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Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example. That said, Kimi is compe
by dghlsakjg 3mo ago
Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example.
That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.
- asenna 3mo agoWith that kind of pricing, I don't think they're competing with GLM with this new launch.
- leecommamichael 3mo agoTokenizers 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.
- smallerize 3mo agoThat'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.
- leecommamichael 3mo agoYes, almost all work people share which seeks to measure the capabilities and differences of models needs to get more precise. We are clamoring to say something meaningful about these things.
- victorbjorklund 3mo agoIt is kind of a shame we ended up comparing token pricing across models and providers when it doesn’t really make sense. Not sure what would be better though.
- whoopdeepoo 3mo agoWell isn't that what benchmarks are for? They compare total cost for a unit of work.
- alain94040 3mo agoUse price per page (standard English text)? That would also help make the metric easier to visualize. If you think a page is too vague, use a famous known writer's work as a reference.
- whodatbo1 3mo agoA better metric is price per byte. Most thinking traces, prompts, skills are in plain English, which is roughly 1 byte per character, assuming UTF-8 encoding (even code should not be much more either). As an aside, it is common to use bits-per-byte as a loss metric instead of the per token calculation, precisely because of the effect of different tokenizers.
- smallerize 3mo agoIt's going to vary dramatically based on which text you put in. Really it's hard to make one benchmark number that's relevant to all cases. But maybe we can make something a little more specific, like regular English text, code, the model's own thinking tokens, image inputs etc.
- kevincox 3mo agoBut even that isn't the whole story because the models can produce wildly amount of thinking output as well as regular output for a similar query. Sometimes you can take a cheap model and have it think a ton or an expensive model that thinks little and get similar results. But the number of tokens generated will be wildly different.
- deleted 3mo ago[deleted]
- semiquaver 3mo agoI’ve been struggling to understand the reason for the newer apparently less efficient Anthropic token encoding. If all inputs are less efficient in this encoding, why does it exist? Has Anthropic released any information that would convincingly show it was anything other than a stealth price hike? Please don’t respond if you are speculating.
- remus 3mo ago> Please don’t respond if you are speculating. I doubt you are going to get a response from an anthropic employee, but I think it is safe to assume they have swapped to a new tokenizer because it improves the performance of their models.
- re-thc 3mo ago> the reason for the newer apparently less efficient Anthropic token encoding Less efficient in token usage but per the blogs; it enables the model to perform better.
- mnicky 3mo agoIt's really simple I think. More tokens per same text length means more capacity to encode information. More information means model can potentially perform better. They introduced it around the time the Mythos came so my speculation is that if you have more capable model at some level you may find the current information encoding not using its full potential. We will see whether OpenAI also introduces new tokenizer when they come to Mythos-size models.
- cmrdporcupine 3mo agoGLM is actually quite expensive in actual practice because it's not very token efficient. I've yet to find a way to run it on a monthly sub reliably for cheaper than Codex. Neuralwatt was cheap (but slow) but they cranked their price. Ollama monthly sub is speedy but doesn't offer a lot of quota. Right now unless you're paying by the token, there's no cost based reason to use the open weight models for daily coding work because the monthly coding plans from Anthropic and OpenAI are a better deal.
- computerex 3mo agoI know GLM is relatively expensive and so is Kimi, in comparison to those DeepSeek V4 pro and flash are a godsend and are absolutely good value.
- arizen 3mo agoAnd DeepSeek V4 Flash + GLM 5.2 is a really good blend of both (fast/cheap DS + more intelligent GLM)
- calgoo 3mo agoExactly, I design in glm 5.2, and then build with deepseek pro. I pay deepseek by token count, and honestly I added $20 and it just keeps going, hardly burning anything.
- versteegen 3mo agoI think MiMo 2.5 seems to be better than DS4 Flash, at the same price. DS4F can write pretty advanced code but it way overthinks the simple stuff, its CoT is full of errors (immediately corrects itself), and yesterday I was shocked it reliably struggled to spot a misplaced % in a format string when reading a whole file.
- pimeys 3mo agoI use V4 flash as my personal agent. It categorizes documents, organizes my calendar, searches information etc. for pennies. Amazing model. Not very good for programming though.
- mdasen 3mo agoIt also depends on how many tokens it needs to burn through to accomplish something. At this point, I always look at things like Artificial Analysis' total cost to run their tests. It'll take into consideration the cost of tokens, how many tokens it burns through, and how effectively it uses caching (and the price of that caching). If a model "costs the same" but its reasoning ends up going through a ton more tokens, it doesn't really cost the same in real world usage.
- KennyBlanken 3mo agoPrecisely. GLM 5.2 Thinking is pretty damn good - but it regularly does something nonsensical. Or even spits out what looks like a fragment of its memory cache. Or returns a bunch of Chinese. I find myself having to resubmit a query very often...so it being a third of the cost of other AIs isn't really relevant.
- zvikara 3mo agoI believe Kimi is spending more on marketing than GLM (a lot of ads lately) so I guess that's part of what the higher price supposed to cover.
- InsideOutSanta 3mo ago> That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price. Having used GLM 5.2 extensively and K3 for a few hours now, these models are nowhere near each other. 5.2 is a great model, and I use it for a lot of things, but it's noticeably below Opus 4.8 or GPT-5.5 in real-world usage. K3 is in the same ballpark as Fable or Sol.
- wmedrano 3mo agoWhere does the less than a third the price come from? From the provided benchmark suite, it came slightly under half.