281–290 of 1001 posts
2.8T param open model, 1M context, native vision. Weights releasing by July 27 with technical report. Launching with max thinking effort by default; low/high effort modes coming in future updates.
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Fable is by Anthropic, and this is too expensive, GLM 5.2 is roughly the same quality at a much cheaper price. (I mantain a client with llama.cpp and 101 models across 14 companies by http)
As much as I like GLM 5.2 it's clearly a step below Opus (or even Fable) for more complicated tasks. I would place it at Opus 4.6/4.7 level. Having said that, the safety system on Fable makes it an extremely unattractive model. It feels that half of the time you're paying double for Opus level performance.
Some official benchmark numbers posted in Chinese social media (I am sure they will publish an English blogpost later too): https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ Generally looks like a Sol/Fable tier model, better across the board than Opus 4.8. (Edit) English blogpost is up now: https://www.kimi.com/blog/kimi-k3
Given the pricing, it suggests that this model is much more efficient/competent than previous-gen OS/distilled models.
Imagine you're a mid sized company and you can host this model locally. Suddenly there are zero reasons to pay a single red cent to the bloodsucking American AI cartel.
Can you host the model for a lower cost per token than you'd pay Anthropic or OpenAI for a similar level of intelligence? I doubt you're beating their efficiencies of scale.
Ok you can host this model once. What if I want a dozen subagents? Ok you can host it 12 times at once. What if we go a whole week only using max 4 at a time? Etc etc. The limits imposed by self-hosting might be bearable for a variety of reasons, but it's going to be more expensive and less convenient/useful.
On the first try, Kimi K3 just found the source of a bug that Fable 5 hasn't been able to pinpoint in multiple attempts. It's just one anecdote, and I haven't used K3 much yet, but so far it's looking extremely promising.
How do you use kimi for agentic tasks? I'm used to claude code & codex extensions for vs code, but recently switched to codex cli w/ vim keybinds. Does something like that exist for openrouter?
This is entirely for personal use and small projects. I don't have huge needs. I get access to gpt models via my employer for work things. But I'm also using pi with those models.
[2]: https://pi.dev/
Pelican: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... - rendered via the OpenRouter API: https://openrouter.ai/moonshotai/kimi-k3 95 input, 16,658 output = 25 cents! https://www.llm-prices.com/#it=95&ot=16658&ic=3&oc=15 (13,241 of those were reasoning tokens.) I think that's the most expensive pelican I've rendered through a Chinese model so far.
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This is weird and reactionary. Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns. Anthropic/american models aren't going anywhere anytime soon.
Nope, but I think this is maybe the critical mass needed to finally crash the AI hype/datacenter cost problem everyones is talking about. With Oracle being junk before this, more will follow.
Now they are betting with Project Stargate but it also seems to be crumbling down.
But don't forget that they literally hold the biggest databases, both in commercial and open source, that is, Oracle Database and MySQL. Plus Oracle Java they literally controls at least 30% of the internet's software infrastructure.
And also with a good team of attorneies enforcing the licenses, they can squeeze so much money at the cost of morality.
Also recently they downgraded the always free OCI ARM instance from 4C24G to 2C12G without telling anyone.
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Tokenizers define the alphabet on which the language model is trained. I don't want people to get the impression it's a module which can be swapped out or modified on its own. Alphabet size is a design consideration related to correctly encoding the training data.
That's true, but it makes it difficult to compare pricing when it's based on tokens. Maybe we need a benchmark for price per a specific input, like enwiki8.
Very interesting to see how Gemini 3.5 Pro stacks up against this new wave of models. Hope they have something similar to a Gemini 3.1 moment soon. Their speciality has always been math and multi modal intelligence and the new models are recently all very coding focused.
(Not posting link coz paywall)
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They will release the full weights by 7/27 along with support in vLLM. Source: their release blog on WeChat. https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ
>We are currently working closely with our inference partners and open-source maintainers to align the technical details and ensure the model can be reliably deployed across the ecosystem. The full model weights will be released by July 27, 2026. Further details regarding the architecture, training, and evaluation will be released with the Kimi K3 technical report. (translated by chrome) 11 days is a long time. It do…