It was almost certainly not trained for coding, as it's got both audio and vision input, is only 12B, and nowhere in the announcement is coding mentioned. It will likely not have good performance on coding in general, compared to other small models like Qwen 3.6 35B A3B, Gemma 4 26B A4B, Nvidia Nemotron 3 Nano 30B-A3B, gpt-oss-20b.
For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
Gemma 4 31B is the top dog at small model coding, but is dense so it needs ~48GB unified RAM for full context. If you want decent coding on a laptop you need a lot of RAM. But this shouldn't be surprising, dev machines have always needed lots of resources.
qwen IMO is far better for coding, esp agentic coding when combined with something like Pi, it comes probably close enough to Sonnet for a lot of use cases.
Gemma family is better for almost all other tasks you'd use a local llm for.
Something about qwen models hold up really well even at low quants. for most other models anything under q5 is cooked, but on 35B-A3B I can get a lot of things done even at q3_xl. It is definitely better than full precision 9B
I want to try a hybrid setup of Gemma 4 E4B with lots of context for general, then Qwen 3.5 9B or larger for coding. Strix Halo set up this weekend, which may enable even larger Qwen models with tons of context.
The larger Gemma models are quite good at PHP. I would not be surprised if that was a training objective — it's one of the more consumer-focussed programming languages. They have very good knowledge of wordpress hooks.
> For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
You seem like the guy to ask. For a laptop with 12GB VRAM (RTX 5070) and 32 GB system RAM, what is a good multilingual (English, Hebrew, Greek) model for conversing with personal notes in Org mode format? I don't care how long updating the model or rag takes, and even inference can be reasonably slow, but the results of the query as they relate to my personal notes are important. I don't care about general knowledge, for those questions I can use e.g. ChatGPT.
Do they prefer E4B over the larger models or is it a matter of what fits their machine? I assume 4B isn't large enough to get interesting writing but I don't know anything about it.
Qwen models are always good. The 35B A3 model is a MoE model which means it has higher performance in RAM constrained environments compared to the 27B dense model (which is better at coding).
I don't have experience to rate it's Hebrew or Greek performance but apparently it's not bad.
For the biggest languages, Spanish, French, maybe.
For smaller ones like my native Latvian, the output could be confused for good translation from across the room, the words do look like Latvian words. But the quality is Google translate circa 20 years ago, tops.
It could probably do a decent enough translation to English, if all you need is to get the gist of text. But for smaller European language outputs, nothing comes close to Gemini.
While Gemini 4 seems fine, Gemma 4 does not do Hebrew well. I've replaced it with Aya Expanse and am getting much better results, but there is still much improvement to be had.
I'm not doing translations, rather querying Hebrew text with a Hebrew prompt.
Just tried it and honestly it's a terrible experience lacking any sort of intent or reason.
Which is unsurprising in the AI space.
You get a wall of text showing you various random fine-tuned models by random people, and that is basically it.
Actual sane default requirements like "just give me the normal AI labs", "please filter for dense only" and "I want this exact context size at this quant" are not part of the tool, apparently. Neither is "compare these quants for me for the same model".
Or maybe it's just hidden enough that I did not find them before I've stopped caring.
Conway's law is at it again.
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Edit:
I have since then had qwen3.6 ponder the codebase and think about my complaints.
Seems to require a major data model overhaul to actually fix those, so they're legit.
Which I didn't doubt, but nice to have some extra fabricated confirmation after it initially refused and said "nooooo the readme says otherwise nooo hypfer is just a hater noo"
___
Edit 2:
It gets worse the longer I stare at it. This could've been a web calculator.
We need benchmarks by engine, cli switch sets, and device with filters by cpu, gpu, and type. And if someone could please aggregate that in a way where people can upload results and just automatically see the best of any model for their device that would be a killer app.
I've wanted to vibe code a tuning app, that pumps data through your CPU-GPU-RAM to try and determine the best parameters for each model, but I think it's just too much work compared to manually running by hand a one-liner and changing things here and there.
I have found these things to be fully exasperating, to be honest, even though I am seeking information about a pretty "known" machine — a 64GB M1 Max MBP.
(Honestly I think Apple's "AI push" could do worse than just focus on a curated model library, a couple of Apple-standard Gemini distillations, an OS-level model manager and some sort of tweak of their containers system to do what Docker's sbx does. They could demystify a lot of this shit.)
Have you found Gemma 4 31B better than Qwen 3.6 27B Q8? I just started using Qwen + Pi agent and it's great, but "which model works best" is still totally crowdsourced and I was going off of peoples' opinions on reddit. Would love to hear more opinions if people have them.
> Have you found Gemma 4 31B better than Qwen 3.6 27B Q8?
Which quant of Gemma? For coding Qwen seems to be pretty far ahead, but generally Gemma seems to have a "vaster" set of knowledge, but armed with a search tool it doesn't really matter, and Qwen 3.6 been really great for all sorts of tool calling. I mostly do programming and related things though, fwiw.
> I was going off of peoples' opinions on reddit
It's extremely astroturfed all over the place, especially the larger subreddits, and especially the one related to a specific animal in a specific location. It's sad, as early on it was a great resource, but now it's mostly paid posts and a race to the bottom, with lots of piling, and all the knowledgeable people I used to recognize are nowhere to be found.
Just a bit of flair. Also, bunch of people have "keyword watchers" setup for various terms, so when you mention certain things on HN, reddit and elsewhere, you get commentators who enter the conversation not because the context or larger conversation, but because the single term/thing they care deeply about was mentioned, and it just gets very boring to read the whole attackers/defenders comments over and over again. But ultimately I just did it like that because it was more fun to write it like that.
I'm not sure that GP is correct, many people in that forum tend to hate Qwen for closing up many of their more recent models and leaving the whole local inference community 'stranded' on their older releases.
Yes. I'm using Gemma-4 31B (gemma-4-31B-it-assistant.Q4_K_M.gguf) with llama.cpp to attribute quotations throughout chapters of my sci-fi novel. I started with Qwen3, but couldn't get it to work. Qwen3 TTS Voice Design, on the other hand, is incredible (Qwen3-TTS-12Hz-1.7B-VoiceDesign). I'm using both for an audiobook generator that produces a variety of voices.
Gemma 4 31B is enormously impressive. You get 1000 requests/day for free on Google's API and another 1000/day off OpenRouter. Only problem is you get 503 like crazy.
It does appear to have training for javascript and PHP, from what I can see, and pretty solid knowledge of wordpress and woocommerce. I would guess it has beginner-friendly knowledge of Python, too?
(Though it is gaslighting me about PHP anonymous functions.)
I would not use it to write code (the MoE 26B writes really good PHP), but it appears to have absolutely good enough knowledge to write implementation plans, and I think that could be useful in a sort of agentic coding tutorial environment.
I test these models with simple things. My favourite mini test is asking an AI to write a "last login" tracker facility for wordpress with a sortable admin column, which is trivial code — only a few lines -- but touches on a reasonably deep bit of the WP API. If you ask it to prompt you with clarifying questions, those questions are quite revealing.
It can write the code. Not tested it but I am sure it works. It's not as elegant.
It is not as good at understanding nuanced instructions as either the 26B or the sparse Qwen 3.6. There are concise things you can say in a prompt to Qwen 3.6 that have it draw logical conclusions that fully impress me.
I am more impressed by it than I expected. I reckon this would be quite useful in a tutorial tool.
(I say this as a sort of qualified cynic; I think much of the AI circus is a farce. But if these things are to ever be useful for teaching without making people dependent on some cloud "intelligence tap", this is progress)
The ram that’s important for LLMs is gpu-accessible memory, meaning either systems with unified ram or VRAM, the latter of which is tied to the caliber of GPU one has.
Unified memory is soldered to the motherboard and needs to be ordered with the new laptop, for prices that are well above what the equivalent amount of SODIMM would cost.
Fine if work's paying, but for personal devices (that might have been purchased before local models got good), people have what they have.
My job still issues 16GB laptops as standard. You need a business reason to get more. This has been going on since before the price hikes.
I’m a system administrator and I can do my job with no issues at 16GB. Most days 8GB would likely be enough, since I’m just using and abusing other systems anyway.
Java devs at my last job were still running 16GB in 2020. Admittedly that was a while ago. Still not a decade.
8Gb was the standard for a long time (before Apple went Silicon), because from what I understood, is that SDRAM needs to contantly power cycle the memory bus otherwise the bits will fade, and so by having more RAM, your battery would last a little less... this was around the time when 3 hours charge was unheard of, so every little bit helped.
Probably doesn't matter these days with all-day batterys, but now the demand-supply curve is lopsided.
For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
Gemma 4 31B is the top dog at small model coding, but is dense so it needs ~48GB unified RAM for full context. If you want decent coding on a laptop you need a lot of RAM. But this shouldn't be surprising, dev machines have always needed lots of resources.