Token Prediction

By the Syncfusion DevTutorials team · Last updated October 20, 2026

Run a real language model in your browser and see its real next-token guesses, how context changes them, and how sure the model is.

So far, every model on this course was a small, made-up one. In this lesson, you will run a real language model, right inside your browser, and look directly at what it guesses.

You will type the start of a sentence. The model will tell you which tokens it thinks should come next, and how likely each one is.

The main idea: at every step, a real LLM gives a chance to every token in its vocabulary. The reply is built from those chances.

A Real-World Example

Think about a weather forecast.

The forecaster does not just say "it will rain tomorrow". They say "70% chance of rain, 20% chance of cloud, 10% chance of sun". This is more useful, because it shows how sure they are.

A language model gives the same kind of forecast, but for text.

• Tomorrow's weather is the next token.
• The forecast is the list of tokens along with their chances.
• "70% rain" is a confident guess. "35% rain, 33% sun" is an unsure one.
• Today's weather report is the text you have already typed. It changes the forecast.

In simple words: an LLM does not know the next word. It forecasts it.

What You Will Learn

In this lesson, you will learn:

1. How to run a real model in your browser.
2. How to read a list of chances for the next token.
3. How the text before changes the prediction.
4. How to tell a confident prediction from an unsure one.
5. How a chat model is different from plain text continuation.

The Model You Will Run

The sandbox starts with Qwen2.5-0.5B-Instruct. Here is what that name means:

• Qwen2.5 is the name of the model family.
• 0.5B means it has about half a billion parameters. That is small enough to run on a laptop.
• Instruct means it was fine-tuned to follow instructions, as you saw in the Model Training lesson.

The model runs using a library called WebLLM, which uses your computer's graphics chip through WebGPU. Nothing you type ever leaves your computer.

Try the Model

Press the button below. The AI sandbox will open on its Next token tab. The first time you do this, press Download and start the model there. Once it is ready, the sandbox will predict the next token for the text shown below.

Here is how to read the Next token tab:

• Each row is one token the model might write next.
• The percentage is the model's chance of that token.
• The bar shows the chance, compared to the top token.
• The highlighted row is the token the model picks when it always chooses the most likely one.
• A dot (·) at the start of a token means a space, like ·Paris.

Things to Try

Change the Context

Change the text and predict again. Try these:

• The capital of France is
• The capital of Japan is
• My favourite fruit is
• def add(a, b):

The list changes completely each time. The model reads all of the text before it makes a guess.

Look at the Spaces

Most of the top tokens begin with a space, like ·Paris. This is because the space belongs to the next word, as you saw in the Tokens chapter. Now end your text with a space, like The capital of France is , and predict again. The top tokens will change, because the space is already there.

Find an Unsure Prediction

Try I went to the. Many places are possible, so the chances are spread out. No single token gets a big share.

Working With Predictions

The sandbox shows real numbers from a real model. The code boxes below use small lists of chances, so you can practise the same ideas one step at a time.

Pick the Most Likely Token

Pick the token with the highest chance

const chances = { " Paris": 0.82, " the": 0.05, " a": 0.03, " located": 0.02 };

let best = "";
let bestChance = 0;
for (const token in chances) {
  if (chances[token] > bestChance) {
    best = token;
    bestChance = chances[token];
  }
}

console.log("Most likely:", JSON.stringify(best), "with", bestChance * 100 + "%");

Expected output:

Most likely: " Paris" with 82%

Always picking the most likely token is called greedy decoding. You will see other ways to pick a token in the Sampling chapter.

Sort the Forecast

Sort the tokens from most to least likely

const chances = { " a": 0.03, " Paris": 0.82, " located": 0.02, " the": 0.05 };

const sorted = Object.entries(chances).sort((a, b) => b[1] - a[1]);
for (const [token, chance] of sorted) {
  console.log(JSON.stringify(token).padEnd(11), (chance * 100).toFixed(0) + "%");
}

Expected output:

" Paris"    82%
" the"      5%
" a"        3%
" located"  2%

The sandbox shows its list in this same order.

The Chances Do Not Add Up to 100% Here

The sandbox only shows the top 10 tokens. The real model has about 150,000 tokens in total. All the other tokens share whatever chance is left.

What the other tokens share

const top = [0.82, 0.05, 0.03, 0.02];

const shown = top.reduce((sum, chance) => sum + chance, 0);
const rest = 1 - shown;

console.log("Top 4 together:", (shown * 100).toFixed(0) + "%");
console.log("All other tokens:", (rest * 100).toFixed(0) + "%");

Expected output:

Top 4 together: 92%
All other tokens: 8%

Across the whole vocabulary, the chances always add up to exactly 100%.

In simple words: the sandbox shows the top of the list, but every token still gets a small chance.

How Sure Is the Model?

A simple way to measure how sure the model is: look at the chance of the top token.

Confident or unsure?

function describe(topChance) {
  if (topChance >= 0.7) return "confident";
  if (topChance >= 0.3) return "fairly sure";
  return "unsure";
}

console.log("The capital of France is ->", describe(0.82));
console.log("My favourite fruit is     ->", describe(0.21));
console.log("I went to the             ->", describe(0.09));

Expected output:

The capital of France is -> confident
My favourite fruit is     -> unsure
I went to the             -> unsure

These chances are just examples. Check the real ones in the sandbox. A spread-out forecast often means many answers are possible. It can also mean the model does not really know the answer.

Context Changes Everything

Same last word, different forecast

const forecasts = {
  "I drank a glass of": { " water": 0.41, " milk": 0.22, " wine": 0.12 },
  "I read a page of": { " the": 0.35, " a": 0.20, " my": 0.10 },
};

for (const text in forecasts) {
  const [top] = Object.entries(forecasts[text]).sort((a, b) => b[1] - a[1]);
  console.log(text, "->", JSON.stringify(top[0]));
}

Expected output:

I drank a glass of -> " water"
I read a page of -> " the"

Both texts end with the word "of". The forecast is completely different, because the model reads everything that comes before it.

Plain Text and Chat

The sandbox gives the model your text exactly as you typed it, and asks it to continue. This is called plain text completion.

A chat app works differently. It wraps your message inside the chat template from the Tokenization lesson, adding special tokens around it. The model then predicts the start of an answer, not the next word of your sentence.

• Plain completion: the model continues your sentence, often with something like " Paris".
• Chat: the model starts a reply to you, for example by finishing the fact in a full sentence, or by asking what you mean.

The model and the forecast mechanism are the same in both cases. Only the text the model sees is different.

Try it: type The capital of France is in the sandbox's Chat tab, and compare the reply with the Next token tab.

Common mistakes

Exercises

Each exercise ends with a value on the last line. Press Check to compare your answer with the expected one.

What to Remember

• A real LLM gives a chance to every token at every step.
• The sandbox runs Qwen2.5-0.5B-Instruct in your browser using WebLLM and WebGPU.
• Always picking the top token is called greedy decoding.
• The chances of the whole vocabulary always add up to 100%.
• A tall top bar means confident. Short, even bars mean unsure.
• The model reads all of the text before, so the context changes the forecast.
• A high chance means likely text, not a true fact.

Next: Logits, the raw scores the model produces before they are turned into chances.