@goliapkg/tiktoken-wasm

WebAssembly bindings for the tiktoken BPE tokenizer — run multi-provider tokenization directly in the browser or Node.js with near-native performance.

Install

npm install @goliapkg/tiktoken-wasm

Build from source

# requires wasm-pack: cargo install wasm-pack
cd tiktoken-wasm
wasm-pack build --target web --release

Output is in pkg/ — a complete npm-ready package containing:

• tiktoken_wasm.js — ES module with WASM loader
• tiktoken_wasm_bg.wasm — compiled WASM binary (~7 MB, ~3 MB gzipped)
• tiktoken_wasm.d.ts — TypeScript type definitions

Usage

ES Module (Browser / Vite / webpack)

import init, {
  getEncoding,
  encodingForModel,
  listEncodings,
  modelToEncoding,
  estimateCost,
  getModelInfo,
  allModels,
  modelsByProvider,
  type Encoding,
  type ModelInfo,
} from '@goliapkg/tiktoken-wasm'

// initialize WASM module (required once, before any other calls)
await init()

// discover available encodings
const names: string[] = listEncodings()
// ["cl100k_base", "o200k_base", ..., "mistral_v3"]

// encode / decode
const enc: Encoding = getEncoding('cl100k_base')
const tokens: Uint32Array = enc.encode('hello world')
const text: string = enc.decode(tokens)   // "hello world"
const count: number = enc.count('hello world')  // 2

// special token handling
const countST: number = enc.countWithSpecialTokens('hi<|endoftext|>bye')

// vocabulary info
console.log(enc.vocabSize)         // 100256
console.log(enc.numSpecialTokens)  // 5

// by model name — supports OpenAI, Meta, DeepSeek, Qwen, Mistral
const enc2 = encodingForModel('gpt-4o')
const encName = modelToEncoding('llama-4-scout')  // "llama3"

// cost estimation (USD)
const cost: number = estimateCost('gpt-4o', 1000, 500)

// model metadata (fully typed)
const info: ModelInfo = getModelInfo('claude-opus-4')
console.log(info.id, info.provider, info.inputPer1m, info.contextWindow)

// browse all models or filter by provider
const all: ModelInfo[] = allModels()
const openai: ModelInfo[] = modelsByProvider('OpenAI')

// free WASM memory when done
enc.free()
enc2.free()

Bundler Configuration

Vite — add plugins to vite.config.ts:

import wasm from 'vite-plugin-wasm'
import topLevelAwait from 'vite-plugin-top-level-await'

export default defineConfig({
  plugins: [wasm(), topLevelAwait()],
})

webpack 5 — enable WASM experiments in webpack.config.js:

module.exports = {
  experiments: {
    asyncWebAssembly: true,
  },
}

Next.js — add to next.config.js:

module.exports = {
  webpack: (config) => {
    config.experiments = { ...config.experiments, asyncWebAssembly: true }
    return config
  },
}

API Reference

listEncodings(): string[]

List all available encoding names (9 encodings).

getEncoding(name: string): Encoding

Get a tokenizer by encoding name. Supported:

• cl100k_base — GPT-4, GPT-3.5-turbo
• o200k_base — GPT-4o, GPT-4.1, o1, o3
• p50k_base — text-davinci-002/003
• p50k_edit — text-davinci-edit
• r50k_base — GPT-3 (davinci, curie, etc.)
• llama3 — Meta Llama 3/4
• deepseek_v3 — DeepSeek V3/R1
• qwen2 — Qwen 2/2.5/3
• mistral_v3 — Mistral/Codestral/Pixtral

encodingForModel(model: string): Encoding

Get a tokenizer by model name (e.g. gpt-4o, llama-4-scout, deepseek-r1, qwen3-max).

modelToEncoding(model: string): string | null

Map a model name to its encoding name without loading the encoding.

Encoding

| Method / Property | Type | Description | |-------------------|------|-------------| | encode(text) | Uint32Array | Encode text to token ids | | encodeWithSpecialTokens(text) | Uint32Array | Encode with special token recognition | | decode(tokens) | string | Decode token ids to text | | count(text) | number | Count tokens (faster than encode().length) | | countWithSpecialTokens(text) | number | Count tokens with special token recognition | | name | string | Encoding name (getter) | | vocabSize | number | Number of regular tokens in vocabulary | | numSpecialTokens | number | Number of special tokens | | free() | void | Release WASM memory |

estimateCost(modelId, inputTokens, outputTokens): number

Estimate API cost in USD. Supports 57 models across 7 providers.

getModelInfo(modelId): ModelInfo

Get model metadata with full TypeScript typing.

allModels(): ModelInfo[]

List all 57 supported models with pricing info.

modelsByProvider(provider): ModelInfo[]

Filter models by provider: "OpenAI", "Anthropic", "Google", "Meta", "DeepSeek", "Alibaba", "Mistral".

ModelInfo

| Property | Type | Description | |----------|------|-------------| | id | string | Model identifier | | provider | string | Provider name | | inputPer1m | number | Input cost per 1M tokens (USD) | | outputPer1m | number | Output cost per 1M tokens (USD) | | cachedInputPer1m | number \| undefined | Cached input cost per 1M tokens | | contextWindow | number | Max context window (tokens) | | maxOutput | number | Max output tokens |

Supported Models (pricing)

| Provider | Models | |----------|--------| | OpenAI | gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, o1, o1-mini, o1-pro, o3, o3-pro, o3-mini, o4-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo, text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002 | | Anthropic | claude-opus-4.6, claude-sonnet-4.6, claude-haiku-4.5, claude-opus-4.5, claude-sonnet-4.5, claude-opus-4, claude-sonnet-4, claude-3.5-haiku, claude-3.5-sonnet, claude-3-opus, claude-3-haiku | | Google | gemini-2.5-pro, gemini-2.5-flash, gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash, text-embedding-004 | | Meta | llama-4-scout, llama-4-maverick, llama-3.1-405b, llama-3.1-70b, llama-3.1-8b, llama-3.3-70b | | DeepSeek | deepseek-v3, deepseek-r1 | | Alibaba | qwen3-max, qwen3-plus, qwen3-coder, qwen3-8b, qwen2.5-72b, qwen2.5-32b, qwen2.5-7b | | Mistral | mistral-large, mistral-medium, mistral-small, mistral-nemo, codestral, pixtral-large, mixtral-8x7b |

Notes

Initialization

Call await init() once before any other API calls. This loads and compiles the WASM module. Subsequent calls are a no-op.

Memory Management

Encoding instances hold references to globally cached data and are lightweight. Calling .free() releases the JS wrapper — the underlying encoding data remains cached for reuse. In short-lived scripts you can skip .free(); in long-running apps, call it when you're done with the instance.

Demo

See examples/react-app for a complete Vite + React demo application.

License

MIT