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Comment by paxys | Hacker News Reader

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)

It is surprisingly capable for coding despite its tiny size.

You can try it out for free:

https://opencode.ai/docs/zen/#pricing

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

The Chinese are quite open about their efforts regarding intellectual espionage and the desire for control of other countries' supply chains. It's not sinophobia to be cautious about it. It doesn't mean that the US companies are angels either.

If the company does any kind of work with the US Government it's far easier to just say no to Chinese-origin models rather than deal with all the overhead on the government contracts. Since just about every major tech company has US contracts they all have to be careful about the use of Chinese models.

Only actualy censored thing i found so far was Tianmen Square. And depending on what you use it for thats realy not a big issue.

Any discussion about the Chinese Communist Party is censored on Deepseek.

Interesting didnt know. Expected but not great. Though depending on the application workable I would say.

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.

I think there's two halves to the conversation: which models have more weights and which models are better than the other ones listed. I think this was about the latter part. There are plenty of smaller models these days which knock the socks off older models 10x the size.

We are talking about the top tier of open weights models - GLM-5.2, DeepSeek V4, Qwen3, Kimi K2. The ones ranking on leaderboards and giving frontier labs a run for their money. Gemma may have its uses but it is not in that conversation.

I find it easy enough to interpret as talking to "a better non-Chinese model since Llama 3", even if it doesn't compete with the current large Chinese models mentioned before that. Again, they aren't saying you're wrong about large models - they are adding that Llama 3 was separately surpassed by smaller non-Chinese models during the wait.

Isn't that British?

British-American with much of the research happening in London. I don't know if it's known what team specifically worked on which of the Gemmas, I'd suspect it's a healthy mix of multiple satellites across the globe, but the contribution by the UK Deepmind parts likely was significant, certainly not so small that it'd justify downing a regular question.

"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.

Yeah, they’re focused on selling to government and defense contractors. I first encountered poolside at my aerospace internship.

That is a nice business niche, but I would hope that companies might also care about security. There are just a few inference providers who I trust to not save data outside the scope of maintaining inference cache, but I do use untrusted providers sine everything I work on is open source or open content.

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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