Comment by segmondy
2 months ago
Very nice, multi modal, largest open weight model that supports audio. Would be interesting to see how good the audio capability is.
If you want to run locally, checkout https://github.com/danielhanchen/llama.cpp/tree/add-inkling https://unsloth.ai/docs/models/inkling https://huggingface.co/unsloth/inkling-GGUF https://huggingface.co/unsloth/inkling-NVFP4
This supposedly is better than KimiK2.7, as much hype as GLM5.2 gets, I find myself using KimiK2.7 half of the time, so if the benchmark is true, then this can definitely go in the mix. My hope is that it might have strengths in some areas to beat all other open weight models.
Not to mention - it is American. This is the first competitive non-Chinese open weights model since what, Llama 3?
North Mini Code by Cohere (HQd in Toronto) has honestly been very competitive in my personal assessment with many of the models coming out of the PRC. I'd position it below Moonshot AIs and Z.ais recent releases, but above the varieties of Qwen, Deepseek, MiMo, etc.
Depends whether America the continent or just the United States counts of course.
>Cohere (HQd in Toronto)
Cohere keeps changing their story about where they're headquartered. From 2019-2020 they were HQed in Toronto. Then from early 2020 to 2026 they billed themselves as dual-headquartered in Toronto and San Francisco. Then in April 2026 they started to bill themselves as dual headquartered in Toronto and Berlin. Sometime in the last two months they've started to bill themselves as HQed in Toronto again.
Interesting, I didn’t know this existed, do you think it’s competitive with Qwen 3.6 35B A3B which seems to be the closest comparator?
It’s 20 vs 32 in favor of Qwen on artificial analysis intelligence index (cohere isn’t benchmarked on the coding index)
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It's not the better model since Llama3. Trinity Large is American and quite decent, unfortunately tons of crazy good models have been out and it's harder to run locally at 400B. I think Arcee did a terrible job of promoting their model.
https://www.arcee.ai/blog/trinity-large
+1 I enthusiastically use Chinese open weight models for a wide range of tasks (I also love Opus and Gemini) but I am so happy to see another high quality American open model (I consider gemma to be high quality, like qwen).
I enjoyed turning off web search for Inkling to experiment with what innate knowledge is encoded in the model weights. A fun thing I do is check what innate knowledge very large models contain about me, as an individual. Inkling has an interesting concise shadow of what I do. (I have written a lot of books, so I am in training data.)
There is also NVIDIA Nemotron 3 Ultra, with 561B parameters, which was released a month ago.
I do not know yet how smart it is, but the NVIDIA LLMs are very well optimized for fast inference (on their GPUs of course).
Previously that was the biggest American open-weights LLM.
What does it mean, if it is American?
Is it censored or will it eventually stop working in Middle Eastern countries?
Or is it biased towards powerful political lobby group interests?
When weights are open I usually don't care where is it from, as long as it is working for my use cases well
There's a lot of Sinophobia in the tech world, and the AI race seems to be magnifying that. With very little evidence to back it up, I might add - we've had regular releases of quality open-weight models from Chinese firms, and no sign they are more censored/ideological, than, say, Grok
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Mistral has released some very good, very useful open models. It's European, that's also non-Chinese.
Google has released some very good and useful models too, Gemma, but they are not so large. But they are in useful size.
Gemma 4
The largest Gemma 4 model has 31B parameters. It’s not in the same class.
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Isn't that British?
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"Competitive" is doing a lot of work there — it debuts at #41 on the AA index and the post itself calls it not the strongest overall. Mistral and Cohere's North are also non-Chinese open weights, so Llama 3 wasn't the last.
Llama 4 was unfairly hated on. Still the longest context window on any LLM ever (so what if you can't use it properly?) and unironically had decent image capabilities compared to llama3 which had none.
Benchmark cheating aside, it wasn't that bad.
I mean if you don't care it's utilized properly you can make a lot of local models have > 10 million context length. I don't know why you would, the quality is already crappy enough when models are built around using it well from the ground up, but go ahead.
GPT OSS was post Llama 3 and pretty strong at the time. But yeah this is the first seriously competitive non-Chinese open model in a good bit now.
> it is American
It's also–hopefully–run by a cooler head. Altman launching a nuke into his own backside with his "stop me before I shoot grandma" routine was at least novel. Dario repeating the same playbook to the same effect years later still genuinely confounds me.
If Thinking Machines pans out I could see it finding a welcome home at Apple.
> If Thinking Machines pans out I could see it finding a welcome home at Apple.
I was thinking the same thing. Apple's use of Gemini can't be a long-term solution.
There's also poolside.ai
I was blown away by how effective their latest model drop that works with their own coding harness pool is. I tested it extensively with Haskell, Python, and TypeScript for small coding projects. The functionality is good but the inference speed is too slow for most of my work: I would set up a problem, take a walk, then return later to evaluate the results. Note that I have an old mac mini with 32B memory; a fast modern home system would be much better.
EDIT: their business model is interesting, aiming for supporting organizations with privacy and security concerns.
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I was hoping for some relation to poolside.fm but alas. This looks cool though.
nemotron
Or rather, Albanian.
gpt oss was competitive when it cam out
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> This supposedly is better than KimiK2.7
How can you tell?
I just looked at the benchmarks and was kinda disappointed that it seems to be between KimiK2.6 and KimiK2.7 on most of the benchmarks.
Do you refer to what it feels like to use the model? Or are there other benchmarks I haven't seen?
Most of the random comments you read on HN and reddit about how nice/bad various LLMs are, is basically based on the commentator's "vibe" about it, and almost nothing is grounded in evidence or actual usage. Don't read too much into it, want to know how good a model is? Run it with your own non-public benchmark, basically the only way to get proper answers you can somewhat rely on, everything else is manipulated, misunderstood or over-relied on.
I thought HN was different. And yeah, wherever I go, my timeline is full of Opus is so bad today and I will switch from Fable to 5.6 Sol, it's 1.5x better and vice versa.
Non-public benchmarks (ideally suited to one's own use case) are probably the best way to judge, I agree.
I also have been preferring kimik2.7 more than GLM5.2. I'm interested to give this a try.
Where are you getting supposedly. It does worse in most benchmarks
Oh thanks for sharing! The llama.cpp PRs should generally be fine for now - I'm fixing a few small edge cases as well!
I'm sure it's better than KimiK2.7 and GLM5.2. Benchmarks aren't the full picture. Despite GLM5.2 performing well on benches and supposedly near frontier, in reality it was nothing close to frontier in actual usage.
Have you used Inkling enough to be able to tell? Or how can you be sure? Please add some substance before making such claims
What harness do you use for Kimi?
Not OP, but I tend to use Kimi 2.6/7 more than other models, works great with omp.