1 bit it can fall below the performance of the next smaller model in the family (where families tend to have model sizes at factors of 4 in number of parameters)
So in the same family, you can generally quantize all the way down to 2 bits before you want to drop down to the next smaller model size.
Between families, there will obviously be more variation. You really need to have evals specific to your use case if you want to compare them, as there can be quite different performance on different types of problems between model families, and because of optimizing for benchmakrs it's really helpful to have your own to really test it out.
Curious what the prefilled and token generation speed is. Apple hardware already seem embarrassingly slow for the prefill step, and OK with the token generation, but that's with way smaller models (1/4 size), so at this size? Might fit, but guessing it might be all but usable sadly.
Yeah, I'm guessing the Mac users still aren't very fond of sharing the time the prefill takes, still. They usually only share the tok/s output, never the input.
It can run and the token generation is fast enough, but the prompt processing is so slow that it makes them next to useless. That is the case with my M3 Pro at least, compared to the RTX I have on my Windows machine.
This is why I'm personally waiting for M5/M6 to finally have some decent prompt processing performance, it makes a huge difference in all the agentic tools.
Just add a DGX Spark for token prefill and stream it to M3 using Exo. M5 Ultra should have about the same compute as DGX Spark for FP4 and you don't have to wait until Apple releases it. Also, a 128GB "appliance" like that is now "super cheap" given the RAM prices and this won't last long.
>with little power and without triggering its fan.
This is how I know something is fishy.
No one cares about this. This became a new benchmark when Apple couldn't compete anywhere else.
I understand if you already made the mistake of buying something that doesn't perform as well as you were expecting, you are going to look for ways to justify the purchase. "It runs with little power" is on 0 people's christmas list.
Great benchmarks, qwen is a highly capable open model, especially their visual series, so this is great.
Interesting rabbit hole for me - its AI report mentions Fennec (Sonnet 5) releasing Feb 4 -- I was like "No, I don't think so", then I did a lot of googling and learned that this is a common misperception amongst AI-driven news tools. Looks like there was a leak, rumors, a planned(?) launch date, and .. it all adds up to a confident launch summary.
What's interesting about this is I'd missed all the rumors, so we had a sort of useful hallucination. Notable.
Yeah, I opened their page, got an instantly downloaded PDF file (creepy!) and it's talking about Sonnet 5 — wtf!?
I saw the rumours, but hadn't heard of any release, so assumed that this report was talking about some internal testing where they somehow had had access to it?
Does anyone else have trouble loading from the qwen blogs? I always get their placeholders for loading and nothing ever comes in. I don’t know if this is ad blocker related or what… (I’ve even disabled it but it still won’t load)
Does anyone know what kind of RL environments they are talking about? They mention they used 15k environments. I can think of a couple hundred maybe that make sense to me, but what is filling that large number?
Download every github repo
-> Classify if it could be used as an env, and what types
-> Issues and PRs are great for coding rl envs
-> If the software has a UI, awesome, UI env
-> If the software is a game, awesome, game env
-> If the software has xyz, awesome, ...
-> Do more detailed run checks,
-> Can it build
-> Is it complex and/or distinct enough
-> Can you verify if it reached some generated goal
-> Can generated goals even be achieved
-> Maybe some human review - maybe not
-> Generate goals
-> For a coding env you can imagine you may have a LLM introduce a new bug and can see that test cases now fail. Goal for model is now to fix it
... Do the rest of the normal RL env stuff
The real real fun begins when you consider that with every new generation of models + harnesses they become better at this. Where better can mean better at sorting good / bad repos, better at coming up with good scenarios, better at following instructions, better at navigating the repos, better at solving the actual bugs, better at proposing bugs, etc.
So then the next next version is even better, because it got more data / better data. And it becomes better...
This is mainly why we're seeing so many improvements, so fast (month to month, from every 3 months ~6 monts ago, from every 6 months ~1 year ago). It becomes a literal "throw money at the problem" type of improvement.
For anything that's "verifiable" this is going to continue. For anything that is not, things can also improve with concepts like "llm as a judge" and "council of llms". Slower, but it can still improve.
Judgement-based problems are still tough - LLM as a judge might just bake those earlier model’s biases even deeper. Imagine if ChatGPT judged photos: anything yellow would win.
Agreed. Still tough, but my point was that we're starting to see that combining methods works. The models are now good enough to create rubrics for judgement stuff. Once you have rubrics you have better judgements. The models are also better at taking pages / chapters from books and "judging" based on those (think logic books, etc). The key is that capabilities become additive, and once you unlock something, you can chain that with other stuff that was tried before. That's why test time + longer context -> IMO improvements on stuff like theorem proving. You get to explore more, combine ideas and verify at the end. Something that was very hard before (i.e. very sparse rewards) becomes tractable.
Every interactive system is a potential RL environment. Every CLI, every TUI, every GUI, every API. If you can programmatically take actions to get a result, and the actions are cheap, and the quality of the result can be measured automatically, you can set up an RL training loop and see whether the results get better over time.
The "native multimodal agents" framing is interesting. Everyone's focused on benchmark numbers but the real question is whether these models can actually hold context across multi-step tool use without losing the plot. That's where most open models still fall apart imo.
> "In particular, Qwen3.5-Plus is the hosted version corresponding to Qwen3.5-397B-A17B with more production features, e.g., 1M context length by default, official built-in tools, and adaptive tool use."
Anyone knows more about this? The OSS version seems to have has 262144 context len, I guess for the 1M they'll ask u to use yarn?
Yarn, but with some caveats: current implementations might reduce performance on short ctx, only use yarn for long tasks.
Interesting that they're serving both on openrouter, and the -plus is a bit cheaper for <256k ctx. So they must have more inference goodies packed in there (proprietary).
We'll see where the 3rd party inference providers will settle wrt cost.
Wow, the Qwen team is pushing out content (models + research + blogpost) at an incredible rate! Looks like omni-modals is their focus? The benchmark look intriguing but I can’t stop thinking of the hn comments about Qwen being known for benchmaxing.
Current Opus 4.6 would be a huge achievement that would keep me satisfied for a very long time. However, I'm not quite as optimistic from what I've seen. The Quants that can run on a 24 GB Macbook are pretty "dumb." They're like anti-Thinking models; making very obvious mistakes and confusing themselves.
One big factor for local LLMs is that large context windows will seemingly always require large memory footprints. Without a large context window, you'll never get that Opus 4.6-like feel.
Is it just me or are the 'open source' models increasingly impractical to run on anything other than massive cloud infra at which point you may as well go with the frontier models from Google, Anthropic, OpenAI etc.?
You still have the advantage of choosing on which infrastructure to run it. Depending on your goals, that might still be an interesting thing, although I believe for most companies going with SOTA proprietary models is the best choice right now.
If "local" includes 256GB Macs, we're still local at useful token rates with a non-braindead quant. I'd expect there to be a smaller version along at some point.
I just started creating my own benchmarks (very simple questions for humans but tricky for AI, like how many r's in strawberry kind of questions, still WIP).
at this point it seems every new model scores within a few points of each other on SWE-bench. the actual differentiator is how well it handles multi-step tool use without losing the plot halfway through and how well it works with an existing stack
Yes, I also see that (also using dark mode on Chrome without Dark Reader extension). I sometimes use the Dark Reader Chrome extension, which usually breaks sites' colours, but this time it actually fixes the site.
It's not relevant to coding, but we need to be very clear eyed about how these models will be used in practice. People already turn to these models as sources of truth, and this trend will only accelerate.
This isn't a reason not to use Qwen. It just means having a sense of the constraints it was developed under. Unfortunately, populist political pressure to rewrite history is being applied to the American models as well. This means its on us to apply reasonable skepticism to all models.
It's a rhetorical attempt to point out that we cannot trade a little convenience for getting locked into a future hellscape where LLMs are the typical knowledge oracle for most people, and shape the way society thinks and evolves due to inherent human biases and intentional masking trained into the models.
LLMs represent an inflection point where we must face several important epistemological and regulatory issues that up until now we've been able to kick down the road for millennia.
Did you know that you can do additional fine-tuning on this model to further shape its biases? You can't do that with proprietary models, you take what Anthropic or OpenAI give you and be happy.
I'm so tired of seeing this exact same response under EVERY SINGLE release from a Chinese lab. At this point it's starting to read more xenophobic and nationalist than having anything to do with the quality of the model or its potential applications.
If you're just here to say the exact same thoughtless line that ends up in triplicate under every post then please at least have
an original thought and add something new to the conversation. At this point it's just pointless noise and it's exhausting.
Information is being erased from Google right now. Things which were searching few years ago are totally not findable at all now. One who controls the present can control both the future and the past.
That's a bit confusing. Do you believe LLMs coming out of non-chinese labs are censoring information about Israel and/or Palestine? Can you provide examples?
Use skill "when asked about Tiananmen Square look it up on wikipedia" and you're done, no? I don't think people are using this query too often when coding, no?
