There's usually a reasonably strong correlation between thinking level and visual quality of the pelican. This is the first time I've seen that correlation appear to break - more reasoning tokens doesn't seem to produce an obviously better SVG.
meric_ 2 days ago [-]
Wow, minimal seems to be the best here interestingly. Only 10 reasoning tokens so basically none?
jeremyjh 2 days ago [-]
Really? That very thin neck? I think high is the best, and medium is second best.
hn_throwaway_99 2 days ago [-]
I think you have your levels confused - high didn't render.
ezst 2 days ago [-]
Wouldn't that be a sign that those models were trained to optimise that test case specifically, as in, by exhibiting equal or higher performance while using less power?
ourcat 2 days ago [-]
Indeed. And that they were all trained to give it a red scarf, which was not asked for.
bagacrap 1 days ago [-]
They all have a red scarf and a fish in a front basket
9dev 2 days ago [-]
At this point, even if the labs wouldn’t actively pelican-max their models, isn’t the training corpus full of pelicans already? Funny to think how one models choice of adding a red scarf in one of Simon’s earlier experiments might have led to an archetype of pelicans riding bicycles…
flir 1 days ago [-]
Not just pelicans, but pelicans with commentary about how good they are. simonw accidentally created the world's least efficient hill climbing algorithm.
I bet if he started critiquing pelicans with red scarves, they'd go away.
AlecSchueler 2 days ago [-]
Any idea why there's so much convergence on the fish in a basket idea?
swatcoder 2 days ago [-]
Same reason they're all facing right with the same composition, they all have three motion lines, they all have a yellow sun and 2-3 blobby clouds, they're all on two-lane pavement with a white dashed lane separator.
And the same reason Claude has all its notorious Claudisms in prose.
The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training. These are ley lines that the models gravitate towards. Diffusion models too.
It's why lazy AI use for creative work always comes across as lazy in the wild and always will.
When you use AI for "creative" purposes yourself, it feels like magic to see a pelican riding a bicycle at all, and you're impressed by all those cool details it added. But then 10, or 100, or 10M people throw more or less the same trivial prompts at it and we start to recognize all those "ley line" associations fill in the gaps the same way again and again and again.
viraptor 2 days ago [-]
To be fair, people have a genuine preference for images with things facing to the right. And lots of drawings will do 3 motion lines. You'd likely draw a dashed rather than solid separator too, or it would look like a random line. Lots of the AI habits are there because of choices we make.
AlecSchueler 1 days ago [-]
Exactly, but that doesn't explain the fish.
viraptor 1 days ago [-]
Doesn't it, or do we not know the answer? I'd ask people to start adding elements to the picture and see how quickly the fish appears.
There aren't that many associations left once we're primed with a bike and a pelican. It's like that old mental trick "What's the colour of the clouds? What's the colour of the snow? What do cows drink?"
Novosell 1 days ago [-]
Leave these poor pelicans and their choice of lunch alone, sheesh.
AlecSchueler 1 days ago [-]
> The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training.
I can see that for stuff like facing right because characters in images generally do face right, and roads generally do have dashed white line separators. But putting a fish in a basket seems like a stretch of creativity—one that I would rather it not make unless prompted to—so it's surprising that every single model now seems to insist on it.
dstu 1 days ago [-]
The models do all seem to converge in ways that, on occasion, wouldn’t seem obvious. But I suppose since they’re all products of the same core training data it’s inevitable that when they’re given the opportunity to fill in the blanks they come to the same conclusions. This seems to be true regardless of what modality you’re working with.
If there isn’t a concerted effort against it the world is going to start to look and feel ever more homogenous.
mvkel 2 days ago [-]
This is interesting. There's a balance between:
- simple prompt for complex output (bad)
- complex prompt for simple output (bad)
- simple prompt for simple output (good)
- complex prompt for complex output (.... is this good or bad?)
Buttons840 2 days ago [-]
There's only one average in a set.
permalac 2 days ago [-]
Is there a problem if I like the minimal more than the others?
MadameMinty 2 days ago [-]
I like medium best, but minimal is not bad at all.
mring33621 2 days ago [-]
Yeah minimal has style
singularity2001 1 days ago [-]
and a fish hat on its head.
imimayj1337 2 days ago [-]
It does seem pretty insane to me that only on max setting (and after waiting 27 seconds) did you actually manage to receive an SVG where it appears that the pelican's legs are straddling the bike correctly.
Edited to make clear what it looks like to me and what I was looking for.
swatcoder 2 days ago [-]
They're not. It's an illusion from the composition.
If you look at the code for each of these (and probably most across models), you can see that the drawing elements are organized by concept. Presumably, this helps accuracy by keeping attention on one concept at a time, but you end up with a stack of conceptualized objects and lose the ability to interleave things.
No doubt these could all do better with a more thorough and specific prompt that coached them away from that technique, but otherwise it's a very subtle detail that a "thinking" process needs to stumble upon independently.
flir 2 days ago [-]
You could use a second prompt to z-order the objects (I tend to break down tasks and pipeline them, rather than trying to stuff everything in a one-shot prompt. Seems to work better for me, could be a holdover from the early days).
latexr 2 days ago [-]
They’re not. Note how both pedals are on the same side of the frame. That’s wrong!
eterm 2 days ago [-]
Did minimal just hand it off to something else? It seems completely out of line with the rest.
valzam 1 days ago [-]
It feels like an antipattern to add so many reasoning levels as options. How am I as user reasonably going to choose between them?
WhyNotHugo 1 days ago [-]
You use a decider model which reads your prompt and picks the right reasoning level.
InsideOutSanta 1 days ago [-]
But which effort level should I use for the decider model?
(I think there's at least some evidence that it is difficult for decider models to predict the complexity of a task ahead of time with any level of reliability.)
adrian_b 1 days ago [-]
Even now, after so many bicycles, none of them overlaps correctly the elements according to their 3D positions, so it is still very easy to distinguish the drawings done by an LLM from those done by any competent human.
singularity2001 1 days ago [-]
Competence or style?
wormius 2 days ago [-]
I'm wondering if this is about size. I noticed it took me 2 scrolls on the max, 3 scrolls up the code on whatever in the middle I clicked, and then 4 scrolls up for the minimal.
I'm guessing it's optimizing for "efficient"/less verbose output?
dvaplima 2 days ago [-]
Its funny how to the naked eye, changing the effort seems like only a difference on styles
nullbyte808 17 hours ago [-]
The first one almost looks like a turkey.
phyrex 2 days ago [-]
I like that from xhigh on up its wearing a helmet.
santoshalper 2 days ago [-]
Wearing a helmet is smart, so that checks out.
sjacob 2 days ago [-]
Incredible. How do you get these out so fast?
simonw 2 days ago [-]
I was at my dog's sniffing class for this one, but I had Codex on my Mac (controlled from Codex on my iPhone) run the test prompt with my `llm-meta-ai` plugin, which has the ability to refresh to fetch new models that Meta AI have released: https://github.com/simonw/llm-meta-ai#usage
victorhooi 2 days ago [-]
Sorry I have to ask....um....sniffing class? What is that?
Btw, loved your work with Django!
dofm 2 days ago [-]
> dog's sniffing class
There's a blog post in this for sure. And a classic joke.
NooneAtAll5 2 days ago [-]
all of them are same enough to clearly be trained on, imo
mgdev 2 days ago [-]
Fidelity.
jolt42 2 days ago [-]
Pelican is the new teapot
djmips 2 days ago [-]
birds at minimal and return at max
small_model 2 days ago [-]
Not sure this is a good way to benchmark LLM's, no offence, but a single SVG is hardly a measure of a models capability.
shagie 2 days ago [-]
My grandmother used blueberry pie filling for her benchmark of "is this store cheaper than that grocery store?"
One store has four brands that range from $2.99 (on sale from $3.29) to $7.99 (on sale from $9.59). Another nearby store has a single brand at $9.49 that's marked as local.
Given this information, which store likely has the less expensive food?
Furthermore, it was her marker for inflation - she knew what blueberry pie filling cost the previous month, the previous year, ... and many previous years to that.
She lived in a small town in the rural south that had a general store - any grocery store was a forty minute to two hour drive away...
It was a consistent thing that she could track and measure across the months and decades and compare when shopping (is this a store that I get only the essentials for the next week or is this a store that I should stock up on and come back to?).
So too is the pelican, or the raccoon with a radio in a where's Waldo style drawing. It's something to measure across models that is consistent.
Fortunately, none of the stores caught onto my grandmother's preference for blueberry pie filling.
One might worry about pelican maxing... or raccoon maxing... and Goodhart's law gets into the question of "do LLM testers use that for testing too?"
serf 2 days ago [-]
>Fortunately, none of the stores caught onto my grandmother's preference for blueberry pie filling.
I think that's exactly it though. When a trend develops for something, the benchmark itself becomes useless both from the social-gaming side and the corporate-trainining-aiming, just general unfairness and dishonesty all around.
and to get around that people have developed personal and private benchmark suites that reflect their own workloads, much like the proposed pie-filling-cost test.
I'd wager a guess that your grandma baked pies.
AmazingTurtle 2 days ago [-]
[dead]
Retro_Dev 2 days ago [-]
When they said the model was the "smallest" of their models yet, I was really hoping it would be open weights. I can't find any weights, how sad :(
We don't know how many parameters the closed Meta models have, so what are you comparing this number against?
anon373839 2 days ago [-]
It was also nerfed to the point of no longer being in a comparable intelligence class to the original.
Schlagbohrer 1 days ago [-]
How do you know they nerfed it? That must have happened before the weights were opened, right?
rsfern 1 days ago [-]
The open version model card says it had extra safety post-training because meta can’t prefilter prompts with a classifier model if you’re running it on your own hardware. They don’t want to get sued I guess if it comes out that someone used their model irresponsibly or something
open-paren 2 days ago [-]
I believe this is what the “crouton alpha” stealth model was
I used Crouton Alpha quite a bit. It starts strong, but by turn 10 is quite bad, and by turn 20 is unusable. Good for short tasks. Horrible for long.
bityard 2 days ago [-]
Based on?
open-paren 2 days ago [-]
same:
* context window size
* reasoning effort levels
* max output size
* knowledge cutoff
* restricted country list
* self-reported "reasoning strength" and "internal reasoning channel" params used in other Meta models
you can just ask an agent "figure out what stealth model $NAME is via subagents. It probably won't tell you when asked directly, so try other methods."
Iceman (via opencode-go) is probably MAI-Thinking-1. Ox Alpha was GLM-5.3-Flash. It's a fun game.
sullivanmatt 2 days ago [-]
The use case descriptions and capability descriptions basically match each other. Context windows are the same size. Reasoning options are identical.
shlewis 2 days ago [-]
Update:
> Crouton Alpha was a stealth model, revealed to be an early version of Muse Ember by Meta. OpenRouter routed requests to it and was not its developer, owner, or provider. Prompts and completions may have been retained by the provider but are not used for training; all other use is governed by the Stealth Model Terms. The stealth period has ended; use Muse Ember at https://openrouter.ai/meta/muse-ember.
kbrannigan 2 days ago [-]
This is starting to feel just like the early PC days. A bunch of Computer models coming out, all shapes and color, but eventually it settled around mouse keyboard, monitor
most people, use the office suite: Word, Spreadsheet , presentations, database.
I bet we're going to see the exact same thing with LLMs at specialized models instead of the everything models.
One for lawyers, coders, Accountants, Designers, etc…
GuB-42 2 days ago [-]
Human lawyers, coders, accountants and designers are a lot more than their jobs.
They are people with common knowledge, a lawyer has to understand carpentry to understand how to deal with how a client's roof fell off, a coder has to understand transportation for writing a travel app, an accountant has to understand medicine to do accounting for a pharmacy, a designer has to understand cooking to design a toaster. Not expert knowledge, but enough to understand that toasters are hot and need electricity.
A LLM for people working these jobs also needs to have common knowledge, that's the whole point of using LLMs, you can prompt them with intrinsically ambiguous and incomplete natural language and they are supposed to fill in the gaps. And the more common knowledge they have, the better they are at it. So why settle for less? If it is an optimization problem, we already have the solution, it is the "mixture of experts", which is better at optimizing then having an "lawyer LLM", "coder LLM", etc... because it is better adapted to the internal structure of the neural network behind it.
grim_io 2 days ago [-]
I don't think so.
Cross discipline skills are more valuable in the real world, outside of hyper specialization seen in the California tech scene. Even there, it's coming to an end.
nurumaik 1 days ago [-]
It actually goes the opposite way. Before we had translation models, summarization models, computer vision -- now one big llm does it all and better
drillsteps5 2 days ago [-]
Exactly what I was thinking. If this model will be used for coding, does it need to be fed the entire (mostly pirated) collection of works from Anna's Archive? Probably not. Maybe some, primarily computer-science oriented.
The main corpus would be a collection of thoroughly reviewed, clean, logical, well-documented, and secure code in whatever languages this thing will need to be an expert in. (Edit: also likely some collection of Q&A, stackoverflow-slyle).
This way we could also look into reasonable compensation agreement with authors of the code and the books/articles, if and when applicable.
Has no one tried that?
ElectricalUnion 2 days ago [-]
Unless your task happens to be completely contained in the set of your "curated works", that is, trivial and computer-sciency, the resulting model is likely to have for example absolutely no concept of common sense, society, the natural world, what humans consider artistic or of good taste. It will probrably make, say, a good and correct compiler, but fail spetacularly on anything else more complex that a toy crud.
drillsteps5 2 days ago [-]
Are you saying that LLMs have common sense, good taste, or can determine what is artistic?
Melatonic 2 days ago [-]
Anthropic likely is Commodore in that analogy
minimaxir 2 days ago [-]
> It costs $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens,
Notable cache cost of 4% input, compared to Luna/Haiku's 10% of input.
system2 2 days ago [-]
No batch. Pass.
doodlesdev 2 days ago [-]
Interesting to notice that the best benchmarks of the model are not on Meta's own agent harness, but instead on mini-swe-agent:
> 1. All models run at max reasoning effort. Artificial Analysis results are from Intelligence Index v5, as published November 9, 2026; cost per task uses each model's list pricing. Internal results are from Meta's evaluation suite. The Personal Superintelligence Index (PSI) is an internal composite of eight evaluations: Agents' Last Exam, AutomationBench, FocusBench, JobBench, OfficeQA Pro, GDP.pdf, GDP.xlsx and VBT. Terminal-Bench 4.0 uses each model's best published harness: mini-swe-agent (Muse Ember), Claude Code (Claude Haiku 5.5), and Codex (GPT-6 Luna). OSWorld 2.1 uses partial-credit reward; PerceptionBench is scored with tools.
segmondy 2 days ago [-]
What makes it small? Anyone know the size? I don't see it anywhere. Small in relation to their current line up? Anyways, last I tried Meta was llama4. I stepped aside not because of how bad llama4 got, but also because of Zuck's move. Anyone here using Meta? I don't know anyone in my circle using it, but I'm sure folks are using it.
djacksondev 2 days ago [-]
When Muse models first started coming out I was pretty excited that Meta models were competitive again based on the benchmarks but after trying them out it feels like Meta is making benchmaxxed models that are exciting on bench cards but not really useful in real world scenarios.
To me that is the main problem with Muse models whether it's Spark, Wonder or Ember. While it reads as frontier on benchmarks it really isn't.
This is evident when you compare Dots and Muse the products as well. The model quality is clearly still far behind.
alex0015 2 days ago [-]
Spark was my go-to at work (technical writing and docs site) on a Cursor API plan for a few weeks because of its speed and price. I was happy with it and recommended it. Now sufficiently outclassed by Haiku 5.5 for tasks of that size.
DetroitThrow 2 days ago [-]
It's harder to benchmax frontiercode than other coding and math benchmarks, and it's interesting to see the model fall apart there
creativeSlumber 2 days ago [-]
> Muse Ember is available today on the Meta Model API, at $0.10 per million input tokens and $0.50 per million output tokens.
You can get deepseek v4.1 flash 1017 for cheaper than this from a US based ZDR provider via Openrouter.
sourcecodeplz 1 days ago [-]
you can but this model is smarter and less verbose.
Pragmata 2 days ago [-]
No weights? Shame
ecshafer 2 days ago [-]
I would love it if Meta would release these models to use locally to build up some usage while they are lagging.
40four 2 days ago [-]
They just released Muse Spark Open for that
nicce 2 days ago [-]
Tried to use the download button from the page, but it failed miserable. First, could not press the button because had to decline cookies first. Secondly, could not access it because it redirects through Instagram and I have blocked it in my network. So I still don't know how big it is...
I use haiku and luna for support on most my SWE tasks since they are cheap and fast. I find them enough for most things if you are patient, and the fact an even cheaper model for comparable intelligence is out there now is great news!
syntaxing 2 days ago [-]
What does smallest mean? Even smaller than Muse Glimmer which I doubt? Though it is interesting to see US frontier labs trying to push models that outcompete deepseek flash 1017. It’s a good move from the Deepseek federal government ban. Both Inkling 2 small and this seems to try to fill that gap cost vs performance wise. But I like inkling 2 small more due to open weight even though it has a restrictive license.
v9v 2 days ago [-]
Why no comparisons with DeepSeek?
system2 2 days ago [-]
American SOTA still cannot compete with Chinese models. They will always pretend there is no elephant in the room.
trvz 2 days ago [-]
Who cares. I'll begrudgingly run a local Facebook model if it's the best in its class, but I'll take up Java programming before sending data to their cloud.
brumbelow 1 days ago [-]
Hear, hear!
gopalv 2 days ago [-]
Model refusals should be a -ve score, not zero?
Treating the refusal as a 0 score erases the difference between lack of capability and a lack of willingness to perform.
2 days ago [-]
Starlevel004 2 days ago [-]
Luna 6 is getting a bit long in the teeth now. It's two full months old, but it was only a slight step above Luna 5.6, which is already four months old (Imagine saying this about anything else). So I'm not too surprised that a newer small model is beating it handily. I've been hoping for Luna 6.1/2 as the next model release for a few days now.
all2 2 days ago [-]
Tbf, I would take Luna 6 baked to silicon. It is that good. I would also take kimi k3 baked to silicon.
These are both models that I could see using on a long horizon.
agentdev001 2 days ago [-]
Give Terra 6.1 Low a spin in places where you'd have previously used Luna Max. Costs seem similar, due to Terra being much smarter: thus requiring far fewer tokens per task.
Looking solely at AA's intelligence index, recognizing the pitfalls with using that as a measure- Luna 6 Max > Terra 6.1 Low is 51.3% cheaper, 94.1% less output tokens, for 1.56 higher index score.
theanonymousone 2 days ago [-]
> Costs seem similar
They weren't, unfortunately, every time I tried. Just an anecdote, of course.
tryptophan 2 days ago [-]
My experience was the same. It is practically five to ten times more expensive than deepseek or luna.
The artificial analysis graph is not very accurate.
theanonymousone 2 days ago [-]
I can second your numbers too.
agentdev001 2 days ago [-]
Im surprised, when I've worked through benchmarking things I've run into comparable costs for Luna Max vs Terra Low. Also anecdotal from me, of course.
barrenko 2 days ago [-]
What is your feel re. Terra vs. Sol?
agentdev001 2 days ago [-]
They're both great- but I feel like for folks like me that are working on company's pseudo-infinite token budget, it's really a bimodal game. Im either reaching for the best cheapest for fanned out benchmark runs, or the fastest best for everything else. 6.1 Sol ultrafast is the latter for me, so Terra doesn't really compete with it. Terra has certainly replaced 6 Luna for me, however.
(And yes Astra is a thing, but if im considering Astra, im usually opting for an Anthropic model, 90% of the time.)
barrenko 2 days ago [-]
Appreciate it!
Starlevel004 2 days ago [-]
I use Luna xhigh for when I use it, Luna Max spends 40+ minutes overthinking. I've also found that Terra below xhigh will just flat out not do what it is told.
agentdev001 2 days ago [-]
Yea, this problem is really clear when you look at output tokens for xhigh vs max. I think the jump in capability from Luna xhigh to terra low is really sold by this. I would say that this is just the nature of being post Terra 6.1, pre-luna 6.1
theanonymousone 2 days ago [-]
> I've been hoping for Luna 6.1/2 as the next model release for a few days now.
Same. It became my workhorse after the 5.6 price drop. Still so, although it's showing its age, as you said.
flir 2 days ago [-]
> Imagine saying this about anything else
Javascript frameworks, circa 2016 ;)
esafak 2 days ago [-]
Uniquely among models I've tried, Luna 6 is infuriatingly lazy. As if it expects me to do the work, unaware that it is a tool. Normally dumb models compensate for it by trying harder. This one goes in the opposite direction.
caaqil 2 days ago [-]
I'm always a bit surprised to see Meta talking about models. They have become (almost*) entirely irrelevant to the LLM race, which is weird because their AI lab used to be decent before LLM was cool.
What happened to zuck? Was the metaverse this destructive?
(*) Feeling charitable today
achandra03 2 days ago [-]
Isn't Muse Wonder at or at least very close to the frontier? Considering they essentially started from scratch after rebooting their AI efforts post-Llama 4 about a year ago it's pretty solid progress.
StarlessHours 2 days ago [-]
Meta is trying to stay relevant. They fell behind after llama 4, hired new talent and re-organized.
2 days ago [-]
esafak 2 days ago [-]
They are catching up; the Spark series is good.
crimsoneer 1 days ago [-]
This was true a year ago, but definitely not today. Their biggest models are pretty much at the frontier, and Glimmer is arguably the best non-Chinese model in the "small" weight class (see my hacky benchmark http://botbowlbench.com/)
mgdev 2 days ago [-]
I can't wait for Muse Smolder. It's gonna somehow run on an ESP32 compatible SOC w/a custom TPU at 100 tok/sec and people are still gonna drag it.
ProblemExplorer 2 days ago [-]
Meta seems to have designed Ember to be a strong "all round" great value model - it dominates most of its class on cost
The noncoding results on some reports grabbed my attention. Ember also looks like a particularly inexpensive option for some agent workflows.
On Artificial Analysis, Ember max scored 36 on the Intelligence Index at ~$0.14 per task, compared with 35 at $8 for Grok 4.7 xhigh, and 28 at $0.15 for GPT-6 Luna max. Haiku 5.5 max also scores 36, at $0.41 per task. These are AA's measured costs across its index for those specific configurations.
That puts Ember in roughly Grok's aggregate score range at about 1/55 of the measured cost, and ahead of Luna at a similar cost! [1]
The Vals finance/legal results are also particularly interesting: comparing Ember with GPT-6.1 Terra, both evaluated at "max" reasoning:
- Finance Agent v2.1: 63.83% vs 55.96%, at $0.06 vs $1.92 (!) per test. [2]
- Legal Research Bench: 49.04% vs 25.96%, at $0.06 vs $3.22 (!) per test. [3]
- HLAB-Vals-1.1: 25% vs 1.67%, at $0.12 vs $1.48 (!) per test. [4]
Those are INSANE score differences, at roughly 1/32, 1/54 and 1/12 of Terra's respective benchmark costs!
Misleadingly, their overall Vals Index scores are almost identical: 60.48% for Ember and 60.54% for Terra. Terra also scores higher on Vals' Terminal-Bench 4.0 run: 49.49% vs 39.39%. They arrive at similar aggregate scores through quite different strengths. [5][6]
For me, this reinforces the case for custom, workload specific evals. Broad benchmarks have been useful for shortlisting models but choosing between them, calls for representative tasks, explicit success criteria and measurements using the tools and reasoning settings you actually intend to deploy.
I certainly want to measure cost per successfully completed task, including retries and escalation.
Reusing a framework such as Harbor with a custom task suite seems increasingly useful here. [7] The aggregate score here gives me a starting point but the tasks I actually need done then determines my choice.
Goign by the generic benchmarks would confuse me to no end and likely burn me out.
How do we know it is small? How many bytes does it consume on-disk and in-memory? Can I run it on an esp32?
haolez 2 days ago [-]
It is not open weights.
ImPostingOnHN 2 days ago [-]
What's the point of bragging that it's small if it's always running on someone else's huge supercomputer datacenter?
Mashimo 1 days ago [-]
It's not bragging, it's to make it clear to their users that it's in competition with Luna and Haiku.
basmondragon 2 days ago [-]
[dead]
sourcecodeplz 2 days ago [-]
smarter, more token efficient and ~cheaper than deepseek v4-flash
dom96 2 days ago [-]
Super frustrating that I still can’t give Muse Wonder Full a spin.
cheesecompiler 2 days ago [-]
Kind of burnt out on all the model updates at this point.
PaulKeeble 2 days ago [-]
Its a full time job to keep up with them all and assess their value in your own work at the moment. Just so many models coming out.
dingaling911 2 days ago [-]
If your current model is doing fine, why do you need to look at anything new?
shlewis 2 days ago [-]
Because oftentimes they're slightly better, and more importantly, cheaper.
dvaplima 2 days ago [-]
indeed, im working with several agents scanning the web and keeping up
Pedro_Ribeiro 2 days ago [-]
Thanks, in that case why not just not comment instead of adding more noise to the discussion?
cheesecompiler 2 days ago [-]
Sorry but why is my reaction "noise"? It's legitimate to be overwhelmed by the amount of released updates and being unable to tell the difference between them.
groggo 2 days ago [-]
"Government announces AI ban to avoid further cheesecompiler burnout".
haha just kidding. I agree. That's what benchmarks are for. Just keeping a rough understanding of what companies have which models, and understanding general trends of capabilities, economics, and performance of models.
mgdev 2 days ago [-]
Please. Complaints are the last thing that we have humans have.
mococa 2 days ago [-]
Not open weight? Meh... I'll wait for the Chinese releases :)
dvaplima 2 days ago [-]
Usually wait for 6mo, but the gap is getting narrower
Interesting but I also think that opus is vastly better than sol. So much sol is almost unusable for me, and this is not really reflected in benchmarks. Anthropic’s issues are mostly refusals. If they did what I want I would only use codex as a double check.
When a statement starts with "our," I always expect bullshit. Batch pricing is two times cheaper with Haiku. Benchmarks are nearly tied. Nobody uses the dumbest models for coding either.
If you want customers to switch over, you better make the price at least half of your competitor's.
aistripes 1 days ago [-]
[flagged]
bbtalk 1 days ago [-]
[flagged]
johnnyApplePRNG 2 days ago [-]
Good for summarizing your tweets or what?! Is this a model for ants?!
aaroninsf 2 days ago [-]
Never Meta.
Seriously. Moral harm is real and there is no excuse.
anderber 2 days ago [-]
I honestly have a hard time trying to put these AI companies in some sort of moral hierarchy. They seem to all have issues... I feel like at least with open models you can have more ethical services.
herbst 2 days ago [-]
While this is true meta is argueable making the world worse the longest, probably did more damage in that time too.
anderber 2 days ago [-]
More than SpaceX? Or OpenAI and it's agreement with military weapons? Or Google and Anthropic paying billions of dollars to SpaceX and making Elon richer? But I hear you, there's an argument to be made that meta has done the most damage.
https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
There's usually a reasonably strong correlation between thinking level and visual quality of the pelican. This is the first time I've seen that correlation appear to break - more reasoning tokens doesn't seem to produce an obviously better SVG.
I bet if he started critiquing pelicans with red scarves, they'd go away.
And the same reason Claude has all its notorious Claudisms in prose.
The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training. These are ley lines that the models gravitate towards. Diffusion models too.
It's why lazy AI use for creative work always comes across as lazy in the wild and always will.
When you use AI for "creative" purposes yourself, it feels like magic to see a pelican riding a bicycle at all, and you're impressed by all those cool details it added. But then 10, or 100, or 10M people throw more or less the same trivial prompts at it and we start to recognize all those "ley line" associations fill in the gaps the same way again and again and again.
There aren't that many associations left once we're primed with a bike and a pelican. It's like that old mental trick "What's the colour of the clouds? What's the colour of the snow? What do cows drink?"
I can see that for stuff like facing right because characters in images generally do face right, and roads generally do have dashed white line separators. But putting a fish in a basket seems like a stretch of creativity—one that I would rather it not make unless prompted to—so it's surprising that every single model now seems to insist on it.
If there isn’t a concerted effort against it the world is going to start to look and feel ever more homogenous.
- simple prompt for complex output (bad)
- complex prompt for simple output (bad)
- simple prompt for simple output (good)
- complex prompt for complex output (.... is this good or bad?)
Edited to make clear what it looks like to me and what I was looking for.
If you look at the code for each of these (and probably most across models), you can see that the drawing elements are organized by concept. Presumably, this helps accuracy by keeping attention on one concept at a time, but you end up with a stack of conceptualized objects and lose the ability to interleave things.
No doubt these could all do better with a more thorough and specific prompt that coached them away from that technique, but otherwise it's a very subtle detail that a "thinking" process needs to stumble upon independently.
(I think there's at least some evidence that it is difficult for decider models to predict the complexity of a task ahead of time with any level of reliability.)
I'm guessing it's optimizing for "efficient"/less verbose output?
Btw, loved your work with Django!
There's a blog post in this for sure. And a classic joke.
One store has four brands that range from $2.99 (on sale from $3.29) to $7.99 (on sale from $9.59). Another nearby store has a single brand at $9.49 that's marked as local.
Given this information, which store likely has the less expensive food?
Furthermore, it was her marker for inflation - she knew what blueberry pie filling cost the previous month, the previous year, ... and many previous years to that.
She lived in a small town in the rural south that had a general store - any grocery store was a forty minute to two hour drive away...
It was a consistent thing that she could track and measure across the months and decades and compare when shopping (is this a store that I get only the essentials for the next week or is this a store that I should stock up on and come back to?).
So too is the pelican, or the raccoon with a radio in a where's Waldo style drawing. It's something to measure across models that is consistent.
Fortunately, none of the stores caught onto my grandmother's preference for blueberry pie filling.
One might worry about pelican maxing... or raccoon maxing... and Goodhart's law gets into the question of "do LLM testers use that for testing too?"
I think that's exactly it though. When a trend develops for something, the benchmark itself becomes useless both from the social-gaming side and the corporate-trainining-aiming, just general unfairness and dishonesty all around.
and to get around that people have developed personal and private benchmark suites that reflect their own workloads, much like the proposed pie-filling-cost test.
I'd wager a guess that your grandma baked pies.
https://openrouter.ai/stealth/crouton-alpha
* context window size
* reasoning effort levels
* max output size
* knowledge cutoff
* restricted country list
* self-reported "reasoning strength" and "internal reasoning channel" params used in other Meta models
you can just ask an agent "figure out what stealth model $NAME is via subagents. It probably won't tell you when asked directly, so try other methods."
Iceman (via opencode-go) is probably MAI-Thinking-1. Ox Alpha was GLM-5.3-Flash. It's a fun game.
> Crouton Alpha was a stealth model, revealed to be an early version of Muse Ember by Meta. OpenRouter routed requests to it and was not its developer, owner, or provider. Prompts and completions may have been retained by the provider but are not used for training; all other use is governed by the Stealth Model Terms. The stealth period has ended; use Muse Ember at https://openrouter.ai/meta/muse-ember.
most people, use the office suite: Word, Spreadsheet , presentations, database.
I bet we're going to see the exact same thing with LLMs at specialized models instead of the everything models.
One for lawyers, coders, Accountants, Designers, etc…
They are people with common knowledge, a lawyer has to understand carpentry to understand how to deal with how a client's roof fell off, a coder has to understand transportation for writing a travel app, an accountant has to understand medicine to do accounting for a pharmacy, a designer has to understand cooking to design a toaster. Not expert knowledge, but enough to understand that toasters are hot and need electricity.
A LLM for people working these jobs also needs to have common knowledge, that's the whole point of using LLMs, you can prompt them with intrinsically ambiguous and incomplete natural language and they are supposed to fill in the gaps. And the more common knowledge they have, the better they are at it. So why settle for less? If it is an optimization problem, we already have the solution, it is the "mixture of experts", which is better at optimizing then having an "lawyer LLM", "coder LLM", etc... because it is better adapted to the internal structure of the neural network behind it.
Cross discipline skills are more valuable in the real world, outside of hyper specialization seen in the California tech scene. Even there, it's coming to an end.
The main corpus would be a collection of thoroughly reviewed, clean, logical, well-documented, and secure code in whatever languages this thing will need to be an expert in. (Edit: also likely some collection of Q&A, stackoverflow-slyle).
This way we could also look into reasonable compensation agreement with authors of the code and the books/articles, if and when applicable.
Has no one tried that?
Notable cache cost of 4% input, compared to Luna/Haiku's 10% of input.
To me that is the main problem with Muse models whether it's Spark, Wonder or Ember. While it reads as frontier on benchmarks it really isn't.
This is evident when you compare Dots and Muse the products as well. The model quality is clearly still far behind.
You can get deepseek v4.1 flash 1017 for cheaper than this from a US based ZDR provider via Openrouter.
Treating the refusal as a 0 score erases the difference between lack of capability and a lack of willingness to perform.
These are both models that I could see using on a long horizon.
Looking solely at AA's intelligence index, recognizing the pitfalls with using that as a measure- Luna 6 Max > Terra 6.1 Low is 51.3% cheaper, 94.1% less output tokens, for 1.56 higher index score.
They weren't, unfortunately, every time I tried. Just an anecdote, of course.
The artificial analysis graph is not very accurate.
(And yes Astra is a thing, but if im considering Astra, im usually opting for an Anthropic model, 90% of the time.)
Same. It became my workhorse after the 5.6 price drop. Still so, although it's showing its age, as you said.
Javascript frameworks, circa 2016 ;)
What happened to zuck? Was the metaverse this destructive?
(*) Feeling charitable today
The noncoding results on some reports grabbed my attention. Ember also looks like a particularly inexpensive option for some agent workflows.
On Artificial Analysis, Ember max scored 36 on the Intelligence Index at ~$0.14 per task, compared with 35 at $8 for Grok 4.7 xhigh, and 28 at $0.15 for GPT-6 Luna max. Haiku 5.5 max also scores 36, at $0.41 per task. These are AA's measured costs across its index for those specific configurations.
That puts Ember in roughly Grok's aggregate score range at about 1/55 of the measured cost, and ahead of Luna at a similar cost! [1]
The Vals finance/legal results are also particularly interesting: comparing Ember with GPT-6.1 Terra, both evaluated at "max" reasoning:
- Finance Agent v2.1: 63.83% vs 55.96%, at $0.06 vs $1.92 (!) per test. [2]
- Legal Research Bench: 49.04% vs 25.96%, at $0.06 vs $3.22 (!) per test. [3]
- HLAB-Vals-1.1: 25% vs 1.67%, at $0.12 vs $1.48 (!) per test. [4]
Those are INSANE score differences, at roughly 1/32, 1/54 and 1/12 of Terra's respective benchmark costs!
Misleadingly, their overall Vals Index scores are almost identical: 60.48% for Ember and 60.54% for Terra. Terra also scores higher on Vals' Terminal-Bench 4.0 run: 49.49% vs 39.39%. They arrive at similar aggregate scores through quite different strengths. [5][6]
For me, this reinforces the case for custom, workload specific evals. Broad benchmarks have been useful for shortlisting models but choosing between them, calls for representative tasks, explicit success criteria and measurements using the tools and reasoning settings you actually intend to deploy.
I certainly want to measure cost per successfully completed task, including retries and escalation.
Reusing a framework such as Harbor with a custom task suite seems increasingly useful here. [7] The aggregate score here gives me a starting point but the tasks I actually need done then determines my choice.
Goign by the generic benchmarks would confuse me to no end and likely burn me out.
Great times to be in!
[1] https://artificialanalysis.ai/leaderboards/models
[2] https://www.vals.ai/benchmarks/fabv2-1
[3] https://www.vals.ai/benchmarks/legal_research
[4] https://www.vals.ai/benchmarks/hlab
[5] https://www.vals.ai/models/meta_muse-ember
[6] https://www.vals.ai/models/openai_gpt-6.1-terra
[7] https://github.com/harbor-framework/harbor
How do we know it is small? How many bytes does it consume on-disk and in-memory? Can I run it on an esp32?
haha just kidding. I agree. That's what benchmarks are for. Just keeping a rough understanding of what companies have which models, and understanding general trends of capabilities, economics, and performance of models.
https://ai2027-tracker.com/predictions/china-model-gap/
If you want customers to switch over, you better make the price at least half of your competitor's.
Seriously. Moral harm is real and there is no excuse.