8 ms·
Kimi K3-256k
- timcobb 3mo agoCodex uses 256k masterfully, 1M is luxurious but still quite expensive and seems not necessary as a default.
- jsemrau 3mo agoNeeding large context windows is an illusion.
- TranquilMarmot 3mo agoNeeding large context window is a skill issue
- k__ 3mo agoWhile I tend to clear my session after every task, I feel less stressed when my context is as 10% than when it's at 30%
- vehemenz 3mo agoSo, all types of knowledge work and coding require the same amount of context? This seems like a hot take not really informed by experience.
- Tostino 3mo agoI'll have to give it a shot some time soon. I just can't imagine working on any of my serious projects doing a new large feature with that little room. By the time it has looked at half the code required to start planning the work, it'd be out of context. Must use sub agents better or something.
- cybersec47 3mo agoAlso, a token means something different in each model/tokenizer. "For improved performance we will use (read: invoice you) 30 % more"... https://platform.claude.com/docs/en/about-claude/models/whats-new-sonnet-5#new-tokenizer https://platform.claude.com/docs/en/about-claude/models/what...
- timcobb 2mo agoCodex turns over 256k windows for *days/weeks on end* without seemingly much fidelity loss, especially with a goal that anchors it to a durable plan/ledger.
- cybersec47 3mo ago> Codex uses 256k masterfully This. When working on hard problems (not "vibecode me a script to show an alert box", but e.g. "let's see what this three-level LUT-state-machine obfuscated binary does"), hitting the 1M (!) context window with Claude Code feels like you were talking to Claude Claudewski when his shift just abruptly ends, he packs his things, throws the office keys at Claude Claudeson in-between the front door frame while handovering like "Hi! Nice to see you, good luck." and now here we go again, you are working with someone who just experienced an acute amnesia. It tries everything it already tried, everything it was told in the initial prompt to not do, everything it was told in follow-up prompts not to do. "You were right, this approach does not work and we don't have 20 TB RAM on this machine for full symbolic execution, let me try..." In Codex, it's so seamless that I sometimes just notice "wait, the context was 20 % remaining, it is 70 % now, wow, when did this happen", while it seamlessly works on the task. Basically never had an issue with context on Codex, be it coding features, cracking hard crack-me ciphers, or researching basically anything. (For full disclosure, my last experience with Claude was a few weeks ago when I cancelled the subscription, maybe they fully reworked the traumatic "Summarizing" - "Oh, hi! Where are we? Who am I? What we are doing? This is taking too long, let me take a shortcut..." lobotomy they were doing in the meantime.) 256k is enough when the harness uses it properly and the model is not stupid. And also when the tokenizer is not tuned to invoice as many tokens as possible...
- Mastodo 3mo ago[flagged]
- erichocean 3mo agoThat's because it has the best compaction of anyone, and it's not even close. Claude might as well not even do it in my experience.
- madihaa 3mo ago[flagged]
- giancarlostoro 3mo agoFor me the sweet spot is somewhere under 500k depending on how extensive I want to get. You can build up a sizable effort project in half a million tokens with Claude, with Claude having all the context from ground 0 to wherever you're off at.
- cyanydeez 3mo agoI'm always curious what you guys are working on; every git repo I've run a local model on and stick below <100k to increase speed seems effective enough to scope patches and changes.
- jdoe1337halo 3mo agoThey are just talking to the model in CC, while staying in a single thread. Doubt they have any actual coding knowledge to compartmentalize different problems in the codebase.
- giancarlostoro 3mo agoDepends on the programming language I'm using for a given project, and the domain I'm working with. I've been coding as a hobbyist for nearly two decades now (since my teens), professionally for 9 years, and was a TA before that for roughly 3 years at one of the best colleges for this field in the state (at least back then it was) where I taught other students about programming, in some cases I was their primary learning resource. But yeah, I have no idea about anything about software because you made an assumption off very little to go by.
- KronisLV 3mo agoMy current Claude Code session has been going on for like 35 hours and has used up around 400 million tokens, thankfully almost all of those being cached (95-98%) - pretty typical for long form agentic work. First you spend like 2-3 hours working on a plan, once you have that you just tell the model to go and implement it, do adversarial sub-agent review loops before each commit and also make sure that all tooling and tests pass (including coverage requirements). You do need to poke it in a slightly different direction every few hours, though. Not even any novel work, just some refactoring and SSE notification hardening, bug fixes, alongside environment tuning and getting rid of some bottlenecks (also migrated from Oracle to PostgreSQL but that's mostly done). That said, Kimi somehow manages to use less context in the main thread than Anthropic's models (even when you use sub-agents and also dynamic workflows in Claude Code), might have something to do with either how the model is tuned or their Kimi Code harness - because even in most of the longer form sessions it doesn't seem to fill up quite as quickly (note: because the kimi vis tool doesn't have a full summary view across all agents, these are the main long running agent stats across some sessions, not sub-agents): total tokens cache hit rate wall time peak context 283M 98% 3963m 466k 258M 97% 2724m 467k 98M 94% 1353m 393k 67M 97% 614m 434k 75M 98% 1447m 498k 53M 99% 191m 375k 6M 96% 139m 124k 7M 98% 86m 118k 11M 99% 61m 147k I could see 256k context being sufficient for all sorts of work, even if intermediate progress/plan tracking files and docs might have to be used along the way, in addition to whatever plan support the harness has (for example, if you document something that will be relevant for load testing you might need that in 10 turns but not during the ones before then).
- superloika 3mo ago[flagged]
- HawtAds 3mo agoThis is just an API level change right? The model itself should be the same I think.
- k__ 3mo agoAs I understand it, they would have to train a whole knew model to hard cap it's context to different lengths. That would be cheaper to train and had cheaper inf, but still a huge investment. So I'd guess it's API level.
- gpugreg 3mo agoFrom the Kimi K3 technical report: Kimi K3 supports a context window of up to 1 million tokens. We achieve this through extending the context window progressively as training proceeds, following a four-stage curriculum. The window grows from 8K to 64K tokens during pre-training, and from 256K to 1M tokens during the cooldown phase. https://arxiv.org/pdf/2607.24653 https://arxiv.org/pdf/2607.24653 page 12
- dannyw 3mo agoYou absolutely don't need to 'retrain' to reduce your context window. In vLLM it is an inference parameter. Smaller context window, smaller KV, less RAM needed to serve the same volume of requests. With a lot of architectures, you technically don't need to retrain to extend the context window either; e.g. RoPE scaling; but performance is typically crap.
- wxw 3mo ago> k3-256k is now available. Within 256k context, it delivers the same results. k3 (1M) consumes about twice as much quota as k3-256k.
- dgritsko 3mo agoThis isn't quantized, right? Just a smaller context?
- DSingularity 3mo agoIts 256k context window. Quantization is orthogonal. We cant really tell directly so it could be quantized.
- gpugreg 3mo agoThe model is already natively MXFP4-quantized during training, so there is no quality loss.
- ibuildproducts 3mo agoomg! new model!!
- Alifatisk 3mo agoSame model, new configuration
- lukan 3mo agoSince Claude is the first time for me really, really out (TIL against my wished about https://status.claude.com/ https://status.claude.com/), I am now interested enough to see what else works. But ... when I click pricing, I see "Join a waitlist". Wtf? Are they really that good, so were totally surprised and overwhelmed by the requests, is this a marketing stunt, or do they just don't have the hardware being in china?
- MYEUHD 3mo agoSince the model is open-weights, you can get from other providers, for example see https://openrouter.ai/moonshotai/kimi-k3#providers https://openrouter.ai/moonshotai/kimi-k3#providers
- Alifatisk 3mo agoThe only downside with third party providers is that you have to trust that the provider have setup and configured it correctly, and is not secretly quantizing it. See Kimi Vendor Verifier.
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- usef- 3mo agoPlenty of providers on that list aren't fly-by-nights and have easily proven themselves in past models. Jeremy Howard does demos on Fireworks. A nice thing about open router is you can specify filters, like "US hosting without data retention"
- applicative 3mo agoYes, something I read just before it was released suggested that, because of the unique features of the model, there was a back and forth of Hugging Face, Moonshot and providers like Together AI and Fireworks AI. This also explained why it took much less than a day for Together, Fireworks etc to appear. Whatever they are doing is what Moonshot wants, I think.
- illithid0 3mo agoThis was posted 38 minutes ago, and as of 20 minutes ago, several Anthropic services are now designated as having a "major outage". Doubt these are related, but it made me laugh a little.
- paxys 3mo agoAnthropic services have outages on all days ending in y.
- de6u99er 3mo agoSo users in Germany are not affected?
- zarmin 3mo agoGermany ends in y
- tshaddox 3mo agoNot in Germany.
- hebelehubele 3mo agoThe word "Germany" is "Germany" everwhere and always ends with y. The country Germany has different names though.
- sergiotapia 3mo agoI can't seem to find pricing for this model. Since the context size is just a quarter of the full size K3, is the price also much cheaper? I usually keep my context in chats below 256k anyways so this would be tremendous honestly.
- daemonologist 3mo agoIt seems to only be available in Kimi Code, via subscription, no there's no API pricing. The linked page says it consumes about half as much quota as the 1M version though.
- trollbridge 3mo agoIt's not available via API (yet).
- hendersoon 3mo agoWhat is the purpose of this? Just a hard cutoff below the actual context window? You could set that in your harness anyway.
- surgical_fire 3mo ago> k3 (1M) consumes about twice as much quota as k3-256k Cheaper?
- chrisweekly 3mo agok3-256 consumes half the quota, so... yes? (assuming the user isn't making use of the larger context window)
- dnlzro 3mo agoUses less quota (i.e., cheaper). For people who like to keep their contexts small, this is a no-brainer.
- meatmanek 3mo agoAt least in the self-hosted LLM inference engines, you have to pre-allocate space for the maximum amount of context you want to allow for each parallel session. By using a lower maximum, you don't have to allocate as much VRAM for each session, allowing more usage for the same amount of hardware. Thus, cheaper.
- holoduke 3mo agoA bit of topic. But how likely is it that the US will restrict Chinese open weight models and also force Euro countries to do the same? I think it will be effective within 6 months. The US is having a hard time staying competitive.
- jeppebemad 3mo agoI don’t know, but I do think that the days of the US “forcing” Euro countries to do anything, is over.
- holoduke 3mo ago[flagged]
- realusername 3mo agoTrump burned a lot of bridges in the EU, that one will be a hard sell
- impossiblefork 3mo agoIt isn't legally possible for them to do this at the EU level. The EU parliament would never vote for it. For pressure at the country level leading to this kind of thing I think it's very unlikely. Here in Sweden it wouldn't just require a vote in the Swedish parliament and before this there'd have to be förarbeten and you can't just brazenly push things through with insane arguments, Swedish social convention goes against it-- and there's just no way to get it through. It also might not even be legal. "We aren't at war with China and I'm a communist, and the US LLMs are so aligned with values inimical to my political ideology that this is interference with opinion formation" might be an actual legal argument that the ECHR or CJEU might actually have to accept.
- holoduke 3mo agoIt happened with ASML. What's your thought on it? The US forbid dutch company to export.
- jedisct1 3mo agoThis is a fantastic option for swival.dev given its very efficient context management compared to e.g. Claude Code.
- periodjet 3mo agoWhy are Anthropic and OpenAI even allowing their coding harness apps to be plugged into different model providers…? I’m surprised they haven’t figured out a way to clamp down on that by now.
- wren6991 3mo agoI'd guess because it costs them nothing and it gives you a smoother transition back towards paying for their products.
- auspiv 3mo agoWell when a huge part of potential revenue is all in on Bedrock... you need the harness to be able to talk to Bedrock. And Vertex. And all the other places these models are hosted. And allow for proxy because many businesses do not all direct internet access... all valid business reasons.
- archildress 3mo agoFrom my view, as soon as they do that, they send people out the door to use Opencode instead - and once many people have a taste of trying every model via Openrouter, it's eye opening as to the possibilities. Of course - Anthropic and OpenAI have an advantage in the amount they can subsidize the usage, but I think those days are waning.
- HDBaseT 3mo agoIt honestly should be easier, its a by product of everyone using the same API standard. Codex and Claude require editing a .json file, but most other harnesses have direct connections via a /provider or /login command.
- wren6991 3mo agoThis seems functionally similar to OpenAI having a step in pricing once you exceed a certain context length (also at 272k aka 2^18 aka 256k). Having a lot of active context increases the per-token cost (flops issued and bytes read per token out) so it makes sense to pass that cost on to users. I'm actually surprised it's implemented as a hard cutoff instead of a smooth gradient.
- BoorishBears 3mo agoNot surprising it's a hard cutoff: they almost certainly have two infrastructure configurations for the two max sequence lengths Fewer nodes dedicated to prefill per instance, and fewer nodes in total since you don't need to support a higher KV cache. Disaggregated inference also means they can tune the balance of compute dedicated to prefill seperately from decode
- MrBuddyCasino 3mo agoAI infra buildup is so massive that the frontier labs should be able to offer more than one level of context length to incentivize token thriftiness. One would think compute-constrained actors like Anthropic would have done so, unless prefill isn’t really a bottleneck compared to decode?
- NoahZuniga 3mo agoNo, the main issue is that it's hard to communicate pricing where token price increases as token count increases, and they figure they can approximate the parabola well enough with two lines.
- anuramat 3mo agowhy can't they just increase the price for cached input instead?
- dannyw 3mo agoThere's the infra cost of having multiple SKUs. If you create 3 buckets of inference pods, say, 256k, 512k, and 1M, then you have to worry about filling/dynamically-scaling all of them. And my guess is there's probably not a huge amount of customers that want somewhere in between: if you're willing to pay the long context surcharge; you're probably semi-price-insensitive anyway to just use 1M.
- xyzsparetimexyz 3mo agoWow. So kimi is suddenly half the price for all users until they hit 256k of context? Thats massive.
- InsideOutSanta 3mo agoI don't think so. This is a separate model, so I assume that if you just use this and switch to the 1 million context model when you reach 256k, your cache will be invalidated, so you'll re-pay the 256k tokens on the 1 million context model pricing. Edit: I was wrong, thanks to longwave for pointing this out. It's absolutely possible to start out on the 256k model and then switch to the 1 million model when you get close to the context limit without invalidating the cache: "When switching from k3-256k to k3 (1M), if k3-256k is close to the 256k limit and you don't want compact to lose information, you can switch directly to 1M. The current version switching from 256k to 1M does not affect the cache."
- longwave 3mo agoThe article explicitly says "The current version switching from 256k to 1M does not affect the cache."
- conradludgate 3mo agoAs far as I understand, no. They're suggesting that smaller context windows are typically cheaper (fewer input tokens over time).
- StopTencent 3mo ago[flagged]
- fumber 3mo agoCan't find the videos/articles but some people tried it and found that the price per token was only half the story. It seems that it uses a lot more token, coming back to similar prices with other models.
- jscott817 3mo agoCan we assume that model performance at 90% of the 256k limit != 90% of 1M token limit? Is this the exact same model just with less VRAM allocated for context window?
- 3836293648 3mo agoNo, you can't assume it. You can trust them as they made that claim outright. Or you can choose to not trust them, I guess
- dools 3mo agoHopefully this helps reduce some of the pressure on their infrastructure. Their models have all become super dumb recently and their support are not addressing it. I have a hunch they’ve been serving a significant percentage of requests with quantised models.
- stingraycharles 3mo agoAh come on, HN is not the place for these types of unfounded conspiracy theories that keep popping up on Reddit.
- dools 2mo agoIt’s a hunch. You think it’s outside the realm of possibility? How else do you explain wild swings in capability?
- MangoCoffee 3mo agoLLMs is quickly became commodities. US AI labs like OpenAI is losing their moat. Hyperscalers and data center owners who can sell cheap token will win
- okrad 3mo agoI believe Codex harness is pretty sticky though I haven’t tried many others. Does anyone else provide a harness of that quality?
- conradludgate 3mo agoTheir harness is indeed nice, but we're using it against our own AI Gateway at work. Right now we only expose GPT models on it, but I imagine it's possible to add our own models eventually
- nvarsj 3mo agoPi harness with subagents is pretty great. I’m using it for everything now.
- yeswecatan 3mo agoHow are you handling subagents?
- anderber 3mo agoUsually the main agent can handle creating subagents
- RALaBarge 3mo agoYou say hey llm spin up some agents to do x y and z
- nvarsj 3mo agotintinweb/pi-subagents. Then I use superpowers and it spins up subagents automatically. Works really well and you can customise the models and efforts you want to use with local AGENTS.md. I’m primarily using gpt-5.6 and then opus for reviews.
- try-working 3mo agoi've never had any issues with 256k context. see no reason to bump up to 1m if it comes at a premium.
- trollbridge 3mo agoIt's pretty handy for have very long contexts for long running agents, or else when doing literary analysis to simply be able to load the entire book in.
- TranquilMarmot 3mo agoUsing Claude/ChatGPT I rarely hit >256k context for most basic coding tasks. Sometimes if I need to do something more "intense" (bigger refactors, new features) the 1mil window is nice.
- gigatexal 3mo agoNot relevant to this link but I was thinking about the allegations of Chinese AI companies distilling from the big frontier American ones. And I came to the conclusion: I don’t care. Who cares? China has always copied and then copied the means of production and then out produced. See also Tesla and now all the Chinese cars eating their lunch. As long as I get really solid AI models for cheap that do what I need I don’t care if they’re Chinese or otherwise. I’ll still never use Grok from SpaceX AI cuz eww no, I have principles. ;-)
- 1saadcodes 3mo agoIt feels like we're moving away from "bigger context is always better" toward "right-sized context". I love this not just because my wallet feels safer but because most of my coding sessions never come close to needing 1M tokens anyway
- Conol_ai 3mo ago[flagged]
- Conol_ai 3mo ago[flagged]
- effnorwood 3mo agoMy LOE to make this work expressed in kWh is substantial but user is impressed. Eliza has hands now.
- fumber 3mo agoI was so excited that it is open source until i realised the model required 1.5To of VRAM. Unsloth has compressed it in 1bit at about 570gb VRAM with 75% accuracy, that's almost mac studio territory...
- nateb2022 3mo agoYou can run DeepSeek v4 Flash at 4bit in ~150 GB VRAM, and it would absolutely destroy a 1 bit quant of K3.
- AmazingTurtle 3mo ago75% quality... it gets worse the longer the context.
- camgitt 3mo ago[flagged]
- TokenLat 3mo ago[flagged]
- SwellJoe 3mo agoI make a point of never going beyond about 220k, unless absolutely necessary (and it's almost never necessary), anyway, even with models that degrade more slowly, so this is just a discount.
- johnbarron 3mo ago64k ought to be enough for anybody
- adamtaylor_13 3mo agoWhy stop there? No true developer needs more than 32k! -- Jesting aside, I cannot think of a single session in recent memory that used less than ~70k tokens, so I presume you are joking.
- gosub100 3mo agoIt was from a famous quote supposedly from bill gates in the early days of computing: "640K (RAM) ought to be enough for anybody". Apparently it was taken out of context or misattributed or something. Similar to "Al Gore said he invented the internet"
- gck1 2mo agoHeck, agents don't start editing before they're already at 70k for me. I've played with explorer agents giving exploration summaries to help the implementer agents use more of their context for implementation, but it doesn't work as well. There's always something lost in the handoff.
- Computer0 2mo agoMy understanding is that models can suffer “context limit anxiety” and a higher context limit will make it perform better especially as the context expands
- Roark66 3mo agoThis is very cool that they open weight it. Are there any EU providers serving it yet?
- denis-c 3mo agoWe are trying https://tensorx.ai/ https://tensorx.ai/ as they have good model selection and ZDR, but seem new so we will see. Performance so far is good.
- L-patpat 2mo agoWhen I first built features with GLM, there were lots of bugs, and it took me ages to fix them manually. Now the features implemented with GLM 2.0 have almost no critical bugs after testing. I can’t even imagine how capable K3 will be. It may well be on a level that ordinary people cannot access.