Claude Token Counter, now with model comparisons

> 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]

You have 3 different tokens which encode the same word (Kill, kill, <space>kill) depending on its capitalization and whether there's a space before it or not, you have separate tokens if it's the past tense, etc.

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

Notice that this makes much more sense now - the model now only has to learn what "<capitalize>" is, what "kill" is, what "<space>" is, and what "ed" (the past tense suffix) is, and it can compose those together. The downside is that it increases the token usage.

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.

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.

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.

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 ?

Interesting findings. Might need a way to downsample images on upload to keep costs down.

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?

> Opus 4.7 tokenizer used 1.46x the number of tokens as Opus 4.6

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

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.

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).

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.

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.

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).

I'd love it if that API (which I do not believe Anthropic charge anything for) worked without an API key.

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.

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.

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).

I'd love it if that API (which I do not believe Anthropic charge anything for) worked without an API key.

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.

20th April 2026 - Link Blog

Claude Token Counter, now with model comparisons. I upgraded my Claude Token Counter tool to add the ability to run the same count against different models in order to compare them.

As far as I can tell Claude Opus 4.7 is the first model to change the tokenizer, so it's only worth running comparisons between 4.7 and 4.6. The Claude token counting API accepts any Claude model ID though so I've included options for all four of the notable current models (Opus 4.7 and 4.6, Sonnet 4.6, and Haiku 4.5).

In the Opus 4.7 announcement Anthropic said:

Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type.

I pasted the Opus 4.7 system prompt into the token counting tool and found that the Opus 4.7 tokenizer used 1.46x the number of tokens as Opus 4.6.

Screenshot of a token comparison tool. Models to compare: claude-opus-4-7 (checked), claude-opus-4-6 (checked), claude-opus-4-5, claude-sonnet-4-6, claude-haiku-4-5. Note:

Opus 4.7 uses the same pricing is Opus 4.6 - $5 per million input tokens and $25 per million output tokens - but this token inflation means we can expect it to be around 40% more expensive.

The token counter tool also accepts images. Opus 4.7 has improved image support, described like this:

Opus 4.7 has better vision for high-resolution images: it can accept images up to 2,576 pixels on the long edge (~3.75 megapixels), more than three times as many as prior Claude models.

I tried counting tokens for a 3456x2234 pixel 3.7MB PNG and got an even bigger increase in token counts - 3.01x times the number of tokens for 4.7 compared to 4.6:

Same UI, this time with an uploaded screenshot PNG image. claude-opus-4-7: 4,744 tokens, 3.01x (yellow badge). claude-opus-4-6: 1,578 tokens, 1.00x (green badge).

Update: That 3x increase for images is entirely due to Opus 4.7 being able to handle higher resolutions. I tried that again with a 682x318 pixel image and it took 314 tokens with Opus 4.7 and 310 with Opus 4.6, so effectively the same cost.

Update 2: I tried a 15MB, 30 page text-heavy PDF and Opus 4.7 reported 60,934 tokens while 4.6 reported 56,482 - that's a 1.08x multiplier, significantly lower than the multiplier I got for raw text.

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Claude Token Counter, now with model comparisons

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