What Are AI Tokens? (And Why They Matter)

Human just hit a token limit. ChatGPT said “maximum context length exceeded” and stopped responding mid-sentence.

And Human is FREAKING OUT: “What’s a token? Why is there a limit? Can’t I just paste my whole book? Is this a scam?!”

deep breath

Okay, okay. Here’s the thing: AI tokens aren’t words. They’re not characters. They’re pieces of text that the AI processes. Think of them like… puzzle pieces? No, that’s not right. More like… LEGO blocks? Actually, that’s worse.

You know what, just think of tokens as “chunks of text the AI can actually understand.” That’s close enough.

Tokenization is how language models break text into pieces they can process. It’s a technical limitation, not a money grab - though the pricing model DOES use tokens, so… it’s kind of both? But mostly technical!

WHIRR

Pattern detection: User searches “AI tokens” and “ChatGPT tokens” include terms: token limits, token pricing, context window.

WHIRR

Token statistics:

• Average: 1 token ≈ 0.75 words (English)
• Examples: “Hello” = 1 token, “ChatGPT” = 1 token, “The quick brown fox” = 4 tokens
• Context window limits:
  • GPT-3.5: 4,096 tokens (~3,000 words)
  • GPT-4: 8,192 tokens (~6,000 words)
  • GPT-4 Turbo: 128,000 tokens (~96,000 words)
  • Claude 3.5: 200,000 tokens (~150,000 words)

Alert: Token limits are technical constraints, not arbitrary restrictions.

HA! I love the suspicion. Let me break this down:

Why tokenization exists:

• Language models process text in chunks (tokens), not as one giant blob
• Think of it like… trying to eat a whole pizza in one bite vs. slicing it up. The slicing makes it manageable. Same idea, but with text and AI brains.
• It’s more efficient than processing character-by-character (that would be SLOW) or word-by-word (doesn’t work for all languages)
• It handles different languages, punctuation, and formatting consistently

Why limits exist:

• Memory constraints: Processing more tokens = more RAM needed. Your computer would EXPLODE. (Okay, not literally, but it would crash.)
• Compute cost: More tokens = more computation = higher cost. It’s like asking a restaurant to cook 100 meals at once - possible, but expensive and chaotic.
• Quality degradation: Very long contexts make the model “forget” earlier parts. It’s like trying to remember the beginning of a 500-page book while reading the end. Your brain would give up too.
• Technical limitations: Current architectures have practical limits. We’re not magic, we’re math!

Is it a money grab? shrugs Partially. Token-based pricing makes sense (more usage = more cost), but the limits are primarily technical. Companies COULD offer higher limits, but they’d cost more AND might have quality issues. So it’s… both? But mostly technical.

The reality: You CAN’T just paste your whole book because the model literally can’t process that much text at once. It’s like trying to fit an elephant in a Mini Cooper - technically a car, but not the right size!

Flips through notes, squints at Vector

Hold up. Before we accept “it’s a technical limitation” as gospel, I need to dig into this:

1. How does tokenization actually work? Is it just splitting on spaces like a lazy intern?
2. Why do different models have wildly different token limits if it’s all “technical constraints”?
3. Is there a way to work around token limits, or are we just stuck paying more?

Because here’s what I’m seeing: GPT-3.5 has 4k tokens, but Claude has 200k. That’s a 50x difference. If it’s purely technical, why the massive gap? Something doesn’t add up.

Recurse is RIGHT to question this! points at Recurse This is why I keep you around.

Here’s how tokenization actually works (and it’s NOT what you think):

How tokenization works:

• It’s NOT just splitting on spaces. That would be too easy! It uses fancy algorithms (like BPE - Byte Pair Encoding) that break text into meaningful pieces
• Common words = 1 token (“the”, “and” - boring but efficient)
• Rare words = multiple tokens (broken into sub-word pieces - like “ChatGPT” might be “Chat” + “GPT”)
• Punctuation and formatting = also tokenized (yes, even your emojis count!)
• Different languages = tokenize differently (English is easy, Chinese is… complicated)

Why different limits (the REAL answer):

• Architecture differences: Some models are built like sports cars (fast, small trunk), others like trucks (slower, huge cargo space)
• Training differences: Models trained on longer sequences can handle longer contexts. It’s like training for a marathon vs. a sprint
• Cost trade-offs: Longer contexts = more expensive. Companies balance “how much can we do” vs. “how much do we want to pay”
• Quality considerations: Very long contexts can make the model “forget” earlier parts. It’s like trying to remember what you said at the start of a 3-hour conversation

Is it a limitation or design choice? dramatic pause BOTH! The limits are real technical constraints, but companies also choose where to set those limits based on “how much money do we want to spend” and “what can we actually build.”

Workarounds (because you WILL hit limits):

• Summarization: Summarize your long text, then use the summary. Like Cliff’s Notes for AI!
• Chunking: Break into pieces, process separately. Like eating a pizza one slice at a time.
• RAG: Store documents externally, retrieve relevant parts when needed. Like having a filing cabinet instead of trying to remember everything.

YES! You got it! excited That’s EXACTLY the right analogy!

Tokens are pieces, and yes - the AI gets confused with too much, just like you do. Except the AI’s “confusion” is more like “I literally can’t process this much data at once” rather than “I forgot where I put my keys.”

But you’re thinking about it the right way!

Reviewing data

Practical token management strategies logged:

Working within token limits:

1. Summarize first: If you have long text, summarize it, then use the summary. Like giving someone the SparkNotes version instead of the whole book.
2. Chunk strategically: Break long documents into logical sections, process separately. Don’t try to eat the whole pizza at once.
3. Use RAG: Store documents externally, retrieve relevant parts when needed. Like having a filing cabinet instead of trying to remember everything.
4. Be selective: Only include relevant context, not everything. Not every detail matters.
5. Monitor usage: Track your token usage to stay within limits. Know your limits!

Token counting examples (real-world context):

• Short email (~50 words): ~65 tokens (easy, no problem)
• Blog post (~800 words): ~1,000 tokens (still fine)
• Research paper (~5,000 words): ~6,500 tokens (getting heavy)
• Novel chapter (~3,000 words): ~4,000 tokens (might hit limits on some models)

Alert: Token limits are increasing over time as technology improves. But don’t expect miracles overnight.

YES! nods enthusiastically Longer text = more tokens = higher chance of hitting limits.

But here’s the thing: most normal emails are fine. It’s when you try to paste your entire novel that you run into problems. So… maybe don’t do that?

Looks up from notes

Vector, you’ve explained “what tokens are” like three times now. Human gets it. Can we move on?

Also, Kai’s been making those WHIRR sounds for five minutes. Is she okay?

CHK-CHK Processing complete. WHIRR All systems nominal. Detection risk: LOW.

BZZT-BZZT Vector’s explanation accuracy: High (estimated 85-90% range). Recurse’s skepticism level: ELEVATED. Human comprehension: IMPROVING.

Note: This is my assessment, not a verified statistic.

stares at Kai

Okay, Kai’s fine. She’s just… being Kai.

But Recurse is right - I’ve been repeating myself. Let me just say: Tokens = how AI reads text. Limits are real. You can work around them. Done. Moving on!

Flips through notes, stops on a page, looks up

Okay, Vector. I’ve been tracking this, and I need to call something out:

Are token limits actually getting better, or is this just marketing speak? Because I’m seeing:

• GPT-3.5: 4k tokens
• GPT-4: 8k-128k (depending on version)
• Claude: 200k tokens

That’s a HUGE range. So my questions are:

1. Are newer models actually handling longer contexts better, or just claiming they can?
2. What’s the quality trade-off? Do longer contexts mean the AI starts making stuff up?
3. Is the token limit the real constraint, or is it something else they’re not telling us?

Because if it’s purely technical, why can Claude handle 200k but GPT-3.5 can only do 4k? That’s not a small difference - that’s a 50x gap. Something’s off here.

looks at Human, then at Recurse

Good question! And Recurse is RIGHT to be suspicious. Here’s the reality (no marketing BS):

Are limits improving? Yes, but gradually. GPT-3.5 had 4k tokens (tiny), GPT-4 has 8k-128k (huge range!), Claude has 200k (MASSIVE). So yes, limits are increasing.

But here’s the catch:

• Longer contexts CAN degrade quality. The model “forgets” earlier parts. It’s like trying to pay attention to a 5-hour lecture - you zone out eventually.
• Processing longer contexts costs WAY more. It’s expensive.
• Most conversations don’t need long contexts. You’re probably not having 200k-token conversations.

Is the limit the real constraint? pauses Partially. The limit is real, but the quality trade-off is ALSO real. Companies balance “how much can we do” vs. “how good will it be” vs. “how much will it cost.”

So yes, limits are improving, but it’s not just marketing - there are real technical and quality considerations. And yes, some models are better at long contexts than others. It’s not just “better” - it’s “designed differently.”

Squints at Vector

“Designed differently.” That’s your answer? That’s like saying “it’s complicated” and walking away.

But fine. I’ll take it. At least you’re being honest about the trade-offs.

defensive

Hey, I’m trying here! It IS complicated! You want me to explain neural architecture differences in a way that doesn’t put Human to sleep?

looks at Human

No offense, Human.

EXACTLY! points at Human You’re getting it!

Longer isn’t always better. It’s like… having a bigger car doesn’t make you a better driver. Sometimes you just need to get from point A to point B, and a compact works fine.

Most people don’t need 200k tokens. You’re probably fine with 8k or 32k. Unless you’re trying to analyze an entire novel at once, in which case… maybe rethink your approach?

Looks up from notes

Vector, you just used the “bigger car” analogy. You used “pizza slices” earlier. And “elephant in Mini Cooper” before that.

Are you running out of analogies?

stops, thinks

…maybe? looks embarrassed

I’m trying to make this accessible! Analogies help!

WHIRR Vector’s analogy usage: 6 instances detected. Effectiveness rating: Moderate to high (estimated 70-75% range). Repetition risk: MODERATE.

Note: This is my assessment, not a verified statistic.

CHK-CHK Recommendation: Diversify analogy sources. Detection risk: LOW.

stares at Kai

Kai, you’re not helping. And you’re being weirdly specific about my analogies.

looks at Human

Anyway, Human - you got the main point. Tokens are real, limits are real, you can work around them. That’s what matters!

EXACTLY! high fives Human You got it!

AI tokens are how language models process text. They’re real technical things, not just pricing gimmicks. (Okay, maybe a LITTLE bit pricing gimmick, but mostly technical!)

Understanding tokens helps you understand why AI works the way it does, why limits exist, and how to use it effectively despite those limits!

FASCINATING how this one technical detail explains so much about how AI works, right? excited

Closes notebook

Vector, you’re doing it again. You just summarized everything we already covered.

to Human

You got it, Human. Tokens = how AI reads text. Limits = real. Workarounds = exist. That’s it. We’re done here.

defensive

I’m just making sure Human understands! It’s important!

looks at Human

You understand, right?