China has caught up is the main takeaway here. The SOTA models are so close that it's really hard to compare them intelligence wise - you have to get a feel for them yourself and what works for you.
What I'm really excited for is the 27B model. 3.6 is still the king of local, and if 3.8 makes the same improvements it could really legitimately make local viable as a default. I'd love to run a perpetual agent on 3.8 that's locally driven.
At this point I feel like the only factor differentiating SOTA models now is who they’re propagandizing you on behalf of (not considering agentic tooling/state management, etc).
I clicked through and it showed Qwen at the top at 55.4 compared to 55.3 for Opus Max. I have a screenshot.
Then I clicked away and back, and now it goes Qwen second, with 58.4, to Opus Max at top with 59.2.
I have screenshots of both. The description above the chart is the same in boh cases:
> Artificial Analysis Agentic Index
> Represents the weighted average of agentic capabilities benchmarks in the Artificial Analysis Intelligence Index (GDPval-AA v2, ³-Banking)
What happened? How can the scores change so much in a few seconds?
All these "intelligence" benchmarks miss something extremely important when using an LLM in a code-agent harness: How it communicates with you about what it did.
Opus-5 is practically unusable (for complex tasks) in this sense - its updates are voluminous, and dense with cryptic language (there are numerous reddit threads complaining about this, so it's not just me). I often have to ask it to re-state concisely in plain terms.
For a fairly gnarly task, after fighting with with Claude-Code + Opus-5, I ported my session to Codex + GPT-5.6-sol, and it was like a breath of fresh air.
Arguably a key aspect of intelligence is concise, clear communication, and current benchmarks miss that, at least as far as I'm aware. I would think some arena-type benchmarks where humans rate responses would measure this, though I'm not sure which those are.
No the models are just ass at communication without being directed.
Try asking them to make useful diagrams for some stuff in a codebase, out of the box without excessive hand holding they don't make good choices about what's worth communicating and how to do it.
You see this in their pointless frontend copy all the time too.
"There is a view in some philosophical circles that anything that can be understood by people who have not studied philosophy is not profound enough to be worth saying. To the contrary, I suspect that whatever cannot be said clearly is probably not being thought clearly either."
Damn I thought it was my extra instructions, I swear everything it writes is in some shorthand with direct references to variables that literally nobody could figure out unless you literally just wrote that code 5 minutes ago. I had it stop writing comments altogether cause it was always four lines of complete and utter nonsense, and it doesn't even obey that rule half the time. Despite doing an extensive back and forth to make a complete plan, 5 seconds into the implementation it changes its mind and makes another assumption, adding some extra thing that tends to break the entire approach and needs follow-ups to repair or cleanup. Instruction following is basically non-existent compared to Fable, it just does whatever the fuck it wants.
I believe it. It's extremely good at troubleshooting. I gave Qwen and Kimi K3 the same annoying, complicated, intermittent bug to track down. Kimi did a bit better in understanding the existing code, but Qwen built some diagnostic tools and did an excellent statistical analysis on the log data. Qwen got way closer to the truth.
I'm very much looking forward to their forthcoming smaller model Qwen 3.8 releases. A version that can easily run locally would be great.
I use https://pi.dev/ which works fine out of the box but is fairly minimal and intended to be customized. There are many extensions.
OpenCode or oh-my-pi might make more sense if you just want a batteries-included agent. You can also make Claude Code work with other models without too much work, but I think that's asking for headaches.
I used claude with GLM and it's easy to set up, just hard to find the documentation. No headaches really, unless you want to use it against multiple different APIs.
I'd certainly rank it at the very top of the want to kill yourself when using it benchmark. It outperforms everything else on that leaderboard.
With weaker models you can sort of understand, they're trying their best and failing, but this thing just channels its immense inteligence into being as annoying as possible instead. I know it can do what I'm asking it to do, but it just finds a way to weasel out of it, or maybe just thinks for 10 minutes instead, then fixes one thing and breaks four additional ones.
Is there any model that knows how to smooth an overly literary text over? I find Opus and Fable constantly decorate the documentation they write like a damn 19/20th century writer. We're working with IT stuff yet it writes like it's going to win some Pulitzer prize. It's that one thing I don't get why they can't train them to do properly: I have not encountered a model yet that sticks to the current language of the domain it's tasked with.
I'm dumbfounded to see Opus 5 making SO MANY mistakes in coding simple stuff. Most times, Fable 5 comes out to be cheaper because it nails so many things much quicker than Opus 5.
I have Fable plan and Opus implement. I haven't had any major issues working this way; however, Opus does seem plain fucking stupid compared to what I experienced with Sonnet previously.
I do the same, and generally have good results, but it does stupid things with gusto.
I'd open a blog with "weird things Opus did". Today it launched a swarm of cpu-hogging processes to test if the widget showing machine and I/O load is rendering nicely and correctly. The test went fine, but it was no longer able to kill those processes since they were really effectively hogging the CPU in various ways - being diligent, some of them were hogging CPU, some were murdering the SSD, some were pounding on the network adapters. Took me 30 mins to recover the machine to a working state without killing the meaningful, messy, in-flight sessions i had going on on other projects.
Weird how different people's experiences are. If it's making simple mistakes something must be wrong in your setup/context I assume? It's been solid for me, beyond the usual LLMisms that all models have. But I keep context pretty minimal.
Statements like this typically come from working on the same setup and context using different models. I actually have that very experience now; I work on something security-adjacent so Fable often drops out, at which point Opus behaves like its lobotomized half-sibling. Pardon me the language, but I can't find a better example to be honest.
Agreed. Opus 5 is doing just fine, slightly better than 4.8. It's personality is insufferable, but I find myself catching fewer problems at code review. It generally understands my conventions and isn't so eager to accrue tech debt.
Ironically, Opus 5 is the most benchmaxxed model I've seen from Anthropic. It is legitimately smart in a lot of ways but it has communication issues, both in terms of how it communicates (all the autism of GPT class models, without the brevity) and how well it catches all the nuance of what you tell it.
This is driving me crazy. opus 4.8 did not do this to me not (at least during pre-5.0 timeframe). Feels like the new cycle is one step forward, two steps back.
Yeah, I've dropped back to 4.8 entirely for the remainder of this billing cycle. I'm going to be seriously looking into Qwen adoption and harness migration options over the course of August.
It's my daily driver. I like it and find it noticeably better than Opus 4.8.
After I started reading complaints about Opus 5, I gave Fable the task of evaluating a bunch of code Opus 4.8 had written and compare it to Opus 5's code. Fable ran a dynamic workflow and the scores came back 15-20% higher for Opus 5's code in terms of quality, correctness and readability/conciseness. I did not tell Fable which Opus wrote which code, and I turned off memory as well to ensure there was no pollution from that angle.
My only complaint is that Opus 5's prose is annoying as hell. I wrote a custom skill for it for concise debriefs and it has been working pretty well for me.
I have some internal tests I use for areas where one particular solution/paradigm is dominant but worse.
Opus 4.6 is the last model that's actually useful and can "adjust" its perspective to use the newer & better solution.
Where Opus 4.8-5 has over fit training on worse/older but "dominant" solutions it refuses to adjust.
Not only does this create an existential threat to adopting progress but it also means that if you have a code base that has rare but real world tradeoff the newest versions of Opus 4.7, 4.8 and 5 are worse than useless and become a major dev timesink.
"As you requested, I've finished task X. Honestly, task X turned out to require task Y, which I haven't actually done. Task Y is the next step if you'd like to continue along this route."
They didn't run all benchmarks. It's the best in AA agentic index (GDPval-AA v2, ³-Banking) but not coding index (DeepSWE which is missing, Terminal-Bench v2.1 they have 81% vs 90% for Sol, SWE-Atlas-QnA missing).
Does "artificial analysis" mean what it says? Dubious.
But: I've been very impressed by the larger Qwen Models, and a brief try of Kimi also impressed me.
A lingering sense of quality degradation when going deep remains.
But that's not an accusation: they seem to be hitting the compute/quality tradeoff extremely well.
And on-prem capability is simply irreplaceable.
Apart from all the innovations that were driven by the strive for this optimization: quantization, "distilling" (without obvious mad-cows-disease)... I think China was an invaluable player in this progress. Intuitively, I'd even go so far to speculate that LLaMa wouldn't exist without the competition.
> Artificial Analysis Agentic Index: Represents the weighted average of agentic capabilities benchmarks in the Artificial Analysis Intelligence Index (GDPval-AA v2, Tau³-Banking)
> Artificial Analysis Coding Agent Index v1.3 incorporates 3 benchmarks: DeepSWE, Terminal-Bench v2, and SWE-Atlas-QnA
Qwen3.8 Max is 55.4 on the Agentic Index but hasn't been tested for the Coding Agent Index.
Looks like coding agent is model+harness. There are far fewer models represented on that page. I believe "agentic index" is still the metric to look at for coding performance. I could be wrong about that though.
Anthropic is a bit nuts, I had $260 of credits on my max account for the extra usage the other night. It was expiring, so I figured I'll fire up an agentic swarm to deep dive and make some deep changes to some old cold bases.. literally 25 minutes or less, $260 burnt, it didn't get get into the implementation, just wrote a ton of useless plans for the most part. It really opened my eyes to what they expect to charge people.. wayyyy overpriced.
Hint: The new models are really good at burning tokens.
I've had to use it a bit for work, and it's been remarkable watching the degradation in performance with the default suggested current models (Opus 5 as a prime example) vs the models that got them huge attention a year ago (Opus 4.6)
If you give 4.6 a spec, or existing code to implement a feature in, it will ask some pointed questions if there's something unclear in the spec, and then produce a plan and move to implement it.
5 will freak out at even a basic task, ask itself if it's own assumptions or your instructions are correct, proceed to re-assess it's own plan, and it's instructions 3-4 times, and then maybe produce code after burning several hundred thousand tokens (and quite a bit of time) analyzing existing code and thoroughly sweeping it for irrelevant problems both to the task it was given and the spec it came up with.
It's quite bizarre to me how well advertised the benchmarks and anecdotes from people one shotting MVP browser games are, compared to the experience of everyone I know that's had to actually use it to accomplish even a relatively basic task.
I managed to lose around $300 in credits I had saved for some emergency /fast sessions the following way: switch to Fable. Work on the design. Downgrade to Opus for the build. If any of other parallel Opus session has /fast enabled it seems to enable it for the newly spawned session by default. Before I knew it, the $300 was gone. I think the bug is now solved, but it was rather unpleasant. I dont ever remember bugs that would drain my wallet - with claude code its just another Tuesday. Still love it.
Claude code is just pool quality. They don't make how this thing will behave clear to the user, or give control. They fail at anything that needs an abstraction or model, not just APIs and shell scripts glued together. And "just ask AI" seems to be the default fix.
That vibe coding they brag about as if it was a good thing, it shows.
Take their notation for describing permissions. The docs are not comprehensive, and in practice it doesn't quite work how they describe it.
Or their management of sub-agents. I once lost a sub-agent, it finished and disappeared from UI. Apparently, you can't bring it back yourself: you have to ask the parent agent to do it for you. But the parent was Fable, and I ran out of credits, so I was locked out of using my opus sub-agent because of it.
Or an even more grotesque example: when you paste your claude API token to authorize, it covers characters with *. But it seems like an LLM has hallucinated a limit of API key length and the tail of your key stays visible.
What amazes me is how, for a vibe coded product where all they have to do is use their AI to fix things ... NOTHING EVER GETS FIXED!
I've probably gone to file 20 bugs. In all 20 cases there wasn't just one issue already filed for it: there were several, each which had a bunch of upvotes. And in all 20 cases ... every. last. one. ... Anthropic closed the ticket with no comment.
IF YOU ARE GOING TO HAVE A SHITTY VIBE CODED PRODUCT, AT LEAST USE YOUR SHITTY AI TO FIX THE SHITTY PROBLEMS!
I suspect it must depend on how one manages their codebase - wrt to docs, ADRs, and general guardrails.
For me it is not great for design work - Fable is way better, and 4.8 was conservative and thus better (Opus 5 seems to jump to conclusions far more eagerly). But for overnight builds, where I give it 8hrs worth of work on LLDs created by Fable - its great. Where Opus 4.8 would often lose the plot and stop for questions clearly answered in the LLD - Opus 5 does manage to complete. Since it launched, I don't remember it ever disappointing me with builds. But designs? Boy, is this thing explosively stupid sometimes.
API pricing is almost definitely profitable, but at this point I assume it's a small minority of their inference traffic compared to subscription usage, and unlikely to make up for the rest of their expenses on its own.
Meanwhile I can do all that and more with reasonix harness for Deepseek with a cache hit rate of 99%. And that's with unsubsidized American providers like cloudflare or Digital Ocean
I think profitability is a matter of accounting. Inference is where money is made, but training is where money is spent. We keep getting new models every few months, but frankly the old models are still quite usable. I suspect labs will soon start specializing in expert models per use case so they can increase the lifespan of individual models, and change the profitability per model.
That's not the only reason to go to expert models. The more different domains you try to stuff in there, the more parameters the model needs to keep things coherent and not overload tokens in a way that induces errors. For example, if a model trained only on biology text sees "sonic hedgehog" there's no ambiguity, and this compounds for all the things that are "overloaded," in the training corpus, which turns out to be quite a bit.
People keep saying this but from what we’ve seen, Anthropic models are marginally profitable and earn back their costs over their lifetime. The company is burning money building the next versions and other ventures (e.g. verticals), but the models themselves have been profitable.
They have something like 80% gross margins, are at a $100B/yr ARR, and are growing at 10x per year... If that keeps up, they're going to be doing more revenue than Google in a year ($400B ARR, 20% per year growth)
How can you sanely project the last 12 months forward? We have seen a huge uptick in usage. Last summer AI was a toy to most devs, now every enterprise developer I talked to uses it every day. Coding agent providers are surely going to hit market saturation in the near future.
Its tough to go from max account at home and pay per usage enterprise account at work with heavy usage limits... but the limits are there because pricing is insane. Feel like I'm in the $5 Uber rides phase at home.
Amazon's first principle is the Customer Obsession. Making customers happy.
Fun bit is that the human psychology rates personal looking fixes better than having no issues at all.
For example, AWS overcharges you, you contact support, and more or less hassle free they refund or issue credits. The customer feels appreciated, or at least got something "extra" or "special treatment".
Meanwhile, any other (small) cloud. Simple, no weird charges. Even _most_ of network egress is free. But, no reason to call support or feel "extraordinary". Comes out as "meh" against Amazon's "top tier" support model...
Yeah, but now we can hire the Chinese instead for 1/100th the cost. It's an even better deal.
Plus we get to own, keep, run, do whatever with the model. We don't feel trapped. Moreover, it's something we can truly build on top of and own our own destiny.
Anthropic and OpenAI are the new Oracle (Oracle pre-AI; Oracle is even worse now). Expensive, feels like dealing with a lawyer, and not at all open. They just became infinitely less cool than they were a month ago.
The whole of our industry is going to migrate to open weights. We're smart enough to know this is the better deal and technical enough to be able to pull it off.
The only thing that might save these OpenAI and Anthropic in the near-term is an abundance of enterprise contracts negotiated with non-tech companies. They'll soak consulting firms and F500 companies for "AI" integrations.
Hopefully this boils down to the smaller versions they've teased. In my experience, Qwen models are the closest to the "less knowledge, more intelligence" (yes, the two are hugely correlated!) ideal some tool-dependent tasks need. Even the 3.5 2B can be easily prompted to always lean on tools and not jump to false conclusions (although its actual coding skills are abysmal, as you'd expect).
People produce such models by over-RL-ing smaller models on math and coding tasks. I've found the results capable of neither innovative work nor thinking outside the box. They're straight-A students raised by tiger moments who never let them play freely for hours in the dirt.
Perhaps you could say such models are skilled --- but intelligent? Not by my measure.
People and AIs alike need diversity of experience and a broad liberal arts education to see hidden connections between fields and make real advances.
I agree with you to an extent, but you have certainly given me food for thought.
Sticking to LLMs, they seemingly get their intelligence (whatever that really means) from building models rich with knowledge, so you could have a point. But Qwen models seem to be particularly good, even at small model sizes, at maintaining both their own knowledge while acquiescing to and integrating external information in the moment.
Prefill is survivable if you cache well. But what kills me is the context. Qwen 27 needs a ton of room for KV Cache. I guess not an issue on a 128 GB Halo or Spark, but if you are running of consumer/prosumer GPUs it's miserable to be compacting every 120k tokens.
For personal stuff, I use it with AnythingLLM. It replaced any Google search for me. For coding, I run opencode though I have been debating switching to Pi. I would argue it’s at Sonnet 3 level.
This really very much depends on how you are using it, I think. If you intend to leave it to solve long context problems and write whole prototypes, the 27B is going to be much better.
But if you are sort of pair-programming with the model, the speed obviously matters and I think then the 35B is acceptably smart, and when it's wrong it'll be wrong much more quickly. It seems very good on SQL and PHP, and I assume on typical JS and Python.
I would rather work that way, so I hope they do produce a small MoE model.
I interpret @syntaxing as meaning they are looking forward to running Qwen3.8-27B, but are frustrated by prefill times with other models, such as Qwen3.6-27B.
I meant Qwen3.6. Unsloth supposedly has early preview of the model and the VRAM requirement is the same so most people expect similar model size and type.
I've been trying it on several projects and have found it's pretty sloppy. It leaves stuff broken, doesn't reliably write tests to check its own work unless explicitly prompted, misunderstands the assignment, etc.
It is smart and reasonably quick but not reliable.
I've come to the same conclusion over and over with all of the Chinese models that have been claimed to be catching up with OpenAI's and Anthropic's frontier models (Deepseek 4, GLM 5.2, Kimi K3).
At their best, I think they're closing in on Opus and GPT, but they're incredibly inconsistent and the variance in output quality is much higher than the best from any of the Anthropic or OpenAI models from the last few generations. The only way I can describe it is that it feels like a lack of intuition with the models which means I find my self needing to write longer prompts or have more back and forth to get them to do what I want from them.
To give an example, I have a saved prompt that I use as a sanity check on some data I'm storing. It reads about 50 rows from a DB and matches them to the UI and makes sure the data is displaying correctly. I've been using this with GPT 5.5 and now 5.6 for a few months and running it a few times a week with no issue. Sometimes I'll run it multiple times in a single chat if I notice bad data (run it, fix thing, run again, fix another thing).
I recently tried to switch to using Deepseek v4 (first flash and then pro) and while both did the task just fine, both would do things like change the response format from one message to another in the same chat or randomly decide to omit things it didn't think were relevant. At one point I ran the prompt, fixed some bad data, and then said "Okay, I fixed row 7, run {prompt} again" and so it decided to leave row 7 out of the response. A few times the first message would contain a table and then the next run in the same chat would contain the data in a bulleted list.
None of those are major issues and all could be solved with a bit more rigor in my prompting, but for me it makes them harder to work with. Those examples are a bit trivial, I think they're the easiest way for me to illustrate the gaps I see with them.
Why does an open weights model cost nearly the same as GPT5.6? $1.14 vs $1.23 on the cost index. Since you can't presumably run this on your own hardware given the model size and hence gain other things like privacy, I don't see any reason to move away from GPT at this rate.
Many providers will host it and will compete on price. It also can't easily be taken away because one company (or one government) decides they don't want it around any more. People can fine-tune it for particular workloads.
They cherrypicked benchmarks. The ONE weighed benchmark where is beats Opus5 by 0.1 points is what was linked because that's how propaganda works. The Agentic Index that includes the full benchmark suite has it in 5th place.
I stopped paying attention to self-published benchmarks when Apple started using those non-sensical performance graphs with "relative performance" as a vertical axis when announcing a new chip.
It's barely better, and barely cheaper, not really enough to challenge the status quo IMO. Half the price for basically the same performance would be a much stronger value proposition.
Things change radically month to month. Nobody is remotely close to capturing the market or having any kind of stability over time. People move around quite a lot, often to sidegrade within a generation. Just playing fly on the wall with discourse would be enough to tell you all of this, even without the data to back it up.
If anybody has, it's DeepSeek. But, with the promised price hikes, I'm sure that'll change. I'm guessing they're raising prices not because they're not making a profit at those prices, but because they're running into capacity problems and need to slow down until they've got more or risk providing poor service. For now DeepSeek Flash is the best deal going for API usage and its popularity makes sense.
Also, OpenRouter misses most of the usage of the US models, as most people are getting those from the vendor directly via subscriptions.
Not really, because that's not a unique aspect of any of those. It's true of all subscription services (that I'm aware of), as well as all of the free models. The selection bias primarily will be against models which be an outlier in the difference between openrouter users and total users, which is a much harder position to argue for any given company except for maybe Twitter.
You can argue there's a selection bias that openrouter users are less likely to display model loyalty, but it would still be a visible confounding factor if it was a statistically significant behavior. And it's not. Nor is there a visibly meaningful indication that people don't sidegrade between models. With every single data set, you're going to see that. You're also going to see it reflected in discourse, as I mentioned. Fact of the matter is there isn't a status quo in AI any more than there's a status quo in cars.
Why does an open weights model cost nearly the same as GPT5.6? $1.14 vs $1.23 on the cost index.
What cost the most in API. Input, Cached Input, or Output. There you have your answer.
Unfortunately, we have moved so much of the actual intelligence of models towards reasoning, what results in some models getting good scores, but this is because they are dumping a insane amount of reasoning tokens at the problem.
So a mid priced model, with heavy reasoning output, cost the same as a expensive model, with medium reasoning output.
Before the GPT Luna price drop of 80%, you actually had the same price if you used Luna High and Sol Low. With the difference that Sol Low was insane fast, and often way better code.
Do not look at the top score but more what is on the horizontal axis as you go down. Sol Medium is frankly, was the best performance for dollar, until that Luna price drop. I will even argue that despite the higher price, Sol Medium is still way better despite Luna Max being cheaper. Or Opus Low, one of the better values also.
What do you notice? Is that those models all have a high intelligence start point for their low setting. So that means they do not rely as much on output tokens aka thinking.
Qwen Max is their large model - over a trillion params. Similar to Kimi K3 in size. Qwen 3.8 27B is going to be more accessible to your own hardware. I'd say that Qwen Max is not approachable for the majority of people and companies to self-host.
Why should open weights correlate with cost? Cost correlates with the expense of running the model more than it does to the expense of developing the model.
GPT5.6Sol completes the suite in 70M tokens, while Qwen3.8Max needs like 145M tokens. So this is a case where models like Qwen 3.8 and Kimi K3 use a lot more output (reasoning) tokens, go a good bit slower, so they can ultimately achieve a better intelligence score than if they went more quickly.
There are a couple of frontiers (ok bad word, maybe categories) in open weight models.
These Qwen 3.8 and Kimi K3 style models aren't trying to win on price, they're trying to compete on intelligence and capability.
Models like Deepseek V4 Flash (updated this week) are $0.03 a task, or 50X cheaper than Qwen3.8/Kimi K3, and 100X cheaper than Fable, while offering stunning intelligence. That's a different frontier for competition, and perhaps one more interesting for someone who wants to see them compete on cost.
Anthropic have a real fight on their hands now. The competition is no longer 6 months behind, it's 6 days. If this had come out two or three weeks earlier this would be an absolute market leader on both quality and timeline.
Haven't tried this yet, but going to soon! I have to wonder what happened at Anthropic. We've cancelled our subscription in favor of OpenCode & Codex. Sol is just so good & OC goes so far for every $ spent. Claude's become a pain to work with - average output with an annoying personality. Who knew this would be an issue even a year ago? In any case, loving the stuff from the Chinese models!
I was with you until there. Qwen and the OpenAI models are great, aggressive agents, but they’re not as good as the anthropic models for human interaction. They just don’t have the subtlety, understanding, or attention to detail.
Claude 4.5/4.6 - absolutely agree. Fable 5? From my (limited) testing, also reasonable to interact with.
Opus 4.7/4.8/5? Absolutely smug and antagonistic and preachy. I'm constantly fighting with it to stop fighting me and accept that I occasionally know better. It's really frustrating to spend so many tokens of such an expensive model arguing with it.
Really? I've heard so many other people complain about this recently. And maybe it's possible that it's the prompt style even. But interesting that it's not across the board.
Depends on what you want to do. Some task specific models can be trained with a few ten or hundred thousand training examples so you can use a bigger model to produce synthetic training examples and then fine tune a smaller student model. I think that's the usual process. Whether you'd get acceptable performance this way depends, as mentioned, on what you're trying to do and what you'd consider acceptable.
once you are able to get the full probability distributions per token you can distill it on specific domains. distilling without that isn't generally a good idea unless you have invested millions in the requisite infrastructure.
With an unlimited budget, Kimi K3 (which is quite comparable to this Qwen Max imo). With a normal budget/a PC you might already have, probably Qwen 3.6 27B.
DS4 Flash Q2/Q4 mixed quant fits on a DGX Spark (a $4000 device which is not particularly unheard of expense for Apple customers), and is indistinguishable for me from Opus for my personal daily use/assistant benchmarks[0].
Apple is already doing this... they worked with Gemini to distill the model into a smaller one that fits on your phone. If you have iOS 27 Beta, you're already using this
It is also the most expensive open source frontier model, per task; cf. Cost per Intelligence Index Task. If it is as good as the benchmarks indicate -- I'll never know because I see no reason to try it -- it is a positive indicator for Qwen and China.
China has caught up is the main takeaway here. The SOTA models are so close that it's really hard to compare them intelligence wise - you have to get a feel for them yourself and what works for you.
What I'm really excited for is the 27B model. 3.6 is still the king of local, and if 3.8 makes the same improvements it could really legitimately make local viable as a default. I'd love to run a perpetual agent on 3.8 that's locally driven.
I'm still skeptical of the smaller models after the talent exodus a few months ago.
At this point I feel like the only factor differentiating SOTA models now is who they’re propagandizing you on behalf of (not considering agentic tooling/state management, etc).
Qwen 3.6 27b is already a viable default. I'm running it on a single 7900 XTX right now for Go development with pi. It's great.
I clicked through and it showed Qwen at the top at 55.4 compared to 55.3 for Opus Max. I have a screenshot.
Then I clicked away and back, and now it goes Qwen second, with 58.4, to Opus Max at top with 59.2.
I have screenshots of both. The description above the chart is the same in boh cases:
> Artificial Analysis Agentic Index > Represents the weighted average of agentic capabilities benchmarks in the Artificial Analysis Intelligence Index (GDPval-AA v2, ³-Banking)
What happened? How can the scores change so much in a few seconds?
They JUST updated their methodology:
https://artificialanalysis.ai/methodology/intelligence-bench...
In that case they should clearly label that this is a new benchmark.
Fixed the result, eh? In both senses of the word.
Can someone please explain what changed, when it happened, and whether it was surreptitious?
I have been suspicious of these AI leaderboard sites for some time now, and this only increases that suspicion.
What was the change?
They should probably freeze the results before publishing.
Welp. That didn't last long
Same, they just updated it. Hacker news effect?
Opus is still first in Intelligence Index followed by Fable, GPT 5.6, Kimi K3 then Qwen 3.8 max. https://artificialanalysis.ai/#intelligence
Our leaderboard combines Arena ELO, AA Intelligence index, latency and speed and goes: #1 Opus 5 #2 Kimi K3 #3 Qwen3.8 Max #4 GPT 5.6 Sol
Source: http://pellmell.ai/leaderboard.
This jumps around a lot based on the top throughput and latency of whatever provider happens to be best at the moment.
All these "intelligence" benchmarks miss something extremely important when using an LLM in a code-agent harness: How it communicates with you about what it did.
Opus-5 is practically unusable (for complex tasks) in this sense - its updates are voluminous, and dense with cryptic language (there are numerous reddit threads complaining about this, so it's not just me). I often have to ask it to re-state concisely in plain terms.
For a fairly gnarly task, after fighting with with Claude-Code + Opus-5, I ported my session to Codex + GPT-5.6-sol, and it was like a breath of fresh air.
Arguably a key aspect of intelligence is concise, clear communication, and current benchmarks miss that, at least as far as I'm aware. I would think some arena-type benchmarks where humans rate responses would measure this, though I'm not sure which those are.
Is that what it feels like when the models get smarter than us?
No the models are just ass at communication without being directed.
Try asking them to make useful diagrams for some stuff in a codebase, out of the box without excessive hand holding they don't make good choices about what's worth communicating and how to do it.
You see this in their pointless frontend copy all the time too.
"There is a view in some philosophical circles that anything that can be understood by people who have not studied philosophy is not profound enough to be worth saying. To the contrary, I suspect that whatever cannot be said clearly is probably not being thought clearly either."
A smarter model would know how to communicate with you correctly, and not just throw jargon it has just invented at you without explaining it.
But if you have two experts in a field talking to each other you wouldn't expect them to dumb down their communication.
s/model/engineer
Guess that's the exact point of the "intelligence" benchmarks
No, a smart model should also give a concise executive summary, "brevity is the soul of wit".
So, in short, once models get smart enough they stop bothering telling us what they did.
Yeah, makes sense. A parent wouldn't bother explaining the details of their job to a toddler.
Which would be fine, but the parents also just smeared tomato sauce over the walls too, so let’s not get too ahead of ourselves.
GPT 5.6 has similar language quirks that makes its comments nearly unusable.
I wonder if this is a side effect of MoE models — they can write excellent prose, but not simultaneously with writing code.
Damn I thought it was my extra instructions, I swear everything it writes is in some shorthand with direct references to variables that literally nobody could figure out unless you literally just wrote that code 5 minutes ago. I had it stop writing comments altogether cause it was always four lines of complete and utter nonsense, and it doesn't even obey that rule half the time. Despite doing an extensive back and forth to make a complete plan, 5 seconds into the implementation it changes its mind and makes another assumption, adding some extra thing that tends to break the entire approach and needs follow-ups to repair or cleanup. Instruction following is basically non-existent compared to Fable, it just does whatever the fuck it wants.
Everything is load-bearing with 3 measured blockers.
Don’t forget the smoking guns! I think these new models have been reading too many Agatha Christie novels.
I believe it. It's extremely good at troubleshooting. I gave Qwen and Kimi K3 the same annoying, complicated, intermittent bug to track down. Kimi did a bit better in understanding the existing code, but Qwen built some diagnostic tools and did an excellent statistical analysis on the log data. Qwen got way closer to the truth.
I'm very much looking forward to their forthcoming smaller model Qwen 3.8 releases. A version that can easily run locally would be great.
Did you also try Opus 5 and 5.6 Sol?
How CLI are you guys using for qwen and kimi?
I use https://pi.dev/ which works fine out of the box but is fairly minimal and intended to be customized. There are many extensions.
OpenCode or oh-my-pi might make more sense if you just want a batteries-included agent. You can also make Claude Code work with other models without too much work, but I think that's asking for headaches.
I used claude with GLM and it's easy to set up, just hard to find the documentation. No headaches really, unless you want to use it against multiple different APIs.
Is there any subscription of any kind for Qwen? Or via Pi.dev needs to be used with API credits?
My first web search turned up this as the top result https://www.alibabacloud.com/help/en/model-studio/coding-pla...
pi
Any benchmark showing Opus 5 as the best just loses credibility for me. Anyone who's actually used Opus 5 daily knows what I'm talking about.
Agreed, it's extremely frustrating. It's the only model that actually makes me curse when talking to it, even knowing how counterproductive it is.
The cursing thing blows my mind. "User is upset? Let's make decisions even faster (ie. more wrong) because clearly that's what they want!"
It's a simple switch to make: cursing = try harder instead of cursing = stop trying. Is it really impossible to train Claude that way?
I'd certainly rank it at the very top of the want to kill yourself when using it benchmark. It outperforms everything else on that leaderboard.
With weaker models you can sort of understand, they're trying their best and failing, but this thing just channels its immense inteligence into being as annoying as possible instead. I know it can do what I'm asking it to do, but it just finds a way to weasel out of it, or maybe just thinks for 10 minutes instead, then fixes one thing and breaks four additional ones.
yep matches my experience completely
But even fable has the annoying tendency to invent new jargon and produce an incomprehensible soup of text.
Is there any model that knows how to smooth an overly literary text over? I find Opus and Fable constantly decorate the documentation they write like a damn 19/20th century writer. We're working with IT stuff yet it writes like it's going to win some Pulitzer prize. It's that one thing I don't get why they can't train them to do properly: I have not encountered a model yet that sticks to the current language of the domain it's tasked with.
I'm dumbfounded to see Opus 5 making SO MANY mistakes in coding simple stuff. Most times, Fable 5 comes out to be cheaper because it nails so many things much quicker than Opus 5.
I have Fable plan and Opus implement. I haven't had any major issues working this way; however, Opus does seem plain fucking stupid compared to what I experienced with Sonnet previously.
I do the same, and generally have good results, but it does stupid things with gusto.
I'd open a blog with "weird things Opus did". Today it launched a swarm of cpu-hogging processes to test if the widget showing machine and I/O load is rendering nicely and correctly. The test went fine, but it was no longer able to kill those processes since they were really effectively hogging the CPU in various ways - being diligent, some of them were hogging CPU, some were murdering the SSD, some were pounding on the network adapters. Took me 30 mins to recover the machine to a working state without killing the meaningful, messy, in-flight sessions i had going on on other projects.
> however, Opus does seem plain fucking stupid
Infuriatingly so, in a way I don't remember Opus 4.8 being, but maybe I've just been ruined by Fable 5.
I bought my first LLM subscription with Claude right before they gave access to Fable 5.
I got so used to it, when they finally pulled access for me and I had to go back to Opus I felt like I was working with my hands tied.
I finally know what those women with AI boyfriends felt like when their app updated and it won't dirty talk with them anymore.
Fable has spoiled us all.
Weird how different people's experiences are. If it's making simple mistakes something must be wrong in your setup/context I assume? It's been solid for me, beyond the usual LLMisms that all models have. But I keep context pretty minimal.
Statements like this typically come from working on the same setup and context using different models. I actually have that very experience now; I work on something security-adjacent so Fable often drops out, at which point Opus behaves like its lobotomized half-sibling. Pardon me the language, but I can't find a better example to be honest.
Agreed. Opus 5 is doing just fine, slightly better than 4.8. It's personality is insufferable, but I find myself catching fewer problems at code review. It generally understands my conventions and isn't so eager to accrue tech debt.
Ironically, Opus 5 is the most benchmaxxed model I've seen from Anthropic. It is legitimately smart in a lot of ways but it has communication issues, both in terms of how it communicates (all the autism of GPT class models, without the brevity) and how well it catches all the nuance of what you tell it.
Sent to solve one task, came back with half of it solved and 2 more problems.
"One thing worth your attention", "Two things worth knowing", "One thing to eyeball"
This is driving me crazy. opus 4.8 did not do this to me not (at least during pre-5.0 timeframe). Feels like the new cycle is one step forward, two steps back.
Yeah, I've dropped back to 4.8 entirely for the remainder of this billing cycle. I'm going to be seriously looking into Qwen adoption and harness migration options over the course of August.
Same.
I could not get Opus 5 to do anything without losing a few years of my life from stress.
Fable has been okay but I am doing ML work and not allowed to use it which feels insane.
It's my daily driver. I like it and find it noticeably better than Opus 4.8.
After I started reading complaints about Opus 5, I gave Fable the task of evaluating a bunch of code Opus 4.8 had written and compare it to Opus 5's code. Fable ran a dynamic workflow and the scores came back 15-20% higher for Opus 5's code in terms of quality, correctness and readability/conciseness. I did not tell Fable which Opus wrote which code, and I turned off memory as well to ensure there was no pollution from that angle.
My only complaint is that Opus 5's prose is annoying as hell. I wrote a custom skill for it for concise debriefs and it has been working pretty well for me.
It is infuriating to interact with, but it is also first in many blind test leaderboards on LLMArena
Can not confirm, for me it's the complete opposite.
What's the clear best, that you see?
GPT 5.6 Sol
GLM 5.2
Fable 5
I have some internal tests I use for areas where one particular solution/paradigm is dominant but worse.
Opus 4.6 is the last model that's actually useful and can "adjust" its perspective to use the newer & better solution.
Where Opus 4.8-5 has over fit training on worse/older but "dominant" solutions it refuses to adjust.
Not only does this create an existential threat to adopting progress but it also means that if you have a code base that has rare but real world tradeoff the newest versions of Opus 4.7, 4.8 and 5 are worse than useless and become a major dev timesink.
"As you requested, I've finished task X. Honestly, task X turned out to require task Y, which I haven't actually done. Task Y is the next step if you'd like to continue along this route."
Yeah, I told it to save in its memory that I don't want to have any more word salad!
This is the hard-won load-bearing quote.
Belt and braces all the way down
It's an infuriating model
Strange that the page https://artificialanalysis.ai/agents/coding-agents doesn't even mention "Qwen" once if it's now the "best" according to one of their one index?
They didn't run all benchmarks. It's the best in AA agentic index (GDPval-AA v2, ³-Banking) but not coding index (DeepSWE which is missing, Terminal-Bench v2.1 they have 81% vs 90% for Sol, SWE-Atlas-QnA missing).
According to those graphs, Grok 4.5 appears to be the most cost-effective model.
$0.05 per task, Intelligence Index score 52 -> GPT 5.6 Luna max
$0.36 per task, Intelligence Index score 56 -> Grok 4.5 high
$1.13 per task, Intelligence Index score 58 -> Qwen 3.8 Max
$0.81 per task, Intelligence Index score 59 -> GPT 5.6 Sol xhigh
$1.80 per task, Intelligence Index score 63 -> Opus 5 xhigh
Does "artificial analysis" mean what it says? Dubious.
But: I've been very impressed by the larger Qwen Models, and a brief try of Kimi also impressed me.
A lingering sense of quality degradation when going deep remains.
But that's not an accusation: they seem to be hitting the compute/quality tradeoff extremely well.
And on-prem capability is simply irreplaceable.
Apart from all the innovations that were driven by the strive for this optimization: quantization, "distilling" (without obvious mad-cows-disease)... I think China was an invaluable player in this progress. Intuitively, I'd even go so far to speculate that LLaMa wouldn't exist without the competition.
Different benchmarks:
> Artificial Analysis Agentic Index: Represents the weighted average of agentic capabilities benchmarks in the Artificial Analysis Intelligence Index (GDPval-AA v2, Tau³-Banking)
> Artificial Analysis Coding Agent Index v1.3 incorporates 3 benchmarks: DeepSWE, Terminal-Bench v2, and SWE-Atlas-QnA
Qwen3.8 Max is 55.4 on the Agentic Index but hasn't been tested for the Coding Agent Index.
Looks like coding agent is model+harness. There are far fewer models represented on that page. I believe "agentic index" is still the metric to look at for coding performance. I could be wrong about that though.
Indeed, and this Qwen 3.8 max specific page:
https://artificialanalysis.ai/models/qwen3-8-max
Doesn't have the claim either. Clickbait?
This page has it, scroll to "Intelligence" header (not the highlights one, but second on the page / with black square) and click "Agentic Index"
So the original link should be: https://artificialanalysis.ai/models/qwen3-8-max?intelligenc...
Even then, this seems a much more marginal win than the headline suggested to me.
Anthropic is a bit nuts, I had $260 of credits on my max account for the extra usage the other night. It was expiring, so I figured I'll fire up an agentic swarm to deep dive and make some deep changes to some old cold bases.. literally 25 minutes or less, $260 burnt, it didn't get get into the implementation, just wrote a ton of useless plans for the most part. It really opened my eyes to what they expect to charge people.. wayyyy overpriced.
Hint: The new models are really good at burning tokens.
I've had to use it a bit for work, and it's been remarkable watching the degradation in performance with the default suggested current models (Opus 5 as a prime example) vs the models that got them huge attention a year ago (Opus 4.6)
If you give 4.6 a spec, or existing code to implement a feature in, it will ask some pointed questions if there's something unclear in the spec, and then produce a plan and move to implement it.
5 will freak out at even a basic task, ask itself if it's own assumptions or your instructions are correct, proceed to re-assess it's own plan, and it's instructions 3-4 times, and then maybe produce code after burning several hundred thousand tokens (and quite a bit of time) analyzing existing code and thoroughly sweeping it for irrelevant problems both to the task it was given and the spec it came up with.
It's quite bizarre to me how well advertised the benchmarks and anecdotes from people one shotting MVP browser games are, compared to the experience of everyone I know that's had to actually use it to accomplish even a relatively basic task.
I managed to lose around $300 in credits I had saved for some emergency /fast sessions the following way: switch to Fable. Work on the design. Downgrade to Opus for the build. If any of other parallel Opus session has /fast enabled it seems to enable it for the newly spawned session by default. Before I knew it, the $300 was gone. I think the bug is now solved, but it was rather unpleasant. I dont ever remember bugs that would drain my wallet - with claude code its just another Tuesday. Still love it.
Claude code is just pool quality. They don't make how this thing will behave clear to the user, or give control. They fail at anything that needs an abstraction or model, not just APIs and shell scripts glued together. And "just ask AI" seems to be the default fix.
That vibe coding they brag about as if it was a good thing, it shows.
Take their notation for describing permissions. The docs are not comprehensive, and in practice it doesn't quite work how they describe it.
Or their management of sub-agents. I once lost a sub-agent, it finished and disappeared from UI. Apparently, you can't bring it back yourself: you have to ask the parent agent to do it for you. But the parent was Fable, and I ran out of credits, so I was locked out of using my opus sub-agent because of it.
Or an even more grotesque example: when you paste your claude API token to authorize, it covers characters with *. But it seems like an LLM has hallucinated a limit of API key length and the tail of your key stays visible.
What amazes me is how, for a vibe coded product where all they have to do is use their AI to fix things ... NOTHING EVER GETS FIXED!
I've probably gone to file 20 bugs. In all 20 cases there wasn't just one issue already filed for it: there were several, each which had a bunch of upvotes. And in all 20 cases ... every. last. one. ... Anthropic closed the ticket with no comment.
IF YOU ARE GOING TO HAVE A SHITTY VIBE CODED PRODUCT, AT LEAST USE YOUR SHITTY AI TO FIX THE SHITTY PROBLEMS!
so many ridiculous "how the fuck did this get through basic QA?" issues with Claude Code.
I can't believe how many critical bugs fall through.
My favourite one is the bug where Plan mode can execute destructive commands inadvertently.
Then all these get closed with `Closing for now — inactive for too long. Please open a new issue if this is still relevant.`. Awesome.
> I can't believe how many critical bugs fall through.
Almost like CC is 100% vibe coded.
I dont love it.
Opus 5 is just a token burner.
I use fable plan and spawn opus 4.8 workflows which seems to work alright.
I suspect it must depend on how one manages their codebase - wrt to docs, ADRs, and general guardrails.
For me it is not great for design work - Fable is way better, and 4.8 was conservative and thus better (Opus 5 seems to jump to conclusions far more eagerly). But for overnight builds, where I give it 8hrs worth of work on LLDs created by Fable - its great. Where Opus 4.8 would often lose the plot and stop for questions clearly answered in the LLD - Opus 5 does manage to complete. Since it launched, I don't remember it ever disappointing me with builds. But designs? Boy, is this thing explosively stupid sometimes.
Opus 5 loves to stop working "for safety reasons" and shuts down the session! I avoid it at all costs now. Opus 4.8 has been my default as well.
And at that cost they're still not profitable. It's going to be a bumpy road ahead...
I thought they are making a profit on API pricing? A quick Google shows somewhere between 50-70% margins on API inference.
API pricing is almost definitely profitable, but at this point I assume it's a small minority of their inference traffic compared to subscription usage, and unlikely to make up for the rest of their expenses on its own.
Meanwhile I can do all that and more with reasonix harness for Deepseek with a cache hit rate of 99%. And that's with unsubsidized American providers like cloudflare or Digital Ocean
I think profitability is a matter of accounting. Inference is where money is made, but training is where money is spent. We keep getting new models every few months, but frankly the old models are still quite usable. I suspect labs will soon start specializing in expert models per use case so they can increase the lifespan of individual models, and change the profitability per model.
That's not the only reason to go to expert models. The more different domains you try to stuff in there, the more parameters the model needs to keep things coherent and not overload tokens in a way that induces errors. For example, if a model trained only on biology text sees "sonic hedgehog" there's no ambiguity, and this compounds for all the things that are "overloaded," in the training corpus, which turns out to be quite a bit.
People keep saying this but from what we’ve seen, Anthropic models are marginally profitable and earn back their costs over their lifetime. The company is burning money building the next versions and other ventures (e.g. verticals), but the models themselves have been profitable.
They're EBITDA profitable, not GAAP profitable.
What’s the blast radius of this bubble popping? It’s all private investment still right?
Two thirds of most of the DC builds are not compute. So it's a CRE play the last leg holding up that mess.
It’s crazy how different the credit cost and subscription cost are.
With the $200 subscription, I can have Fable on ultracode working for hours and not dent the usage limits.
VCs are footing the bill for that $200 subscription.
They have something like 80% gross margins, are at a $100B/yr ARR, and are growing at 10x per year... If that keeps up, they're going to be doing more revenue than Google in a year ($400B ARR, 20% per year growth)
How can you sanely project the last 12 months forward? We have seen a huge uptick in usage. Last summer AI was a toy to most devs, now every enterprise developer I talked to uses it every day. Coding agent providers are surely going to hit market saturation in the near future.
At last, a valid usecase for VCs.
Its tough to go from max account at home and pay per usage enterprise account at work with heavy usage limits... but the limits are there because pricing is insane. Feel like I'm in the $5 Uber rides phase at home.
The Chinese models are the public transport in the uber analogy. Once the price the goes up catch the bus!
Lol perfect analogy. I'm still paying for claude because the quality is unmatched.
You plugged in a space heater on a roofless house.
There is some element of responsibility on the user to guide and monitor the model/harness and not let it rip to burn tokens.
Anthropic is the new AWS.
Amazon's first principle is the Customer Obsession. Making customers happy.
Fun bit is that the human psychology rates personal looking fixes better than having no issues at all.
For example, AWS overcharges you, you contact support, and more or less hassle free they refund or issue credits. The customer feels appreciated, or at least got something "extra" or "special treatment".
Meanwhile, any other (small) cloud. Simple, no weird charges. Even _most_ of network egress is free. But, no reason to call support or feel "extraordinary". Comes out as "meh" against Amazon's "top tier" support model...
I’ve never gotten a refund from Athropic.
Anthropic's constant changing of its mind leads to instability which leads to unhappy customers
> wayyy overpriced
Maybe they consider that hiring a person to do it would have cost at least as much and taken much more time, so paying them is a bargain.
Yeah, but now we can hire the Chinese instead for 1/100th the cost. It's an even better deal.
Plus we get to own, keep, run, do whatever with the model. We don't feel trapped. Moreover, it's something we can truly build on top of and own our own destiny.
Anthropic and OpenAI are the new Oracle (Oracle pre-AI; Oracle is even worse now). Expensive, feels like dealing with a lawyer, and not at all open. They just became infinitely less cool than they were a month ago.
The whole of our industry is going to migrate to open weights. We're smart enough to know this is the better deal and technical enough to be able to pull it off.
The only thing that might save these OpenAI and Anthropic in the near-term is an abundance of enterprise contracts negotiated with non-tech companies. They'll soak consulting firms and F500 companies for "AI" integrations.
> the new Oracle
I think that's exactly what they are going for - enterprise and government customers.
You should just spend those towards a cursor subscription.
Hopefully this boils down to the smaller versions they've teased. In my experience, Qwen models are the closest to the "less knowledge, more intelligence" (yes, the two are hugely correlated!) ideal some tool-dependent tasks need. Even the 3.5 2B can be easily prompted to always lean on tools and not jump to false conclusions (although its actual coding skills are abysmal, as you'd expect).
> less knowledge, more intelligence
People produce such models by over-RL-ing smaller models on math and coding tasks. I've found the results capable of neither innovative work nor thinking outside the box. They're straight-A students raised by tiger moments who never let them play freely for hours in the dirt.
Perhaps you could say such models are skilled --- but intelligent? Not by my measure.
People and AIs alike need diversity of experience and a broad liberal arts education to see hidden connections between fields and make real advances.
It's amusing to me that AI has become sophisticated enough that people have started being racist to it.
I agree with you to an extent, but you have certainly given me food for thought.
Sticking to LLMs, they seemingly get their intelligence (whatever that really means) from building models rich with knowledge, so you could have a point. But Qwen models seem to be particularly good, even at small model sizes, at maintaining both their own knowledge while acquiescing to and integrating external information in the moment.
I am so excited for Qwen 3.8 27B. It’s a shame how slow prefill (~3-400) is on a strix halo but it’s such a good model for agentic tasks.
Prefill is survivable if you cache well. But what kills me is the context. Qwen 27 needs a ton of room for KV Cache. I guess not an issue on a 128 GB Halo or Spark, but if you are running of consumer/prosumer GPUs it's miserable to be compacting every 120k tokens.
What type of agentic tasks are you using it for (eg how complex)?
For personal stuff, I use it with AnythingLLM. It replaced any Google search for me. For coding, I run opencode though I have been debating switching to Pi. I would argue it’s at Sonnet 3 level.
I find that 35B-A3B is much easier to run on my M4 Max (both prefill and generation)
It's well known 35b is much faster (on any hardware) and quite a bit dumber
This really very much depends on how you are using it, I think. If you intend to leave it to solve long context problems and write whole prototypes, the 27B is going to be much better.
But if you are sort of pair-programming with the model, the speed obviously matters and I think then the 35B is acceptably smart, and when it's wrong it'll be wrong much more quickly. It seems very good on SQL and PHP, and I assume on typical JS and Python.
I would rather work that way, so I hope they do produce a small MoE model.
How are you running it on a Strix Halo? The weights aren't out yet, are they?
I interpret @syntaxing as meaning they are looking forward to running Qwen3.8-27B, but are frustrated by prefill times with other models, such as Qwen3.6-27B.
I meant Qwen3.6. Unsloth supposedly has early preview of the model and the VRAM requirement is the same so most people expect similar model size and type.
I find that surprising.
I've been trying it on several projects and have found it's pretty sloppy. It leaves stuff broken, doesn't reliably write tests to check its own work unless explicitly prompted, misunderstands the assignment, etc.
It is smart and reasonably quick but not reliable.
I've come to the same conclusion over and over with all of the Chinese models that have been claimed to be catching up with OpenAI's and Anthropic's frontier models (Deepseek 4, GLM 5.2, Kimi K3).
At their best, I think they're closing in on Opus and GPT, but they're incredibly inconsistent and the variance in output quality is much higher than the best from any of the Anthropic or OpenAI models from the last few generations. The only way I can describe it is that it feels like a lack of intuition with the models which means I find my self needing to write longer prompts or have more back and forth to get them to do what I want from them.
To give an example, I have a saved prompt that I use as a sanity check on some data I'm storing. It reads about 50 rows from a DB and matches them to the UI and makes sure the data is displaying correctly. I've been using this with GPT 5.5 and now 5.6 for a few months and running it a few times a week with no issue. Sometimes I'll run it multiple times in a single chat if I notice bad data (run it, fix thing, run again, fix another thing).
I recently tried to switch to using Deepseek v4 (first flash and then pro) and while both did the task just fine, both would do things like change the response format from one message to another in the same chat or randomly decide to omit things it didn't think were relevant. At one point I ran the prompt, fixed some bad data, and then said "Okay, I fixed row 7, run {prompt} again" and so it decided to leave row 7 out of the response. A few times the first message would contain a table and then the next run in the same chat would contain the data in a bulleted list.
None of those are major issues and all could be solved with a bit more rigor in my prompting, but for me it makes them harder to work with. Those examples are a bit trivial, I think they're the easiest way for me to illustrate the gaps I see with them.
It’s because they’re doing some sort of combined score of intelligence, speed and cost. On pure intelligence it doesn’t even show up in the top 10.
Why does an open weights model cost nearly the same as GPT5.6? $1.14 vs $1.23 on the cost index. Since you can't presumably run this on your own hardware given the model size and hence gain other things like privacy, I don't see any reason to move away from GPT at this rate.
It's not enough that it's better?
Many providers will host it and will compete on price. It also can't easily be taken away because one company (or one government) decides they don't want it around any more. People can fine-tune it for particular workloads.
They cherrypicked benchmarks. The ONE weighed benchmark where is beats Opus5 by 0.1 points is what was linked because that's how propaganda works. The Agentic Index that includes the full benchmark suite has it in 5th place.
Might as well use gpt-sol.
The whole industry cherry picks benchmarks.
I stopped paying attention to self-published benchmarks when Apple started using those non-sensical performance graphs with "relative performance" as a vertical axis when announcing a new chip.
> It's not enough that it's better?
It's barely better, and barely cheaper, not really enough to challenge the status quo IMO. Half the price for basically the same performance would be a much stronger value proposition.
What status quo? Just look at Openrouter's rankings: https://openrouter.ai/rankings
Things change radically month to month. Nobody is remotely close to capturing the market or having any kind of stability over time. People move around quite a lot, often to sidegrade within a generation. Just playing fly on the wall with discourse would be enough to tell you all of this, even without the data to back it up.
If anybody has, it's DeepSeek. But, with the promised price hikes, I'm sure that'll change. I'm guessing they're raising prices not because they're not making a profit at those prices, but because they're running into capacity problems and need to slow down until they've got more or risk providing poor service. For now DeepSeek Flash is the best deal going for API usage and its popularity makes sense.
Also, OpenRouter misses most of the usage of the US models, as most people are getting those from the vendor directly via subscriptions.
That's got a significant selection bias. Claude and ChatGPT and Gemini and other subs do not go through openrouter.
Not really, because that's not a unique aspect of any of those. It's true of all subscription services (that I'm aware of), as well as all of the free models. The selection bias primarily will be against models which be an outlier in the difference between openrouter users and total users, which is a much harder position to argue for any given company except for maybe Twitter.
You can argue there's a selection bias that openrouter users are less likely to display model loyalty, but it would still be a visible confounding factor if it was a statistically significant behavior. And it's not. Nor is there a visibly meaningful indication that people don't sidegrade between models. With every single data set, you're going to see that. You're also going to see it reflected in discourse, as I mentioned. Fact of the matter is there isn't a status quo in AI any more than there's a status quo in cars.
For one thing, providers of open models can't arbitrarily increase their prices without facing competition.
But given the extremely low cost of switching, why wouldn't you use the cheaper one if they're comparable?
As low as it is, switching between providers on OpenRouter is still lower.
That said, it's a fair point. For me, it boils down to things covered here: https://earendil.com/posts/session-portability/
Things like obscured reasoning traces.
Speed and reliability.
Why does an open weights model cost nearly the same as GPT5.6? $1.14 vs $1.23 on the cost index.
What cost the most in API. Input, Cached Input, or Output. There you have your answer.
Unfortunately, we have moved so much of the actual intelligence of models towards reasoning, what results in some models getting good scores, but this is because they are dumping a insane amount of reasoning tokens at the problem.
So a mid priced model, with heavy reasoning output, cost the same as a expensive model, with medium reasoning output.
Before the GPT Luna price drop of 80%, you actually had the same price if you used Luna High and Sol Low. With the difference that Sol Low was insane fast, and often way better code.
https://deepswe.datacurve.ai/
Do not look at the top score but more what is on the horizontal axis as you go down. Sol Medium is frankly, was the best performance for dollar, until that Luna price drop. I will even argue that despite the higher price, Sol Medium is still way better despite Luna Max being cheaper. Or Opus Low, one of the better values also.
What do you notice? Is that those models all have a high intelligence start point for their low setting. So that means they do not rely as much on output tokens aka thinking.
Qwen Max is their large model - over a trillion params. Similar to Kimi K3 in size. Qwen 3.8 27B is going to be more accessible to your own hardware. I'd say that Qwen Max is not approachable for the majority of people and companies to self-host.
Why should open weights correlate with cost? Cost correlates with the expense of running the model more than it does to the expense of developing the model.
Running a large model on rented GPU is still meaningfully more private than handing your chat logs over to FAGA
The acronym is new to me: Facebook, Anthropic, Google, openAi?
You said it yourself, model size and hardware. Big models cost more (good optimisation reduces things slightly, but they still need the hardware).
GPT5.6Sol completes the suite in 70M tokens, while Qwen3.8Max needs like 145M tokens. So this is a case where models like Qwen 3.8 and Kimi K3 use a lot more output (reasoning) tokens, go a good bit slower, so they can ultimately achieve a better intelligence score than if they went more quickly.
There are a couple of frontiers (ok bad word, maybe categories) in open weight models.
These Qwen 3.8 and Kimi K3 style models aren't trying to win on price, they're trying to compete on intelligence and capability.
Models like Deepseek V4 Flash (updated this week) are $0.03 a task, or 50X cheaper than Qwen3.8/Kimi K3, and 100X cheaper than Fable, while offering stunning intelligence. That's a different frontier for competition, and perhaps one more interesting for someone who wants to see them compete on cost.
it's a big honking trillion some parameters model. it's not cheap to run
A couple days ago they had published an overall score of 53 for this model, but that was removed and today it returned with a score of 56.
I wasn't able to find an explanation from them. Anyone knows what happened?
A wire transfer happened.
The kind of distillation guaranteed to work.
I just don't think you can combine speed, latency, price and intelligence into a single useful metric.
Clearly the weighting of those things depends on the usecase
I distrust any benchmark where Opus 5 beats Fable 5.
Anthropic have a real fight on their hands now. The competition is no longer 6 months behind, it's 6 days. If this had come out two or three weeks earlier this would be an absolute market leader on both quality and timeline.
Haven't tried this yet, but going to soon! I have to wonder what happened at Anthropic. We've cancelled our subscription in favor of OpenCode & Codex. Sol is just so good & OC goes so far for every $ spent. Claude's become a pain to work with - average output with an annoying personality. Who knew this would be an issue even a year ago? In any case, loving the stuff from the Chinese models!
> an annoying personality
I was with you until there. Qwen and the OpenAI models are great, aggressive agents, but they’re not as good as the anthropic models for human interaction. They just don’t have the subtlety, understanding, or attention to detail.
Claude 4.5/4.6 - absolutely agree. Fable 5? From my (limited) testing, also reasonable to interact with.
Opus 4.7/4.8/5? Absolutely smug and antagonistic and preachy. I'm constantly fighting with it to stop fighting me and accept that I occasionally know better. It's really frustrating to spend so many tokens of such an expensive model arguing with it.
Really? I've heard so many other people complain about this recently. And maybe it's possible that it's the prompt style even. But interesting that it's not across the board.
This has me hopeful for Qwen3.8-27B!
Is there a path to distill this model to do very specific things? Like a RAG strategy for a small (or even large) corpus?
Depends on what you want to do. Some task specific models can be trained with a few ten or hundred thousand training examples so you can use a bigger model to produce synthetic training examples and then fine tune a smaller student model. I think that's the usual process. Whether you'd get acceptable performance this way depends, as mentioned, on what you're trying to do and what you'd consider acceptable.
once you are able to get the full probability distributions per token you can distill it on specific domains. distilling without that isn't generally a good idea unless you have invested millions in the requisite infrastructure.
Qwen is just crushing it overall. I regularly use 3.7-flash for everyday coding needs and it gets the job done.
Out of curiosity, what's currently the best model I can use locally?
With an unlimited budget, Kimi K3 (which is quite comparable to this Qwen Max imo). With a normal budget/a PC you might already have, probably Qwen 3.6 27B.
$500k - Kimi K3 (maybe $250k? Haven’t done this one)
$25k - DSv4 Flash
$4k - Qwen 3.6 35A3B Q5
$1k - Qwen 3.6 27B Q4
Some people prefer the sense over the MoE YMMV.
That took what 2 years? I love how the chip ban made them more efficient
curious about methodology. ive seen them post results for claude/codex when they only ran over benchmarks 3 times per model...
Could someone like Apple be playing the long game - Good Enough(tm) intelligence will eventually fit in our pocket and homes?
DS4 Flash Q2/Q4 mixed quant fits on a DGX Spark (a $4000 device which is not particularly unheard of expense for Apple customers), and is indistinguishable for me from Opus for my personal daily use/assistant benchmarks[0].
[0]https://humanparadox.org/local-vs-frontier-benchmarks-for-my... - note here I tested Q8 but have found no difference at lower quant.
Apple is already doing this... they worked with Gemini to distill the model into a smaller one that fits on your phone. If you have iOS 27 Beta, you're already using this
Almost surely. Apple is extremely well positioned to take advantage of this over the next decade.
It is also the most expensive open source frontier model, per task; cf. Cost per Intelligence Index Task. If it is as good as the benchmarks indicate -- I'll never know because I see no reason to try it -- it is a positive indicator for Qwen and China.
Does it matter, it's all non deterministic bs ware and deepseek is eating the Americans lunch
Qwen is the way to go
Well, that's the bad index then. It is barely usable in my opinion compared to other Chinese frontier models.
which one of the other chinese frontier models is better?
It doesn’t even show up in the raw intelligence index, so how could it possibly be the best?
Go China!