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This is perfectly legitimate. It's something I've been denouncing day after day. Company X charges you 10dolar per token, while company Y charges you 7dolar, yet company X is cheaper because of the tokenizer they use. The token consumption depends on the tokenizer, and companies create tokenizers using standard algorithms like BPE. But they're charging for hardware access, and the system can be biased to the point that if you speak in English, you consume 17% less than if your prompt is written in Spanish, or even if you write with Chinese characters, you'll significantly reduce your token consumption compared to English speakers. I've written about this several times on HN, but for whatever reason, every time I mention it, they flag my post.
I have often wondered if Chinese is a much 'better' language for LLMs - every character is a token, boom you're done. No weird subword nonsense, no strange semantics being applied to arbitrary chunks of words.. I feel like there must be benefits to being able to have the language tokenized in what must be very close to 1:1.
> Opus 4.7 tokenizer used 1.46x the number of tokens as Opus 4.6
Interesting. Unfortunately Anthropic doesn't actually share their tokenizer, but my educated guess is that they might have made the tokenizer more semantically aware to make the model perform better. What do I mean by that? Let me give you an example. (This isn't necessarily what they did exactly; just illustrating the idea.)
Let's take the gpt-oss-120b tokenizer as an example. Here's how a few pieces of text tokenize (I use "|" here to separate tokens):
Kill -> [70074]
Killed -> [192794]
kill -> [25752]
k|illed -> [74, 7905]
<space>kill -> [15874]
<space>killed -> [17372]
This is not necessarily an ideal way of encoding text, because the model must learn by brute force that these tokens are, indeed, related. Now, imagine if you'd encode these like this:
<capitalize>|kill
<capitalize>|kill|ed
kill|
kill|ed
<space>|kill
<space>|kill|ed
So I wouldn't be surprised if this is what they did. Or, my guess number #2, they removed the tokenizer altogether and replaced them with a small trained model (something like the Byte Latent Transformer) and simply "emulate" the token counts.
There is currently very little evidence that morphological tokenizers help model performance [1]. For languages like German (where words get glued together) there is a bit more evidence (eg a paper I worked on [2]), but overall I start to suspect the bitter lesson is also true for tokenization.
LLMs are explicitly designed to handle, and also possibly 'learn' from different tokens encoding similar information. I found this video from 3blue1brown very informative: https://www.youtube.com/watch?v=wjZofJX0v4M
Also, think about how a LLM would handle different languages.
This is how language models have worked since their inception, and has been steadily improved since about 2018.
See embedding models.
> they removed the tokenizer altogether
This is an active research topic, no real solution in sight yet.
This is such a superficial, English-centric take, but it might as well be true. It seems to me that in non-english languages the models, especially chatgpt, have suffered in the declension department and output words in cases that do not fit the context.
I have just ran an experiment: I have taken a word and asked models (chatgpt, gemini and claude) to explode it into parts. The caveat is that it could either be root + suffix + ending or root + ending. None of them realized this duality and have taken one possible interpretation.
Any such approach to tokenizing assumes context free (-ish) grammar, which is just not the case with natural languages. "I saw her duck" (and other famous examples) is not uniquely tokenizable without a broader context, so either the tokenizer has to be a model itself or the model has to collapse the meaning space.
I was looking into morpheme tokenization approach, but went even more radical with building a semantic primitive tokenizer [1], i.e. kill, killed, killer would all share the same semantic connection and tokens, e.g. [KILL], [KILL, BEFORE], [KILL, SOMEONE].
It’s based on semantic primitives (Wierzbicka NSM) and emoji (the fun idea that got me interested in this in the first place).
So far I’ve tested 6 iterations and it trains and responds well with a 10k vocab, but the grammar came out rougher. Working on 8th iteration, mainly to improve the grammar and language. Turns out the smaller vocab couldn’t be maintained and all improvements get us back in the ballpark of the 32k vocab size. Further testing is still outstanding for this week.
their old tokenizer performed some space collapsing that allowed them to use the same token id for a word with and without the leading space (in cases where the context usually implies a space and one is not present, a "no space" symbol is used).
This is almost certainly wrong.
Case sensitive language models have been a thing since way before neural language models. I was using them with boosted tree models at least ten years ago, and even my Java NLP tool did this twenty years ago (damn!). There is no novelty there of course - I based that on PG's "A Plan for Spam".
See for example CountVectorizer: https://scikit-learn.org/stable/modules/generated/sklearn.fe...
The bitter lesson says that you are much better off just adding more data and learning the tokenizer and it will be better.
It's not impossible that the new Opus tokenizer is based on something learnt during Mythos pre-training (maybe it is *the learned Mythos tokenizer?%), and it seems likely that the Mythos pre-training run is the most data ever trained on.
Putting an inductive bias in your tokenizer seems just a terrible idea.
Poor Ed.
This is the rugpull that is starting to push me to reconsider my use of Claude subscriptions. The "free ride" part of this being funded as a loss leader is coming to a close. While we break away from Claude, my hope is that I can continue to send simple problems to very smart local llms (qwen 3.6, I see you) and reserve Claude for purely extreme problems appropriate for it's extreme price.
> This is the rugpull that is starting to push me to reconsider my use of Claude subscriptions.
I'm still with them cause the model is good, but yes, I'm noticing my limits burning up somewhat faster on the 100 USD tier, I bet the 20 USD tier is even more useless.
I wouldn't call it a rugpull, since it seems like there might be good technical reasons for the change, but at the same time we won't know for sure if they won't COMMUNICATE that to us. I feel like what's missing is a technical blog post that tells uz more about the change and the tokenizer, although I fear that this won't be done due to wanting to keep "trade secrets" or whatever (the unfortunate consequence of which is making the community feel like they're being rugpulled).
For now you can run /model claude-opus-4-6
Quality of answers from quantized models is noticeable worse than using the full model.
You'll be better using Qwen 3.6 Plus through Alibaba coding plan.
I think an LLM that is a decent chunk smarter/better than other LLM's ought to be able to charge a premium perhaps 10x or 100x it's competitors.
See for example the price difference between taking a taxi and taking the bus, or between hiring a real lawyer Vs your friend at the bar who will give his uninformed opinion for a beer.
I'm really surprised that:
1. Anthropic has not published anything about why they made the change and how exactly they changed it
2. Nobody has reverse engineered it. It seems easy to do so using the free token counting APIs (the Google Vertex AI token count endpoint seems to support 2000 req/min = ~3million req/day, seems enough to reverse engineer it)
> It seems easy to do so
What are you waiting for? ;)
Anyone have good tips or resources on token management best practices? Because I’ve hit the limiter with one single prompt now on Opus 4.7.
What I’m reading so far seems to be:
-selective use of models based on task complexity
-encoding large repos into more digestible and relevant data structures to reduce constant reingesting
-ask Claude to limit output to X tokens (as output tokens are more expensive)
-reduce flailing by giving plenty of input context
-use Headroom and RTK
-disable unused MCP, move stuff from CLAUDE.md to skills
But I’d love to learn if anyone has any good tips, links, or tools as I’m getting rate limited twice a day now.
Thank you for sharing these tips! Just checked RTK and Headroom out and looks like Headroom actually uses RTK under the hood for CLI output compression: https://github.com/chopratejas/headroom#compared-to
I’m working on a tool that is a more token-efficient code search than grep. I don’t have hard numbers yet, but it’s been working for me to get longer sessions. https://github.com/ebcode/SourceMinder
What was your single prompt? That seems highly unlikely.
Aren't these increases offset by the quality of the responses and reducing the iterations needed to fine-tune the responses?
Only for the range of tasks where 4.7 performs well but 4.6 performed suboptimally. If both models can one-shot the task without retries, then the number of iterations is already at the lower bound.
This also applies at the sub-task level. If both models need to read three files to figure out which one implements the function they need to modify, then the token tax is paid for all three files even though "not the right file" is presumably an easy conclusion to draw.
This is also related to the challenge of optimizing subagents. Presumably the outer, higher-capacity model can perform better with everything in its context (up to limits), but dispatching a less-capable subagent for a problem might be cheaper overall. Anthropic has a 5:1 cost on input tokens between Opus and Haiku, but Google has 8:1 (Gemini Pro : Flash Lite) and OpenAI has 12:1 (GPT 4.2 : 4.2 nano).
Likely only some of the time
Is there any provided reason from anthropic why they changed the tokenizer ?
Is there a quality increase from this change or is it a money grab ?
The tokenizer is an important part of overall model training and performance. It’s only one piece of the overall cost per request. If a tokenizer that produces more tokens also leads to a model that gets to the correct answer more quickly and requires fewer re-prompts because it didn’t give the right answer, the overall cost can still be lower.
Comparisons are still ongoing but I have already seen some that suggest that Opus 4.7 might on average arrive at the answer with fewer tokens spent, even with the additional tokenizer overhead.
So, no, not a money grab.
How would it be a money grab? If the new tokenizer requires more tokens to encode the same information, it costs them more money for inference. The point of charging per token is that the cost is proportional to the number of tokens. That's my understanding anyway
If they wanted they could always just double the $/token. They don't seem to be able to keep up with their current demand and that's what companies normally do in that circumstance if they're looking to money grab, no need for the bankshot approach.
It's a better model in my usage. I have benchmarks.
This is a great piece of data, but only a piece of the actual question that we need to answer, which is:
For a given input, how many tokens will be used for an answer, and how high quality will that answer be?
Measuring the tokenizer is just one input into the cost-benefit tradeoff.
Many comparisons between 4.6 & 4.7 at https://tokens.billchambers.me/leaderboard
My prompt was 40% more tokens using Opus 4.7.
Why do you need an API key to tokenize the text? Isn't it supposed to be a cheap step that everything else in the model relies on?
I'd guess it's because they don't want people to reverse engineer it.
Note that they're the only provider which doesn't make their tokenizer available offline as a library (i.e. the only provider whose tokenizer is secret).
To prevent abuse? It's a completely free endpoint so I don't understand your complaint.
I'd love it if that API (which I do not believe Anthropic charge anything for) worked without an API key.
Anthropic was pulling ahead of their peers, but if they can't hear their customer's complaints about negatively changing value between releases they're going to undermine their position until no advantage is left.
Interesting findings. Might need a way to downsample images on upload to keep costs down.
Yeah that should work - it looks like the same pixel dimension image at smaller sizes has about the same token cost for 4.6 and 4.7, so the image cost increase only kicks in if you use larger images that 4.6 would have presumably resized before inspecting.
I just asked Claude about defaulting to 4.6 and there are several options. I might go back to that as default and use --model claude-opus-4-7 as needed. The token inflation is very real.
An interesting question is whether the tokenizer is better at something measurable or just denser. A denser tokenizer with worse alignment to semantic boundaries costs you twice, higher bill and worse reasoning. A denser tokenizer that actually carves at the joints of the model's latent space pays for itself in quality. Nobody outside Anthropic can answer which it is without their eval suite, so the rugpull read is fair but premature. Perhaps the real tell will be whether 4.7 beats 4.6 on the same dollar budget on the benchmarks you care about, not on the per-token ones Anthropic publishes.
Okay, but what about output tokens?