“Don’t worry, it costs the same.” That was Anthropic’s message to announce the launch of its new AI model, Claude Opus 4.7. In that statement they made it clear that “the price remains the same as Opus 4.6: $5 per million entry tokens and $25 per million exit tokens“There was, however, fine print, because the model is better but to achieve it it reasons more, and that means one thing: more tokens. And the more tokens you consume, the more the AI bill goes up.
Anthropic already warned. It should be noted that in that official announcement Anthropic did not hide the facts. In one of the paragraphs he clearly explained how Opus 4.7 “thinks more” and that has a direct impact on token consumption (we highlight the difference in bold):
“Opus 4.7 is a direct update to Opus 4.6, but there are two changes worth keeping in mind as they affect the use of tokens. First, Opus 4.7 uses an updated tokenizer that improves the model’s processing of text. This means that the same input can generate more tokens (approximately 1.0 to 1.35 times moredepending on the type of content). Second, Opus 4.7 performs deeper analysis at higher effort levels, especially in the later phases of agent scenarios. “This improves its reliability on complex problems, but also means generating more output tokens.”
Or what is the same: when it responds, Opus 4.7 uses significantly more tokens than its predecessor, and that is important because the output tokens are much more expensive than the input ones. In the specific case of Opus 4.7, five times more expensive ($5 versus $25).
What is a tokenizer and why does it matter?. Large language models (LLMs) do not process text directly, but rather convert it into units called tokenswhich are fragments of words, symbols or characters. The tokenizer is the mechanism that makes that conversion. Anthropic has decided to update the tokenizer in Opus 4.7, arguing that its new system improves how text is processed. The direct consequence: the prompt that previously generated 1,000 tokens now generates up to 1,350. And since it is billed per token, the effective cost rises even though the price per token has remained the same.
Confirmed by third parties. Simon Willison, a well-known analyst and popularizer in this field, created a tool to measure the difference in token consumption with the Claude Opus 4.6 and 4.7 API. He took the official Opus 4.7 ‘system prompt’ and ran it through both models:
- With Opus 4.6 it generated 5,039 output tokens
- With Opus 4.7 it generated 7,335 output tokens
This represents a growth of 1.46x tokens between Opus 4.6 and Opus 4.7, even greater than that indicated by Anthropic (1.35x). For images the difference is even more extreme since the token consumption is up to 3.01x. There is an important clarification here, because there is support for images of up to 3.75 Mpixels and that higher resolution causes consumption to increase significantly.
Bill Chambers, another X user, published another tool called Tokenomics that also allows you to compare token consumption between both models with any prompt. The aggregate ranking of all users who have tried this tool shows that the average increase is 38.6%, very much in line with what Anthropic points out.
And also think more. As we said, this new model applies two changes in its way of acting. The first is the aforementioned tokenizer: the same input is converted into more input tokens. The second is the fact that the model now “thinks more” before responding, which means more token consumption. Opus 4.7 arrives with a new “effort” level called xhigh, located between high and max. Anthropic has decided that now the default effort will be precisely xhigh for all plans, so both mechanisms contribute to this higher token consumption. As Anthropic itself indicates,
“Opus 4.7 thinks more about high effort levels, particularly in later turns in agentic settings. This improves its reliability on difficult problems, but it does mean that it produces more output tokens.”
Criticisms on networks. The reaction of users has been clear and there are various examples on networks such as X or Reddit in which said users criticize the changes. On Reddit a thread titled ‘Opus 4.7 is a serious regression, not an improvement‘It already has 3,200 votes and 800 comments that sum up that this new model ignores instructions, hallucinates and lies, It’s “dumber”has become too complacent or even lazyand “talks too much”, which also contributes to the cost of each consultation.
Many complain that their Pro and Max paid limits are running out faster than before due to these changes. Some users claim that Opus 4.7 is the first sign that Anthropic may has gone too fast for the first time when launching a new model.
Anthropic reacts. Criticism about the cost and behavior of the model has made those responsible for Anthropic try to clarify things. Borys Cherny retweeted a message from the company in which was spoken how the “/usage” parameter in Claude Code allowed us to show what kind of things our API or usage plan is spent on. This same engineer, who is the person most responsible for the development of the aforementioned Claude Code, also indicated that since his new model now uses more tokens, in Anthropic they had increased the fees of use of the models, although without giving specific details.
The pattern that repeats. For weeks now the user community he complained about what noticed a “regression” in the behavior of Opus 4.6. Although it is impossible to verify or validate it, there were many users who complained on networks about how the performance of the model had gotten worse in your tests. Now they have just launched a model that promises to be better than the previous one, but that ends up costing more to use if you are not careful. Both events draw a pattern: that Anthropic is increasing its revenue without announcing price increases as such.
What users can do. Although it is true that Opus 4.7 consumes more tokens by default, it is possible to make some adjustments to try to contain costs. The effort parameter allows you to reduce this setting from xhigh to high or even medium for tasks that do not require as much reasoning. There are also new ways to establish “task budgets” to limit the tokens used in certain processes, and in images we can reduce the output resolution. In addition, Opus 4.6 is still available for now, so we can opt for it (or Sonnet 4.6) if we think this version is worth it for us. The other alternative, of course, is to use other AI models that work well for us and are more affordable, both in the cloud and on-premises.
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