5 ms·
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 local
by segmondy 3mo 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://github.com/danielhanchen/llama.cpp/tree/add-inkling
https://unsloth.ai/docs/models/inkling https://unsloth.ai/docs/models/inkling
https://huggingface.co/unsloth/inkling-GGUF https://huggingface.co/unsloth/inkling-GGUF
https://huggingface.co/unsloth/inkling-NVFP4 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.
- danielhanchen 3mo agoOh thanks for sharing! The llama.cpp PRs should generally be fine for now - I'm fixing a few small edge cases as well!
- dimgl 3mo agoWhat harness do you use for Kimi?
- rpdillon 3mo agoNot OP, but I tend to use Kimi 2.6/7 more than other models, works great with omp.
- paxys 3mo agoNot to mention - it is American. This is the first competitive non-Chinese open weights model since what, Llama 3?
- whimsicalism 3mo agonemotron
- segmondy 3mo agoIt'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 https://www.arcee.ai/blog/trinity-large
- oehtXRwMkIs 3mo agoGemma 4
- jansan 3mo agoIsn't that British?
- Topfi 3mo agoBritish-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.
- paxys 3mo agoThe largest Gemma 4 model has 31B parameters. It’s not in the same class.
- zamadatix 3mo agoI 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.
- paxys 3mo agoWe 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.
- zamadatix 3mo agoI 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.
- Topfi 3mo agoNorth 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.
- andy99 3mo agoInteresting, 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)
- throwa356262 3mo agoIt is surprisingly capable for coding despite its tiny size. You can try it out for free: https://opencode.ai/docs/zen/#pricing https://opencode.ai/docs/zen/#pricing
- AlanYx 3mo ago>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.
- Der_Einzige 3mo agoLlama 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.
- zamadatix 3mo agoI 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.
- deleted 3mo ago[deleted]
- ascent817 3mo agoThere's also poolside.ai
- NetOpWibby 3mo agoI was hoping for some relation to poolside.fm but alas. This looks cool though.
- mark_l_watson 3mo agoI 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.
- ascent817 3mo agoYeah, they’re focused on selling to government and defense contractors. I first encountered poolside at my aerospace internship.
- mark_l_watson 3mo agoThat 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.
- JumpCrisscross 3mo ago> 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.
- malshe 3mo ago> 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.
- throwaw12 3mo agoWhat 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
- swiftcoder 3mo agoThere'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
- mastermage 3mo agoOnly actualy censored thing i found so far was Tianmen Square. And depending on what you use it for thats realy not a big issue.
- Saline9515 3mo agoAny discussion about the Chinese Communist Party is censored on Deepseek.
- mastermage 3mo agoInteresting didnt know. Expected but not great. Though depending on the application workable I would say.
- smiths1999 3mo agoIf 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.
- 3mo ago
- berkes 3mo agoMistral has released some very good, very useful open models. It's European, that's also non-Chinese.
- kzrdude 3mo agoGoogle has released some very good and useful models too, Gemma, but they are not so large. But they are in useful size.
- daniela-scott 3mo ago"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.
- el1s7 3mo agoOr rather, Albanian.
- z0ltan 3mo ago[dead]
- adrian_b 3mo agoThere 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.
- mark_l_watson 3mo ago+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.)
- jjice 3mo agoGPT 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.
- ekianjo 3mo agogpt oss was competitive when it cam out
- mistahchris 3mo agoI also have been preferring kimik2.7 more than GLM5.2. I'm interested to give this a try.
- Bolwin 3mo agoWhere are you getting supposedly. It does worse in most benchmarks
- juliangoetze 3mo ago> 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?
- embedding-shape 3mo agoMost 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.
- juliangoetze 3mo agoI 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.
- nullbio 3mo agoI'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.
- juliangoetze 3mo agoHave you used Inkling enough to be able to tell? Or how can you be sure? Please add some substance before making such claims