If you're new to this: All of the open source models are playing benchmark optimization games. Every new open weight model comes with promises of being as good as something SOTA from a few months ago then they always disappoint in actual use.
I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released.
They are impressive, but they are not performing at Sonnet 4.5 level in my experience.
I have observed that they're configured to be very tenacious. If you can carefully constrain the goal with some tests they need to pass and frame it in a way to keep them on track, they will just keep trying things over and over. They'll "solve" a lot of these problems in the way that a broken clock is right twice a day, but there's a lot of fumbling to get there.
That said, they are impressive for open source models. It's amazing what you can do with self-hosted now. Just don't believe the hype that these are Sonnet 4.5 level models because you're going to be very disappointed once you get into anything complex.
Respectfully, from my experience and a few billions of tokens consumed, some opensource models really are strong and useful. Specifically StepFun-3.5-flash https://github.com/stepfun-ai/Step-3.5-Flash
I'm working on a pretty complex Rust codebase right now, with hundreds of integration tests and nontrivial concurrency, and stepfun powers through.
I have no relation to stepfun, and I'm saying this purely from deep respect to the team that managed to pack this performance in 196B/11B active envelope.
Yes and no. "Last-gen" (like, from 6 months ago) frontier models do still tend to outperform the best open source models. But some models, especially GLM-5, really have captured whatever circuitry drives pattern matching in the models they were trained off of.
I like this benchmark that competes models against one another in competitive environments, which seems like it can't really be gamed: https://gertlabs.com
I'm using Qwen 3.5 27b on my 4090 and let me tell you. This is the first time I am seriously blown away by coding performance on a local model. They are almost always unusable. Not this time though...
Are there any up-to-date offline/private agentic coding benchmark leaderboards?
If the tests haven't been published anywhere and are sufficiently different from standard problems, I would think the benchmarks would be robust to intentional over optimization.
Edit:
These look decent and generally match my expectations:
Hmm, I second this. Haven't compared Qwen3.5 122B yet, but played around with OpenCode + Qwen3-Coder-Next yesterday and did manual comparisons with Claude Code and Claude Code is still far ahead in general felt "intelligence quality".
I’ve switched to using Kimi 2.5 for all of my personal usage and am far from disappointed.
Aside from being much cheaper than the big names (yes, I’m not running it locally, but like that I could) it just works and isn’t a sycophant. Nice to get coding problems solved without any “That’s a fantastic idea!”/“great point” comments.
At least with Kimi my understanding is that beating benchmarks was a secondary goal to good developer experience.
I've been trying to get these things to local host and use tools. Am I right in understanding that it's impossible for these things to use tools from within llama.cpp? Do I need another "thing" to run the models? What exactly is the mechanism by which the models became aware that they're somewhere where they have tools availbale? So many questions...
Death by KPIs. Management makes it too risky to do anything but benchmaxx. It will be the death of American AI companies too. Eventually, people will notice models aren’t actually getting better and the money will stop flowing. However, this might be a golden age of research as cheap GPUs flood the market and universities have their own clusters.
I periodically try to run these models on my MBP M3 Max 128G (which I bought with a mind to run local AI). I have a certain deep research question (in a field that is deeply familiar to me) that I ask when I want to gauge model's knowledge.
So far Opus 4.6 and Gemini Pro are very satisfactory, producing great answers fairly fast. Gemini is very fast at 30-50 sec, Opus is very detailed and comes at about 2-3 minutes.
Today I ran the question against local qwen3.5:35b-a3b - it puffed for 45 (!) minutes, produced a very generic answer with errors, and made my laptop sound like it's going to take off any moment.
Wonder what am I doing wrong?.. How am I supposed to use this for any agentic coding on a large enough codebase? It will take days (and a 3M Peltor X5A) to produce anything useful.
You're comparing 100b parameters open models running on a consumer laptop VS private models with at the very least 1t parameters running on racks of bleeding edge professional gpus
Local agentic coding is closer to "shit me the boiler plate for an android app" not "deep research questions", especially on your machine
Well Opus and Gemini are probably running on multiple H200 equivalents, maybe multiple hundreds of thousands of dollars of inference equipment. Local models are inherently inferior; even the best Mac that money can buy will never hold a candle to latest generation Nvidia inference hardware, and the local models, even the largest, are still not quite at the frontier. The ones you can plausibly run on a laptop (where "plausible" really is "45 minutes and making my laptop sound like it is going to take off at any moment". Like they said -- you're getting sonnet 4.5 performance which is 2 generations ago; speaking from experience opus 4.6 is night and day compared to sonnet 4.5
The biggest gaps are not in hardware or model size. There is a lot of logical fallacy in the industry. Most people believe bigger is better. For model size, compute, tools, etc.
The reality in ML is that small models can perform better at a narrow problem set than large ones.
The key is the narrow problem set. Opus can write you a poem, create a shopping list, and analyze your massive code base.
We trained our model to only focus on coding with our specific agent harness, tools, and context engine. And it’s small enough to fit on an M2 16GB. It’s as good as sonnet 4.5 and way better than qwen3.5:35b-a3b
Well first of all you're running a long intense task on a thermally constrained machine. Your MacBook Pro is optimised for portability and battery life, not max performance under load. And apple's obsession with thinness overrules thermal performance for them. Short peaks will be ok but a 45 minute task will thoroughly saturate the cooling system.
Even on servers this can happen. At work we have a 2U sized server with two 250W class GPUs. And I found that by pinning the case fans at 100% I can get 30% more performance out of GPU tasks which translates to several days faster for our usecase. It does mean I can literally hear the fans screaming in the hallway outside the equipment room but ok lol. Who cares. But a laptop just can't compare.
Something with a desktop GPU or even better something with HBM3 would run much better. Local models get slow when you use a ton of context and the memory bandwidth of a MacBook Pro while better than a pc is still not amazing.
And yeah the heaviest tasks are not great on local models. I tend to run the low hanging fruit locally and the stuff where I really need the best in the cloud. I don't agree local models are on par, however I don't think they really need to be for a lot of tasks.
I've seen reports of qwen3.5-35b-a3b spending a ton of time reasoning if the context window is nearly empty-- supposedly it reasons less if you provide a long system prompt or some file contents, like if you use it in a coding agent.
I'm too GPU-poor to run it, but r/LocalLLaMa is full of people using it.
Running local AI models on a laptop is a weird choice. The Mini and especially the Studio form factor will have better cooling, lower prices for comparable specs and a much higher ceiling in performance and memory capacity.
I think knowledge of frontier research certainly scale with number of parameters. Also, US labs can pay more money to have researchers provide training data on these frontier research areas.
On the other hand, if indeed open source models and Macbooks can be as powerful as those SOTA models from Google, etc, then stock prices of many companies would already collapsed.
Depending on the specificity of the research, having a model with fewer parameters will come with a higher penalty. If you want a model to perform better at something specific while staying smaller, generally it will take specific training to achieve that.
Your Gemini or Opus question got send to a Texas datacenter where it got queued and processed by a subunit of 80 h200 140gb 1000w cards running a many billion or trillion parameter model. It took less that 200ms to process a single request. Your Claude cliënt decided to spawn 30 sub agents and iterated in a total of 90 requests totalling about 45000ms. Now compare that to your 100b transistor cpu doing something similar. Yes that would be slow.
use a larger model like Qwen3.5-122B-A10B quantized to 4/5/6 bits depending on how much context you desire, MLX versions if you want best tok/s on Mac HW.
if you are able to run something like mlx-community/MiniMax-M2.5-3bit (~100gb), my guess if the results are much better than 35b-a3b.
I have the exact same hardware. Was going to do the same thing with the 122B model … I’ll just keep paying Anthropic and he models are just that good. Trying out Gemini too. But won’t pay OpenAI as they’re going to be helping Pete Kegseth to develop autonomous killing machines.
On my 32GB Ryzen desktop (recently upgraded from 16GB before the RAM prices went up another +40%), did the same setup of llama.cpp (with Vulkan extra steps) and also converged on Qwen3-Coder-30B-A3B-Instruct (also Q4_K_M quantization)
On the model choice: I've tried latest gemma, ministral, and a bunch of others. But qwen was definitely the most impressive (and much faster inference thanks to MoE architecture), so can't wait to try Qwen3.5-35B-A3B if it fits.
I've no clue about which quantization to pick though ... I picked Q4_K_M at random, was your choice of quantization more educated?
I am a total neophyte when it comes to LLMs, and only recently started poking around into the internals of them. The first thing that struck me was that float32 dimensions seemed very generous.
I then discovered what quantization is by reading a blog post about binary quantization. That seemed too good to be true. I asked Claude to design an analysis assessing the fidelity of 1, 2, 4, and 8 bit quantization. Claude did a good job, downloading 10,000 embeddings from a public source and computing a similarity score and correlation coefficient for each level of quantization against the float32 SoT. 1 and 2 bit quantizations were about 90% similar and 8 bit quantization was lossless given the precision Claude used to display the results. 4 bit was interesting as it was 99% similar (almost lossless) yet half the size of 8 bit. It seemed like the sweet spot.
This analysis took me all of an hour so I thought, "That's cool but is it real?" It's gratifying to see that 4 bit quantization is actually being used by professionals in this field.
4-bit quantization on newer nvidia hardware is being supported in training as well these days. I believe the gpt-oss models were trained natively in MXFP4, which is a 4-bit floating point / e2m1 (2-exponent, 1 bit mantissa, 1 bit sign).
It doesn't seem terribly common yet though. I think it is challenging to keep it stable.
There's also work on ternary models that's quite interesting, because the arithmetic operations are super fast and they're extremely cache efficient. Well worth looking into if that's the sort of thing that interests you.
I do wonder where that extra acuity you get from 1% more shows up in practice.
I hate how I have basically no way to intuitively tell that because of how much of a black box the system is
I decided to try Qwen3.5 122B in LM Studio with Opencode and I am impressed. It's not super slow (M4 Max/128GB) and it's pretty close to how Claude Code feels. Getting pretty good code analysis, definitely feels Sonnet-esque. I'm hyped completely local alternatives are getting so good.
Getting better, but definitely not there yet, nor near Sonnet 4.5 performance.
What these open models are great for are for narrow, constrained domains, with good input/output examples. I typically use them for things like prompt expansion, sentiment analysis, reformatting or re-arranging flow of code.
What I found they have trouble with is going from ambiguous description -> solved problem. Qwen 3.5 is certainly the best of the OSS models I've found (beating out GPT 120b OSS which was the previous king), and it's just starting to demonstrate true intelligence in unbound situations, but it isn't quite there yet. I have a RTX 6000 pro, so Qwen 3.5 is free for me to run, but I tend to default to Composer 1.5 if I want to be cheap.
The trend however is super encouraging. I bought my vid card with the full expectation that we'll have a locally running GPT 5.2 equiv by EoY, and I think we're on track.
Smells like hyperbole. A lot of people making such claims don’t seem to have continued real world experience with these models or seem to have very weird standards for what they consider usable.
Up until relatively recently, while people had already long been making these claims, it came with the asterisks of „oh, but you can’t practically use more than a few K tokens of context“.
"Create a single page web app scientific RPN calculator"
Qwen 3.5 122b/a10b (at q3 using unsloth's dynamic quant) is so far the first model I've tried locally that gets a really usable RPN calculator app. Other models (even larger ones that I can run on my Strix Halo box) tend to either not implement the stack right, have non-functional operation buttons, or most commonly the keypad looks like a Picasso painting (i.e., the 10-key pad portion has buttons missing or mapped all over the keypad area).
This seems like such as simple test, but I even just tried it in chatgpt (whatever model they serve up when you don't log in), and it didn't even have any numerical input buttons. Claude Sonet 4.6 did get it correct too, but that is the only other model I've used that gets this question right.
Qwen3-Coder-30B-A3B-Instruct is good I think for in line IDE integration or operating on small functions or library code but I dont think you will get too far with one shot feature implementation that people are currently doing with Claude or whatever.
I used the 35b model to create a polars implementation of PCA (no sklearn or imports other than math and polars). In less than 10 minutes I had the code. This is impressive to me considering how poorly all models were with polars until very recently. (They always hallucinated pandas code.)
SWE chart is missing Claude on front page, interesting way to present your data. Mix and match at will.
Grown up people showing public school level sneakiness. That fact alone disqualifies your LL. Business/marketing leaders usually are brighter than average developers... so there.
Thinking about getting a new MBP M5 Max 128GB (assuming they are released next week). I know "future proofing" at this stage is near impossible, but for writing Rust code locally (likely using Qwen 3.5 for now on MLX), the AIs have convinced me this is probably my best choice for immediate with some level of longevity, while retaining portability (not strictly needed, but nice to have). Alternatively was considering RTX options or a mac studio, but was leaning towards apple for the unified memory. What does HN think?
Thermals. Your workloads will be throttled hard once it inevitably runs hot. See comments elsewhere in thread about why LLMs on laptops like MBP is underwhelming. The same chips in even a studio form factor would perform much better.
Strix Halo machines are a good option too if you are at all price sensitive. AMD (with all the downsides of that for AI work) but people are getting decent performance from them.
I have a Mac Studio with 128GB and a M4 Max and I'd recommend it. The power usage is also pretty good, but you may not care if you live somewhere where energy is cheap.
I've been mulling the same, but decided against (for now)
Using Claude Code Max 20 so ROI would be maybe 2+ years.
CC gives me unlimited coding in 4-6 windows in parallel. Unsure if any model would beat (or even match) that, both in terms in quality and speed.
I wouldn't gamble on that now. With a subscription, I can change any time. With the machine, you risk that this great insane model comes out but you need 138GB and then you'll pay for both.
A big part that a lot of local users forget is inference is hard. Maybe you have the wrong temperature. Maybe you have the wrong min P. Maybe you have the wrong template. Maybe the implementation in llama cpp has a bug. Maybe Q4 or even Q8 just won’t compare to BF16. Reality is, there’s so many knobs to LLM inferencing and any can make the experience worse. It’s not always the model’s fault.
Radeon R9700 with 32 GB VRAM is relatively affordable for the amount of RAM and with llama.cpp it runs fast enough for most things. These are workstation cards with blower fans and they are LOUD. Otherwise if you have the money to burn get a 5090 for speeeed and relatively low noise, especially if you limit power usage.
It depends. How much are you willing to wait for an answer? Also, how far are you willing to push quantization, given the risk of degraded answers at more extreme quantization levels?
It's less than you'd think. I'm using the 35B-A3B model on an A5000, which is something like a slightly faster 3080 with 24GB VRAM. I'm able to fit the entire Q4 model in memory with 128K context (and I think I would probably be able to do 256K since I still have like 4GB of VRAM free). The prompt processing is something like 1K tokens/second and generates around 100 tokens/second. Plenty fast for agentic use via Opencode.
I think the 27B dense model at full precision and 122B MoE at 4- or 6-bit quantization are legitimate killer apps for the 96 GB RTX 6000 Pro Blackwell, if the budget supports it.
I imagine any 24 GB card can run the lower quants at a reasonable rate, though, and those are still very good models.
Big fan of Qwen 3.5. It actually delivers on some of the hype that the previous wave of open models never lived up to.
I've got the unsloth q4_K_XL 35b running in llama.cpp on an i9/64G/4090 machine doing double-digit tokens per second with a 90k+ token context window available. The model's completely in VRAM.
It is slow but usable via opencode on a mbp m3 max 48 gb. So I guess hosted is still the better option for most people.
The local models are considerably better relative to the hosted ones compared to 6 months ago. Bench maxing or not - stuff is happening in this area for sure.
The larger 3.5 quants are actually pretty close to the full-blown 397B model's performance, at least looking at the numbers. Qwen 3.5 seems more tolerant of quantization than most.
122B-A10B-UD-Q4-K-XL generated https://pastebin.com/j3ddfNtS -- but I can't get it to do anything in a couple of online interpreters. Guessing it wasn't trained on a lot of Brainfuck code.
Edit: it looks like the flagship models work by writing a C or Python program to do the bookkeeping. I don't have Qwen set up to use tools, and even Opus 4.6 shits the bed when told to do it without tools [1], so not too surprising that it didn't work.
1: https://claude.ai/share/1f5289ae-decd-4dfa-98fd-0d34346008c6 -- I interrupted it and told it not to use a C/Python program or any other tools to generate the Brainfuck code, and it gave me an error message after about 10 minutes that wasn't logged to the chat.
18GB was an odd 3-channel one-off for the M3 Pros. I guess there's a bunch of them out there, but how slow would 27B be on it, due to not being an MOE model.
An AMD AI max+ 395 - I use the one from frame.work (https://frame.work/de/en/desktop) with 128GB unified RAM and it can run a 120b model (gpt-oss:120b) just fine.
No it does not. None of these models have the “depth” that the frontier models have across a variety of conversations, tasks and situations. Working with them is like playing snakes and ladders, you never know when it’s going to do something crazy and set you back.
I would say 27B matches with Sonnet 4.0, while 397B A17B matches with Opus 4.1. They are indeed nowhere near Sonnet 4.5, but getting 262144 context length at good speed with modest hardware is huge for local inference.
You mean 35B A3B? If this is shit, this is some of the best shit out I've seen yet. Never in a million years did I think I'd have an LLM running locally, actually writing code on my behalf. Accurately too.
Our shop cannot use cloud models for sensitive data and code. For shops like ours, we continue to be impressed and appreciative of the progress in open-source / self-hosted models.
In practice I have not seen this. Sonnet is incredible performance. No open model is close. Hosted open models are so much worse that I end up spending more because of inferior intelligence.
One highly annoying facet of the hardware is that AND's support for the NPU under linux is currently non-existent. which abandons 50 of the 126 TOPS stated of AI capability. They seem to think that Windows support is good enough. Grrrrrr.
I asked it to recite potato 100 times coz I wanted to benchmark speed of CPU vs GPU. It's on 150 line of planning. It recited the requested thing 4 times already and started drafting the 5th response.
Qwen3.5 pretty much requires a long system prompt, otherwise it goes into a weird planning mode where it reasons for minutes about what to do, and double and triple checks everything it does. Both Gemini's and Claude Opus 4.6's prompts work pretty well, but are so long that whatever you're using to run the model has to support prompt caching. Asking it to "Say the word "potato" 100 times, once per line, numbered.", for example, results in the following reasoning, followed by the word "potato" in 100 numbered lines, using the smallest (and therefore dumbest) quant unsloth/Qwen3.5-35B-A3B-GGUF:UD-IQ2_XXS:
"User is asking me to repeat the word "potato" 100 times, numbered. This is a simple request - I can comply with this request. Let me create a response that includes the word "potato" 100 times, numbered from 1 to 100.
I'll need to be careful about formatting - the user wants it numbered and once per line. I should use minimal formatting as per my instructions."
well hold on now, maybe it’s onto something. do you really know what it means to “recite” “potato” “100” “times”? each of those words could be pulled apart into a dissertation-level thesis and analysis of language, history, and communication.
either that, or it has a delusional level of instruction following. doesn’t mean it can’t code like sonnet though
It's not only "non-Chinese" to think about here. There's nobody really touching Qwen in the single-GPU size class and there hasn't been for a couple of generations.
All the western ones are closed while all the Chinese ones are open. The only exception is the European Mistral but performance of that model is not very satisfactory. Hopefully they make some improvements soon
from my personal experience, qwen 30b a3b understand command quiet well as long as the input is not big enough that ruin the attention (I feel the boundary is somewhere between 8000 or 12000?). But that isn't really bug of model itself though. A smaller model just have shorter memory, it's simply physical restriction.
I made a mixed extraction, cleaning, translation, formatting task on job that have average 6000 token input. And so far, only 30b a3b is smart enough not miss job detail (most of time)
I later refactor the task to multi pass using smaller model though. Make job simpler is still a better strategy to get clean output if you can change the pipeline.
Aurornis|22 hours ago
I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released.
They are impressive, but they are not performing at Sonnet 4.5 level in my experience.
I have observed that they're configured to be very tenacious. If you can carefully constrain the goal with some tests they need to pass and frame it in a way to keep them on track, they will just keep trying things over and over. They'll "solve" a lot of these problems in the way that a broken clock is right twice a day, but there's a lot of fumbling to get there.
That said, they are impressive for open source models. It's amazing what you can do with self-hosted now. Just don't believe the hype that these are Sonnet 4.5 level models because you're going to be very disappointed once you get into anything complex.
kir-gadjello|21 hours ago
I'm working on a pretty complex Rust codebase right now, with hundreds of integration tests and nontrivial concurrency, and stepfun powers through.
I have no relation to stepfun, and I'm saying this purely from deep respect to the team that managed to pack this performance in 196B/11B active envelope.
lend000|16 hours ago
I like this benchmark that competes models against one another in competitive environments, which seems like it can't really be gamed: https://gertlabs.com
wolvoleo|21 hours ago
I bet the cloud ones are doing it a lot more because they can also affect the runtime side which the open source ones can't.
dimgl|19 hours ago
rudhdb773b|20 hours ago
If the tests haven't been published anywhere and are sufficiently different from standard problems, I would think the benchmarks would be robust to intentional over optimization.
Edit: These look decent and generally match my expectations:
https://www.apex-testing.org/
chaboud|20 hours ago
Goodhart's law shows up with people, in system design, in processor design, in education...
Models are going to be over-fit to the tests unless scruples or practical application realities intervene. It's a tale as old as machine learning.
warpspin|10 hours ago
crystal_revenge|20 hours ago
I’ve switched to using Kimi 2.5 for all of my personal usage and am far from disappointed.
Aside from being much cheaper than the big names (yes, I’m not running it locally, but like that I could) it just works and isn’t a sycophant. Nice to get coding problems solved without any “That’s a fantastic idea!”/“great point” comments.
At least with Kimi my understanding is that beating benchmarks was a secondary goal to good developer experience.
ekjhgkejhgk|8 hours ago
amelius|22 hours ago
And could quantization maybe partially explain the worse than expected results?
noosphr|21 hours ago
The only benchmarks worth anything are dynamic ones which can be scaled up.
baq|12 hours ago
that said, sonnet 4.5 is not a good model today, March 1st 2026. (it blew my mind on its release day, September 29th, 2025.)
ekianjo|18 hours ago
there is nothing open "source" about them. They are open weights, that's all.
eurekin|21 hours ago
jackblemming|21 hours ago
bourjwahwah|21 hours ago
[deleted]
mstaoru|23 hours ago
So far Opus 4.6 and Gemini Pro are very satisfactory, producing great answers fairly fast. Gemini is very fast at 30-50 sec, Opus is very detailed and comes at about 2-3 minutes.
Today I ran the question against local qwen3.5:35b-a3b - it puffed for 45 (!) minutes, produced a very generic answer with errors, and made my laptop sound like it's going to take off any moment.
Wonder what am I doing wrong?.. How am I supposed to use this for any agentic coding on a large enough codebase? It will take days (and a 3M Peltor X5A) to produce anything useful.
lm28469|23 hours ago
You're comparing 100b parameters open models running on a consumer laptop VS private models with at the very least 1t parameters running on racks of bleeding edge professional gpus
Local agentic coding is closer to "shit me the boiler plate for an android app" not "deep research questions", especially on your machine
aspenmartin|23 hours ago
adam_patarino|9 hours ago
The reality in ML is that small models can perform better at a narrow problem set than large ones.
The key is the narrow problem set. Opus can write you a poem, create a shopping list, and analyze your massive code base.
We trained our model to only focus on coding with our specific agent harness, tools, and context engine. And it’s small enough to fit on an M2 16GB. It’s as good as sonnet 4.5 and way better than qwen3.5:35b-a3b
Our beta will be out soon / rig.ai
wolvoleo|21 hours ago
Even on servers this can happen. At work we have a 2U sized server with two 250W class GPUs. And I found that by pinning the case fans at 100% I can get 30% more performance out of GPU tasks which translates to several days faster for our usecase. It does mean I can literally hear the fans screaming in the hallway outside the equipment room but ok lol. Who cares. But a laptop just can't compare.
Something with a desktop GPU or even better something with HBM3 would run much better. Local models get slow when you use a ton of context and the memory bandwidth of a MacBook Pro while better than a pc is still not amazing.
And yeah the heaviest tasks are not great on local models. I tend to run the low hanging fruit locally and the stuff where I really need the best in the cloud. I don't agree local models are on par, however I don't think they really need to be for a lot of tasks.
meatmanek|18 hours ago
I'm too GPU-poor to run it, but r/LocalLLaMa is full of people using it.
__mharrison__|22 hours ago
Admittedly, I haven't tried these models on my Mac, but I have on my DGX Spark, and they ran fine. I didn't see the slowdown you're mentioning.
zozbot234|23 hours ago
xtn|18 hours ago
On the other hand, if indeed open source models and Macbooks can be as powerful as those SOTA models from Google, etc, then stock prices of many companies would already collapsed.
muyuu|15 hours ago
notreallya|23 hours ago
holoduke|15 hours ago
culi|23 hours ago
rienko|22 hours ago
if you are able to run something like mlx-community/MiniMax-M2.5-3bit (~100gb), my guess if the results are much better than 35b-a3b.
furyofantares|23 hours ago
andxor|22 hours ago
gigatexal|18 hours ago
CamperBob2|22 hours ago
Also, performance on research-y questions isn't always a good indicator of how the model will do for code generation or agent orchestration.
Paddyz|19 hours ago
[deleted]
alexpotato|1 day ago
- llama.cpp
- OpenCode
- Qwen3-Coder-30B-A3B-Instruct in GGUF format (Q4_K_M quantization)
working on a M1 MacBook Pro (e.g. using brew).
It was bit finicky to get all of the pieces together so hopefully this can be used with these newer models.
https://gist.github.com/alexpotato/5b76989c24593962898294038...
freeone3000|23 hours ago
kpw94|23 hours ago
On the model choice: I've tried latest gemma, ministral, and a bunch of others. But qwen was definitely the most impressive (and much faster inference thanks to MoE architecture), so can't wait to try Qwen3.5-35B-A3B if it fits.
I've no clue about which quantization to pick though ... I picked Q4_K_M at random, was your choice of quantization more educated?
robby_w_g|23 hours ago
copperx|1 day ago
jackcosgrove|20 hours ago
I then discovered what quantization is by reading a blog post about binary quantization. That seemed too good to be true. I asked Claude to design an analysis assessing the fidelity of 1, 2, 4, and 8 bit quantization. Claude did a good job, downloading 10,000 embeddings from a public source and computing a similarity score and correlation coefficient for each level of quantization against the float32 SoT. 1 and 2 bit quantizations were about 90% similar and 8 bit quantization was lossless given the precision Claude used to display the results. 4 bit was interesting as it was 99% similar (almost lossless) yet half the size of 8 bit. It seemed like the sweet spot.
This analysis took me all of an hour so I thought, "That's cool but is it real?" It's gratifying to see that 4 bit quantization is actually being used by professionals in this field.
deepsquirrelnet|20 hours ago
It doesn't seem terribly common yet though. I think it is challenging to keep it stable.
[1] https://www.opencompute.org/blog/amd-arm-intel-meta-microsof...
[2] https://www.opencompute.org/documents/ocp-microscaling-forma...
regularfry|8 hours ago
silisili|19 hours ago
tymscar|20 hours ago
I do wonder where that extra acuity you get from 1% more shows up in practice. I hate how I have basically no way to intuitively tell that because of how much of a black box the system is
pram|13 hours ago
jjcm|21 hours ago
What these open models are great for are for narrow, constrained domains, with good input/output examples. I typically use them for things like prompt expansion, sentiment analysis, reformatting or re-arranging flow of code.
What I found they have trouble with is going from ambiguous description -> solved problem. Qwen 3.5 is certainly the best of the OSS models I've found (beating out GPT 120b OSS which was the previous king), and it's just starting to demonstrate true intelligence in unbound situations, but it isn't quite there yet. I have a RTX 6000 pro, so Qwen 3.5 is free for me to run, but I tend to default to Composer 1.5 if I want to be cheap.
The trend however is super encouraging. I bought my vid card with the full expectation that we'll have a locally running GPT 5.2 equiv by EoY, and I think we're on track.
solarkraft|1 day ago
Up until relatively recently, while people had already long been making these claims, it came with the asterisks of „oh, but you can’t practically use more than a few K tokens of context“.
derekp7|23 hours ago
Qwen 3.5 122b/a10b (at q3 using unsloth's dynamic quant) is so far the first model I've tried locally that gets a really usable RPN calculator app. Other models (even larger ones that I can run on my Strix Halo box) tend to either not implement the stack right, have non-functional operation buttons, or most commonly the keypad looks like a Picasso painting (i.e., the 10-key pad portion has buttons missing or mapped all over the keypad area).
This seems like such as simple test, but I even just tried it in chatgpt (whatever model they serve up when you don't log in), and it didn't even have any numerical input buttons. Claude Sonet 4.6 did get it correct too, but that is the only other model I've used that gets this question right.
tempest_|1 day ago
__mharrison__|22 hours ago
oscord|22 hours ago
shell0x|19 hours ago
plastic3169|14 hours ago
zos_kia|14 hours ago
nu11ptr|23 hours ago
pamcake|20 hours ago
Thermals. Your workloads will be throttled hard once it inevitably runs hot. See comments elsewhere in thread about why LLMs on laptops like MBP is underwhelming. The same chips in even a studio form factor would perform much better.
nl|19 hours ago
Also Nvidia Spark.
shell0x|19 hours ago
cmenge|21 hours ago
Using Claude Code Max 20 so ROI would be maybe 2+ years.
CC gives me unlimited coding in 4-6 windows in parallel. Unsure if any model would beat (or even match) that, both in terms in quality and speed.
I wouldn't gamble on that now. With a subscription, I can change any time. With the machine, you risk that this great insane model comes out but you need 138GB and then you'll pay for both.
solarkraft|1 day ago
Edit: The unsloth quants seem to have been fixed, so they are probably the go-to again: https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks
xmddmx|22 hours ago
Theory is that some of the model parameters aren't set properly and this encourages endless looping behavior when run under ollama:
https://github.com/ollama/ollama/issues?q=is%3Aissue%20state... (a bunch of them)
syntaxing|21 hours ago
mark_l_watson|1 day ago
johnnyApplePRNG|1 day ago
stavros|23 hours ago
erelong|1 day ago
suprjami|1 day ago
If you want to spend twice as much for more speed, get a 3090/4090/5090.
If you want long context, get two of them.
If you have enough spare cash to buy a car, get an RTX Ada with 96G VRAM.
dajonker|1 day ago
andsoitis|1 day ago
Check out the HP Omen 45L Max: https://www.hp.com/us-en/shop/pdp/omen-max-45l-gaming-dt-gt2...
zozbot234|1 day ago
throwdbaaway|18 hours ago
xienze|1 day ago
elorant|1 day ago
CamperBob2|1 day ago
I imagine any 24 GB card can run the lower quants at a reasonable rate, though, and those are still very good models.
Big fan of Qwen 3.5. It actually delivers on some of the hype that the previous wave of open models never lived up to.
OkWing99|15 hours ago
Excluding MBP M5 128GB.
regularfry|8 hours ago
chvid|14 hours ago
The local models are considerably better relative to the hosted ones compared to 6 months ago. Bench maxing or not - stuff is happening in this area for sure.
sunkeeh|23 hours ago
Quite misleading, really.
CamperBob2|22 hours ago
car|22 hours ago
CamperBob2|16 hours ago
Edit: it looks like the flagship models work by writing a C or Python program to do the bookkeeping. I don't have Qwen set up to use tools, and even Opus 4.6 shits the bed when told to do it without tools [1], so not too surprising that it didn't work.
1: https://claude.ai/share/1f5289ae-decd-4dfa-98fd-0d34346008c6 -- I interrupted it and told it not to use a C/Python program or any other tools to generate the Brainfuck code, and it gave me an error message after about 10 minutes that wasn't logged to the chat.
kristianpaul|23 hours ago
kristianp|23 hours ago
aliljet|1 day ago
magicalhippo|23 hours ago
Obviously there's more to a model than that but it's a data point.
[1]: https://github.com/fairydreaming/lineage-bench
[2]: https://github.com/fairydreaming/lineage-bench-results/tree/...
lostmsu|1 day ago
Somewhere between Haiku 4.5 and Sonnet 4.5
gunalx|23 hours ago
Strong vision and reasoning performance, and the 35-a3b model run s pretty ok on a 16gb GPU with some CPU layers.
lubitelpospat|17 hours ago
carlio|17 hours ago
See Wendel's review here - https://www.youtube.com/watch?v=L-xgMQ-7lW0
There are other mini-pc manufacturers, the mainboard is the important part.
piyh|19 hours ago
karmasimida|20 hours ago
hsaliak|20 hours ago
jbellis|22 hours ago
none of the qwen 3.5 models are anywhere near sonnet 4.5 class, not even the largest 397b.
BUT 27b is the smartest local-sized model in the world by a wide wide margin. (35b is shit. fast shit, but shit.)
benchmarks are complete, publishing on Monday.
throwdbaaway|19 hours ago
Will check your updated ranking on Monday.
dimgl|19 hours ago
jgalt212|8 hours ago
kristianpaul|1 day ago
jedisct1|11 hours ago
If you want to use small models for coding, I'd highly recommend Swival https://swival.dev which was explicitly optimized for these.
renewiltord|18 hours ago
pstuart|19 hours ago
PunchyHamster|23 hours ago
...yeah I doubt it
lachiflippi|23 hours ago
"User is asking me to repeat the word "potato" 100 times, numbered. This is a simple request - I can comply with this request. Let me create a response that includes the word "potato" 100 times, numbered from 1 to 100.
I'll need to be careful about formatting - the user wants it numbered and once per line. I should use minimal formatting as per my instructions."
lumirth|23 hours ago
either that, or it has a delusional level of instruction following. doesn’t mean it can’t code like sonnet though
xenospn|1 day ago
MarsIronPI|23 hours ago
What's your problem with Chinese LLMs?
regularfry|8 hours ago
vagrantJin|4 hours ago
culi|23 hours ago
shell0x|19 hours ago
Paddyz|19 hours ago
[deleted]
mmis1000|16 hours ago
I made a mixed extraction, cleaning, translation, formatting task on job that have average 6000 token input. And so far, only 30b a3b is smart enough not miss job detail (most of time)
I later refactor the task to multi pass using smaller model though. Make job simpler is still a better strategy to get clean output if you can change the pipeline.
aplomb1026|21 hours ago
[deleted]
u1hcw9nx|1 day ago
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ramon156|1 day ago
andsoitis|1 day ago