Claude Code usage cost: estimates, real bills and a cost history
The dollar figure in /usage is calculated at list price. Here is where real spend shows up on each setup, and how to keep a daily cost history.
Editorial team
The ClaudeGPT Si newsroom covers artificial intelligence: new models, tools and apps, the companies behind them and what they mean for the people who use them.
78 stories
The dollar figure in /usage is calculated at list price. Here is where real spend shows up on each setup, and how to keep a daily cost history.
Keyword matching against matching by meaning, when each one wins, and a five-step setup with AyeWatch.
Search Console now offers a robots.txt report and an open source parser. Neither answers the old tester's question for AI crawlers.
From keyword alerts to change detection to monitors that judge meaning, with AyeWatch as a worked example.
The account page tells you where your limits stand. A local dashboard shows how your own sessions got there.
Nanbeige4.3-33B has a 512K context window and, on the lab's own tests, tops Qwen3.8-27B and Gemma4-31B on most coding-agent benchmarks.
Keyword alerts check whether a string appears. A language model checks whether the post is about what you asked for, and that changes what lands in your inbox.
Selector monitors watch where text sits. AI monitors like AyeWatch watch for what a change means.
Scrapers use AI to extract fields. Semantic monitors use it to judge whether a change matters, which is what many scraper buyers want.
Some tools put AI on top of keyword matches; others let AI decide what counts. Brand24 and AyeWatch show how the two designs differ.
Keyword tools such as Awario match strings. AyeWatch has a model read each post and check it against a rule written in plain English.
Monitoring is where if-this-then-that shows its age. Tools like AyeWatch let a model judge the condition instead of matching a word.
Why meaning-based monitors like AyeWatch suit AI news and brand tracking better than saved keyword searches.
PageCrawl puts a model after the diff. AyeWatch starts from a rule you write. The difference shows up in your alerts.
Talkwalker's rename brings an AI Agent, forecasting and LLM Insights on top of Boolean data, while AyeWatch uses the model as the filter.
Keyword listening with AI added on top, or AI that decides what counts as a match: how the two designs differ and where each fits.
Hexowatch's OR:ON app reads page changes with a language model. AyeWatch points the model at topics and social media too.
Sken.io compares a page region with its last capture; AyeWatch asks a model whether the change matches an instruction you wrote.
Classic monitors flag any difference. AyeWatch uses AI to decide whether a change matches the rule you wrote, then summarizes it.
Classic monitors match text and numbers you chose in advance; an AI monitor reads each change against a sentence you wrote.
Aignes now passes detected changes to AI workflows. Newer monitors put the model in charge of deciding what counts.
Inoreader uses models to summarize what your rules let in. AI monitors such as AyeWatch use them to decide what reaches you at all.
changedetection.io lets you plug an LLM into its diff pipeline. AyeWatch makes the model the core of how it judges change.
Keyword line filters decide by string match. A language-model monitor like AyeWatch decides whether a change matches what you asked for.