NederlandsTokens
Tokens are the pieces of text a language model reads and writes: often a short word or part of a word. The speed, cost and memory of AI are measured in tokens.
A language model does not read and write in whole words or letters, but in tokens, also called word pieces. A token is a piece of text: a short word, part of a long word, a punctuation mark or a space followed by a word. You can compare it to words being chopped into syllables before the model gets to work on them.
How does it work?
Before a model processes a text, the text is split into tokens. Each token gets a number, and the model calculates with those numbers. An answer usually emerges token by token: the model picks the next piece, adds it and then looks at what fits next.
That is why tokens are used as a measure everywhere. A model's speed is given in tokens per second. The amount of text a model can take in at once is the number of tokens that fit in its working memory. And the price of an AI service is often calculated per million tokens going in and per million coming out. Long texts and long chains of reasoning therefore cost more tokens, and with them more computing time and money.
Why it matters
Tokens make it easy to see what AI work costs. According to its maker, the American company Inception's AI model Mercury 3 writes more than a thousand word pieces per second, because it finishes a text in a few rounds all at once instead of token by token (link). That makes AI faster and cheaper.
For heavy work the numbers climb quickly. Anthropic has Claude supply a mathematical proof with software, which is then checked step by step, and a single such proof can cost hundreds of millions of tokens (link). When computing becomes more efficient, that number drops. For anyone reading news about AI, it is useful to know that a token is not a word, but a building block of a word.
Articles about Tokens
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No need to take the AI's word for it: Claude delivers a mathematical proof for software
How do you know whether an AI's work is sound if nobody can read it all? Anthropic has Claude supply a mathematical proof that a small program checks step by step. This way Claude examined 123 widely used software building blocks and found hundreds of hidden bugs.
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Free coding model for the laptop was largely developed by AI itself
The AI company Recursive has released Flint, a free AI model that helps with programming and runs on an ordinary laptop. According to the company, its own research AI did a large part of the work: a first step towards AI that improves itself.
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A different order makes AI servers start answering more than three times faster
Engineers from Amazon and Mistral have made the open software vLLM, which many companies use to run AI models, work together more cleverly. With long questions, the first word of the answer appeared more than three times faster in their test. The new method is free to use.
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AI model Mercury 3 writes more than a thousand tokens per second by creating the whole text at once
The American company Inception has released Mercury 3, a language model that does not write word by word but brings a whole text into focus in a few steps. According to the company, this makes it four to eight times as fast as comparable models.
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Chinese company MiniMax releases fast and cheap AI model for agents
The Chinese AI company MiniMax has released M3.1 Flash, a model for AI agents that carry out tasks on their own. According to MiniMax, it is almost three times as fast as its predecessor and an agent task costs 70 per cent less. Independent tests will now follow.
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Researchers let an AI agent think partly without words: faster and cheaper
Researchers from Stanford and Together AI let an AI agent do part of its thinking without writing it out. It only switches to text to look something up or to answer. According to them, it works up to 2.6 times faster this way and scores better than comparable systems.
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Meta releases Muse Spark Open: a large AI model for programming that also runs on your own computers
Meta has released Muse Spark Open, a large AI model for programming and independent agent tasks that anyone can download. According to Meta, it is the strongest American open model so far, and it even runs on two Mac Studios.
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Gemini 4 Argon widely available: Google back among the leaders
Google has rolled out Gemini 4 Argon widely. On paper it beats GPT-6 Astra and Claude Opus 5.5 on 13 of 19 benchmarks, but independent measurements put it alongside the competition, not above it. The biggest leap is in price, long context and science.
