It's strange that they don't include reasoning training (RLVR). Their justification doesn't sound convincing:
> While reasoning models have grown in popularity in recent years, their abilities aren’t always the most efficient way to get a result. In enterprise settings, token costs and speed are often as important as performance. That is why turning to less expensive, non-reasoning models with similar benchmark performance for select tasks like instruction following and tool calling makes sense for enterprise users.
I guess they currently don't have the ability to do proper RLVR.
People complain a lot about LLM-written articles, but the human comments here on HN are far worse. Mostly a bunch of people extremely proud of themselves for not reading an LLM-written article, and then a bunch of people who take it at face value and make the model seem almost useful, and one comment that actually looked at other benchmarks. Good 'ol humanity, good at.. being emotional... and not doing analysis.....…
>> The only benchmark it does well at compared to other models is non-hallucination and instruction following.
I think instruction following is going to be the most useful thing these models do. Add a voice interface and access to a bunch of simple, straight-forward devices or APIs and you have a mildly useful assistant. If that can be done in 8B parameters it will soon run on edge devices. That's solid usefulness.
People complain a lot about LLM-written articles, but the human comments here on HN are far worse. Mostly a bunch of people extremely proud of themselves for not reading an LLM-written article, and then a bunch of people who take it at face value and make the model seem almost useful, and one comment that actually looked at other benchmarks. Good 'ol humanity, good at.. being emotional... and not doing analysis.....…
The real "sleeper" might be https://huggingface.co/ibm-granite/granite-vision-4.1-4b if the benchmarks hold up for such a small model against frontier models for table & semantic k:v extraction.
Woah, is this part of the future of models? Basically little models you can use as tools.
I don’t know how many difference little models this uses under the hood, but I was shocked at how good it was at the couple document extraction tasks I threw it at.
I test drove it yesterday. It's pretty impressive at 8b. Runs on commodity hardware quickly. Qwen3.6 35b a3b is still my local champion but I may use this for auto complete and small tasks. Granite has recent training data which is nice. If the other small models got fine tuned on recent data I don't know if I would use this at all, but that alone makes it pretty decent. The 4b they released was not good for my needs…
Have you tried the Gemma 4 series, out of curiosity? I haven’t run a local model in a while, but the benchmarks look good. I’d take a free local tool-use model if it was relatively consistent.
I have tested Gemma4-26B against Qwen3.6-35B. Gemma beats Qwen on structured data extraction and instruction following. Gemma is far more precise than Qwen in these tasks, while Qwen gets a bit more creative, verbose, and imprecise. However Qwen has far more general smartness, high token throughput. Qwen could precisely pinpoint the issues in data quality and code, while Gemma had no clue. On the coding skills, Qwen appears to have edge over Gemma, but this could depend on the agent you use. For direct chat (llama_cpp UI), bot models show same skills for coding.
People complain a lot about LLM-written articles, but the human comments here on HN are far worse. Mostly a bunch of people extremely proud of themselves for not reading an LLM-written article, and then a bunch of people who take it at face value and make the model seem almost useful, and one comment that actually looked at other benchmarks. Good 'ol humanity, good at.. being emotional... and not doing analysis.....…
> the human comments here on HN are far worse
I already assume some comments here are LLM written.
> Full stop. Why people don't edit out obvious sloppification and expect to still have readers left
Third line in to the article: "But there’s one result in the benchmarks I keep coming back to." I hear this sort of thing all the time now on YouTube from media/news personalities: “And that’s the part nobody seems to be talking about.” "And here's what keeps me up at night." “This is where the story gets complicated.” “Here’s the piece that doesn’t quite fit.” “And this is where the usual explanation starts to break…
Nate B Jones videos ... YouTube channel "AI News and Strategy Daily" channel uses all of these. Every video.
The 8B class closing the gap with 32B is the real story of 2026 for anyone running models locally. I've been using smaller models for agent tool-use and the progress this year is real.
The gap that still matters most isn't intelligence — it's consistency on structured output. When you chain 5+ tool calls in sequence, even a small per-call reliability difference compounds fast. Would love to see Granite 4.1 benchmarked specifically on multi-step function calling rather than just general benchmarks.
I notice this very often in LinkedIn posts, and it's annoying, but I had not realized it was LLM-speak? Isn't it possible that people write like this naturally?
I think LLM's have that sort of "summarise, wrap it in a bow tie, give a little dramatic punch as a preview to the next few points".
Guys, LLMs are build on all these social cues which were developed pre-model. There's atleast 10 years of pre-llm gibberish.
This is to say: Marketers and spammers repeat the same things over and over, and these models are build on coalescing repetition into the basis.
So yeah, of course people talked like this before, but it was always in some known context like linked in or a spam website.