What are tokens in LLMs?

Your marketing team finally figured out how to use ChatGPT to draft blog posts. “This is huge,” they say. Until you get the usage bill and realize: that 3-minute brainstorm just cost you $8.47. What?”

Chances are, you’ve hit the invisible wall of tokens.

If AI tools feel like a black box—helpful, but unpredictable—it’s probably because no one’s explained that behind every text prompt, there’s a tiny hidden economy made of tokens. These things are the fuel, the bottleneck, and the billable unit of language models.

And yes—understanding them can actually help you cut costs, write better prompts, and make smarter AI decisions.

So... what are tokens in LLMs?

Imagine you’re feeding your AI-tool-of-choice a sentence. You think it’s sending one neat chunk of text. The AI thinks you’re handing it a box of LEGO bricks.

Tokens are those LEGO bricks—the smallest units a language model uses to read, understand, and respond to your input.

Depending on the model, a token can be:

• A full word ("marketing")
• A partial word ("market" and "ing")
• A single character, space, or punctuation

So even a 10-word sentence can break down into 20+ tokens. Wild, right?

Here’s a concrete example. Let’s say you type:

"Boost leads by automating client follow-up."

That’s seven words. For an LLM, like GPT-4, it might tokenize it like this: ["Boost", " leads", " by", " autom", "ating", " client", " follow", "-", "up", "."] – 10 tokens. That tiny sentence is already costing you tokens to process.

Why tokens matter more than you think

This isn’t just AI trivia—tokens control what your AI can do, how fast it runs, and how much it costs. They are mission critical if you:

• Use AI for content, email, or chatbot generation
• Pay by usage (most tools bill per 1,000 tokens)
• Want predictable outcomes from prompts and flows

Context length = token limits. Most models have hard limits defined in tokens—say, 4,000 or 8,000—on how much they can process in a single prompt + their response.

Not characters. Not words. Tokens.

If your prompt and input together go beyond that? The model starts to forget. Or truncates. Or costs spike as you jam in more than it can chew.

Tokenization: The “secret” pregame every AI model runs

So how does your text become a bunch of tokens?

Through something called tokenization—the preprocessing step that splits language into these manageable bits.

There are a few flavors:

• Word tokenization: "Hello there" → ["Hello", "there"]
  Simple, but freaks out if it sees new or misspelled words.
• Character tokenization: Every single letter or symbol becomes a token. Flexible, but super inefficient.
• Subword tokenization: This one’s the MVP—splits words into common building blocks like “auto / mat / ion.” It balances flexibility with efficiency.
  This is what GPTs use (specifically a method called Byte Pair Encoding, or BPE).

If you’re a business dealing with slangy customer feedback, weird niche industry jargon, or typos galore? Subword tokenization saves the day. The model can still piece together what you meant, even if “automationnn” isn’t properly spelled.

The trade-offs of different token sizes

Token size isn’t just an academic detail—it creates real bottlenecks and superpowers in your AI workflows.

In short: smaller tokens = more coverage + complexity, but at the cost of speed and token budget.

Tokens & your business: where it hits ops, marketing, and budgets

This stuff might sound nerdy—but it has real-world impact. Here's how tokens affect your actual operations:

1. Your chatbot isn’t “forgetting”—it’s hitting token limits.

If your AI-generated onboarding assistant seems to ignore earlier parts of the convo, it’s probably going past its allowed context window. Models like GPT-4-8k can only "remember" up to about 6,000–8,000 tokens per session. Beyond that, things drop from memory.

2. Your prompt costs are likely way higher than you think.

Many commercial tools charge per 1,000 tokens. That “quick blog outline” may cost less than $1 in tokens—but if you’re running 50 a week, that adds up fast.

One client came to us spending over $600/month on AI blog drafts. Once we helped them optimize their prompts and trim excess filler (without losing quality), they cut usage by 40%—without cutting output.

3. Prompt quality = output quality

The more efficient your token usage, the more room you leave in the model’s brain for the actual task: your response, your brand tone, your nuance. Packed prompts slow the model down, cost more, and weaken relevance.

Common myths you can ignore

• “Tokens = words.”
  This one’s everywhere. But one word can be 1, 2, even 4 tokens long. Don’t guess—use a tokenizer tool to see how AI sees your input.
• “Character limits = token limits.”
  Nope. 1,000 characters might be only 400 tokens or as high as 800. Depends on the language and punctuation.
• “More tokens = smarter output.”
  Not always. Going overboard with token-heavy prompts might make your AI drift. Brevity = clarity.

How to see what your text looks like in tokens

Want to peek under the hood?

• OpenAI’s tokenizer tool lets you paste your prompt and see its token breakdown instantly.
• You can also use API-based tools for batch testing if you're generating content at scale.

Pro tip: Use this in workshops or brainstorming sessions with your team. You’ll shave hours off trial-and-error prompt writing once you actually see how the machine thinks.

What’s next: smarter prompts, smoother tools, faster ops

Some platforms now include token visualization, advanced tokenizers, and prompt optimization features—a huge plus for lean teams managing AI at scale.

Expect more tools in the next 12–18 months that dynamically resize prompt templates around token limits or guide you in writing leaner, better prompts on the fly.

How we use this with clients

At Timebender, we build targeted AI automations that take all this token logic into account—because if we’re scripting email nurture or dynamic ad copy or training your sales team’s AI assistant, getting token counts right = faster systems, lower cost, fewer headaches.

Some clients want fully custom builds. Some just plug in our semi-custom solutions for onboarding flows, sales follow-up, or content repurposing.

No matter what, we build it so your team doesn’t have to babysit a robot that runs out of memory halfway through an email thread.

Want to see where your AI usage is leaking time—or money?

If your team’s using AI but the results are...meh—or worse, the costs keep climbing—let’s make it efficient.

Book a free Workflow Optimization Session and we’ll help you map what actually saves time, cuts overhead, and upgrades your stack using the systems that already work for your team.

It’s like debugging your AI without needing to speak robot.

Sources

• Microsoft AI token explanation
• MLQ Token Guide
• Tokenization Overview by Sean Trott
• Mistral AI Tokenization Guide
• NVIDIA Token Deep Dive
• Koyeb: How LLMs Work

River Braun, founder of Timebender, is an AI consultant and systems strategist with over a decade of experience helping service-based businesses streamline operations, automate marketing, and scale sustainably. With a background in business law and digital marketing, River blends strategic insight with practical tools—empowering small teams and solopreneurs to reclaim their time and grow without burnout.