A faster than tiktoken tokenizer with first-class support for Vercel's AI SDK.
- Supports the AI SDK tool and message schema
- No WASM; highly portable; 5-7x faster than tiktoken WASM
- >=97% accuracy for top models (GPT-5, Claude Sonnet, Gemini):
| Model | ~500 tokens | ~5k tokens | ~50k tokens |
|---|---|---|---|
| openai/gpt-5 | 99.16% | 99.43% | 99.47% |
| anthropic/claude-sonnet-4.5 | 98.48% | 98.91% | 99.70% |
| google/gemini-2.5-pro | 98.37% | 99.42% | 99.82% |
Install:
bun install ai-tokenizerEstimate tokens with the AI SDK:
import Tokenizer, { models } from "ai-tokenizer";
import { count } from "ai-tokenizer/sdk";
import * as encoding from "ai-tokenizer/encoding";
// Find the respective model.
const model = models["openai/gpt-5"];
const tokenizer = new Tokenizer(encoding[model.encoding]);
const result = count({
tokenizer,
model,
// Every message is counted separately
messages,
// Tool descriptions and input schemas are counted
tools,
})
// {
// total: 150,
// messages: [
// {
// total: 120,
// content: [
// { type: "text", total: 100 },
// { type: "tool-call", total: 20, input: 15 }
// ]
// }
// ],
// tools: {
// total: 30,
// definitions: {
// myTool: {
// name: 5,
// description: 10,
// inputSchema: 15
// }
// }
// }
// }
// No API calls are performed; no keys.
const costUSD = result.total * model.pricing.input;Estimate tokens using an encoding:
import { Tokenizer } from "ai-tokenizer"
import * as o200k_base from "ai-tokenizer/encoding/o200k_base"
const tokenizer = new Tokenizer(o200k_base);
const total = tokenizer.count("some text input");Warning
Import encodings selectively based on what you need. Each encoding is 2-8MB uncompressed.
Validated against actual API responses with pseudo-random messages:
| Model | ~500 tokens | ~5k tokens | ~50k tokens |
|---|---|---|---|
| alibaba/qwen-3-14b | 96.45% | 96.45% | N/A |
| alibaba/qwen-3-235b | 98.26% | 96.38% | N/A |
| alibaba/qwen-3-30b | 96.45% | 96.27% | N/A |
| alibaba/qwen-3-32b | 95.64% | 95.70% | N/A |
| alibaba/qwen3-coder | 94.55% | 96.70% | 94.57% |
| alibaba/qwen3-coder-plus | 94.84% | 96.76% | 94.54% |
| alibaba/qwen3-max | 98.51% | 95.59% | 94.33% |
| alibaba/qwen3-next-80b-a3b-instruct | 95.66% | 96.71% | 95.81% |
| alibaba/qwen3-next-80b-a3b-thinking | 96.02% | 95.70% | 95.09% |
| alibaba/qwen3-vl-instruct | 95.08% | 96.73% | 96.04% |
| amazon/nova-lite | 98.10% | 92.67% | 90.17% |
| amazon/nova-micro | 98.10% | 92.10% | 89.20% |
| amazon/nova-pro | 98.10% | 92.82% | 90.16% |
| anthropic/claude-3-haiku | 97.61% | 99.32% | 99.77% |
| anthropic/claude-3-opus | 98.66% | 98.68% | 99.70% |
| anthropic/claude-3.5-haiku | 97.61% | 99.80% | 99.86% |
| anthropic/claude-3.5-sonnet | 97.93% | 99.26% | 99.67% |
| anthropic/claude-3.5-sonnet-20240620 | 98.01% | 99.98% | 99.86% |
| anthropic/claude-3.7-sonnet | 98.07% | 99.59% | 99.74% |
| anthropic/claude-opus-4 | 98.07% | 99.39% | 99.74% |
| anthropic/claude-opus-4.1 | 97.78% | 99.83% | 99.80% |
| anthropic/claude-sonnet-4 | 98.22% | 99.89% | 99.83% |
| anthropic/claude-sonnet-4.5 | 98.48% | 98.91% | 99.70% |
| deepseek/deepseek-r1 | 96.24% | 98.67% | 98.10% |
| deepseek/deepseek-r1-distill-llama-70b | 98.20% | 99.08% | N/A |
| deepseek/deepseek-v3 | 95.30% | 95.37% | 95.19% |
| deepseek/deepseek-v3.1 | 97.40% | 95.85% | 96.46% |
| deepseek/deepseek-v3.1-terminus | 96.20% | 97.57% | 97.55% |
| deepseek/deepseek-v3.2-exp | 95.77% | 97.62% | 97.45% |
| deepseek/deepseek-v3.2-exp-thinking | 95.98% | 97.55% | 97.56% |
| google/gemini-2.0-flash | 97.97% | 99.69% | 99.95% |
| google/gemini-2.0-flash-lite | 98.18% | 99.65% | 99.77% |
| google/gemini-2.5-flash | 98.78% | 99.60% | 99.85% |
| google/gemini-2.5-flash-lite | 99.19% | 99.33% | 99.84% |
| google/gemini-2.5-flash-lite-preview-09-2025 | 92.74% | 99.63% | 99.74% |
| google/gemini-2.5-flash-preview-09-2025 | 92.53% | 99.69% | 99.74% |
| google/gemini-2.5-pro | 98.37% | 99.42% | 99.82% |
| meituan/longcat-flash-chat | 70.65% | 99.15% | 96.13% |
| meta/llama-3.1-8b | 98.69% | 90.67% | 89.69% |
| meta/llama-3.3-70b | 100.00% | 70.03% | 67.23% |
| meta/llama-4-scout | 0.00% | 56.65% | 66.03% |
| mistral/codestral | 93.78% | 91.86% | 91.89% |
| mistral/devstral-small | 98.77% | 94.09% | 93.72% |
| mistral/magistral-medium | 98.24% | 94.23% | 93.71% |
| mistral/magistral-small | 97.89% | 94.32% | 93.65% |
| mistral/mistral-large | 98.23% | 94.09% | N/A |
| mistral/mistral-medium | 98.06% | 93.96% | 93.79% |
| mistral/mistral-small | 96.73% | 92.78% | N/A |
| mistral/pixtral-large | 98.94% | 94.25% | 94.27% |
| moonshotai/kimi-k2 | 89.51% | NaN% | 82.80% |
| moonshotai/kimi-k2-0905 | 85.58% | 81.01% | 80.56% |
| moonshotai/kimi-k2-turbo | 94.51% | 92.36% | 91.83% |
| openai/gpt-4-turbo | 99.79% | 99.89% | 99.86% |
| openai/gpt-4.1 | 100.00% | 99.93% | 99.92% |
| openai/gpt-4.1-mini | 100.00% | 99.93% | 99.92% |
| openai/gpt-4o | 100.00% | 99.93% | 99.92% |
| openai/gpt-4o-mini | 100.00% | 99.93% | 99.92% |
| openai/gpt-5 | 99.16% | 99.43% | 99.47% |
| openai/gpt-5-codex | 99.17% | 99.43% | 99.47% |
| openai/gpt-5-mini | 99.16% | 99.43% | 99.47% |
| openai/gpt-5-nano | 99.16% | 99.43% | 99.46% |
| openai/gpt-5-pro | 99.17% | 99.43% | 99.47% |
| openai/gpt-oss-120b | 80.77% | 69.64% | 69.62% |
| openai/gpt-oss-20b | 68.55% | 60.79% | 60.98% |
| openai/o1 | 94.77% | 99.30% | 99.86% |
| openai/o3 | 99.16% | 99.43% | 99.46% |
| openai/o3-mini | 100.00% | NaN% | 99.92% |
| openai/o4-mini | 99.16% | 99.43% | 99.47% |
| stealth/sonoma-dusk-alpha | 99.47% | 71.65% | 66.69% |
| stealth/sonoma-sky-alpha | 99.46% | 69.52% | 66.49% |
| vercel/v0-1.0-md | 98.99% | 53.45% | 92.42% |
| vercel/v0-1.5-md | 98.96% | 53.55% | 92.39% |
| xai/grok-2 | 95.43% | 96.09% | 95.28% |
| xai/grok-2-vision | 94.44% | 95.68% | N/A |
| xai/grok-3 | 99.57% | 98.67% | 99.23% |
| xai/grok-3-fast | 99.57% | 98.59% | 99.13% |
| xai/grok-3-mini | 99.59% | 98.76% | 99.18% |
| xai/grok-3-mini-fast | 99.59% | 98.59% | 99.13% |
| xai/grok-4 | 99.72% | 73.95% | 67.43% |
| xai/grok-4-fast-non-reasoning | 90.02% | -22.27% | -52.27% |
| xai/grok-4-fast-reasoning | 99.29% | 71.17% | 66.72% |
| xai/grok-code-fast-1 | 80.14% | -13.63% | -51.13% |
| zai/glm-4.5 | 94.55% | 96.26% | 96.16% |
| zai/glm-4.5-air | 96.73% | 96.45% | 96.70% |
| zai/glm-4.5v | 95.17% | 95.56% | 95.36% |
Accuracy shows percentage within actual token count.
Warning
Not every tool/token scenario is tested for accuracy. There will be edge-cases where this is more/less accurate. If you find this inaccurate for your scenario, please open an issue.
Run bun run scripts/generate-accuracy.ts to update this table. Refer to accuracy.json for greater detail.
ai-tokenizer is 5-7x faster than tiktoken for counting tokens. It is on par with gpt-tokenizer.
$ bun bench/versus
clk: ~3.96 GHz
cpu: AMD Ryzen AI 9 HX 370 w/ Radeon 890M
runtime: bun 1.2.19 (x64-linux)
benchmark avg (min … max) p75 / p99 (min … top 1%)
------------------------------------------- -------------------------------
• initialization
------------------------------------------- -------------------------------
ai-tokenizer 13.99 µs/iter 14.14 µs █
(13.44 µs … 15.50 µs) 14.85 µs █
( 0.00 b … 1.24 kb) 308.13 b ██▁█▁█▁▁▁▁█▁▁▁▁▁▁▁▁▁█
tiktoken 85.05 ms/iter 85.68 ms █ █
(81.61 ms … 89.19 ms) 86.55 ms ▅ █ ▅ ▅▅█▅▅ ▅
( 0.00 b … 1.39 mb) 385.33 kb █▁▁▁▁▁▁▁█▁▁█▁█████▁▁█
• encode: small text (~13 chars)
------------------------------------------- -------------------------------
ai-tokenizer 1.33 µs/iter 1.34 µs ▇█
(1.11 µs … 1.90 µs) 1.60 µs ██
( 0.00 b … 2.06 kb) 1.60 kb ▆▃▁▁▁▁▁▂███▃▄▂▃▂▃▂▁▁▂
gpt-tokenizer 1.05 µs/iter 1.13 µs █
(926.82 ns … 1.65 µs) 1.53 µs ██ ▆
( 0.00 b … 864.00 b) 352.86 b ██▂▁▁▁██▆▃▂▂▁▁▁▁▁▁▁▁▁
tiktoken 32.23 µs/iter 30.19 µs █
(27.83 µs … 468.68 µs) 66.56 µs █
( 0.00 b … 1.13 mb) 5.27 kb ██▃▂▂▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁
• encode: medium text (~4.5KB)
------------------------------------------- -------------------------------
ai-tokenizer 89.81 µs/iter 92.53 µs █
(66.07 µs … 372.65 µs) 208.17 µs █ █▇
( 0.00 b … 1.13 mb) 98.84 kb ▄█▃██▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁
gpt-tokenizer 76.97 µs/iter 80.01 µs █ ▆
(66.79 µs … 279.16 µs) 115.17 µs █ █▅
( 0.00 b … 384.00 kb) 5.44 kb ▆█▅█▆██▃▂▁▂▂▁▂▂▁▁▁▁▁▁
tiktoken 651.22 µs/iter 649.48 µs ▄█
(632.83 µs … 874.11 µs) 782.25 µs ██
( 0.00 b … 192.00 kb) 4.31 kb ▄██▆▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁
• encode: large text (~500KB)
------------------------------------------- -------------------------------
ai-tokenizer 19.07 ms/iter 20.23 ms ▅█
(15.19 ms … 20.67 ms) 20.66 ms ▃ ▃██▃▃
( 0.00 b … 26.44 mb) 19.22 mb ▄▁▄▄█▁█▁▁▁▄▁▁▁▄▄█████
gpt-tokenizer 18.19 ms/iter 18.62 ms █
(15.35 ms … 23.68 ms) 22.91 ms █ ▃
( 0.00 b … 31.50 mb) 6.44 mb ▅▃▅▁█▁█▃▃▃▁▁█▁▁▁▁▅▁▁▅
tiktoken 135.38 ms/iter 135.35 ms █ █ █
(134.51 ms … 138.22 ms) 136.61 ms █▅ ▅ ▅█ █▅ ▅
( 0.00 b … 768.00 kb) 648.00 kb ██▁█▁██▁██▁▁▁▁▁▁▁▁▁▁█
• encode: unicode text
------------------------------------------- -------------------------------
ai-tokenizer 118.76 µs/iter 118.95 µs █
(84.61 µs … 500.48 µs) 234.66 µs ██
( 0.00 b … 768.00 kb) 129.37 kb ▃▄▂██▇▄▄▂▂▂▂▁▁▁▁▁▁▁▁▁
gpt-tokenizer 231.35 µs/iter 256.95 µs █▅▇█
(139.24 µs … 2.08 ms) 489.02 µs ████ █
( 0.00 b … 2.41 mb) 260.61 kb ▃▅████▇▆█▆▃▂▂▂▁▁▁▂▁▁▁
tiktoken 836.84 µs/iter 819.36 µs █
(799.44 µs … 1.97 ms) 1.39 ms █▅
( 0.00 b … 192.00 kb) 5.87 kb ██▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
• encode: code
------------------------------------------- -------------------------------
ai-tokenizer 357.64 µs/iter 372.83 µs █▄
(278.58 µs … 1.04 ms) 591.56 µs ▃ ██
( 0.00 b … 1.31 mb) 364.10 kb ██▂▁▂██▅▃▂▂▁▂▂▁▁▁▁▁▁▁
gpt-tokenizer 316.39 µs/iter 316.25 µs █▅
(279.98 µs … 919.92 µs) 622.31 µs ██
( 0.00 b … 192.00 kb) 54.12 kb ▄██▅▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
tiktoken 3.12 ms/iter 3.12 ms ▆█
(3.05 ms … 3.72 ms) 3.52 ms ██▃
( 0.00 b … 192.00 kb) 20.06 kb ▆███▅▃▁▁▁▁▂▂▂▂▁▁▂▁▁▁▂
• encode: mixed content
------------------------------------------- -------------------------------
ai-tokenizer 2.44 ms/iter 2.48 ms ▇█▄
(2.06 ms … 2.89 ms) 2.81 ms ▂████▃
( 0.00 b … 3.56 mb) 2.36 mb ▁▂▂▁▁▁▁██████▄▃▆▄▂▂▁▂
gpt-tokenizer 2.62 ms/iter 2.60 ms █
(2.38 ms … 4.34 ms) 3.96 ms ▅█
(576.00 kb … 4.88 mb) 1.34 mb ▄███▄▂▂▁▁▁▁▂▁▁▂▁▁▁▁▁▁
tiktoken 15.35 ms/iter 15.37 ms ▂█ ▂
(15.17 ms … 16.35 ms) 15.80 ms ██▇█▂
( 0.00 b … 192.00 kb) 93.54 kb █████▇▄▁▄▄▄▁▄▄▄▁▁▇▄▁▄
• decode: large token array
------------------------------------------- -------------------------------
ai-tokenizer 1.57 ms/iter 1.93 ms █
(1.14 ms … 4.02 ms) 3.53 ms ██
( 0.00 b … 6.94 mb) 1.44 mb ██▄▃▁▁▁▁▁▂▃▄▄▃▂▁▁▁▁▁▁
gpt-tokenizer 876.67 µs/iter 857.62 µs █
(628.32 µs … 3.95 ms) 2.34 ms █
( 0.00 b … 6.00 mb) 27.15 kb ▁██▃▄▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁
tiktoken 1.64 ms/iter 1.67 ms ▄ █
(1.47 ms … 2.49 ms) 2.33 ms ▆█ ██▃
( 0.00 b … 1.31 mb) 1.14 mb ██████▅▆▂▃▁▁▂▁▂▂▁▁▂▁▁
• count: large text (~500KB)
------------------------------------------- -------------------------------
ai-tokenizer 18.33 ms/iter 19.52 ms █
(14.94 ms … 20.09 ms) 20.05 ms ▃▆ ▆██▃
( 0.00 b … 25.33 mb) 18.64 mb ██▁▁▁▄▁▁▁▁▁▁▄▄▄█████▄
gpt-tokenizer 16.65 ms/iter 18.26 ms █ █
(14.63 ms … 19.14 ms) 19.01 ms ███ ▅ █
( 0.00 b … 7.50 mb) 1.15 mb ▅████▁▅▅▁▁█▅▁▅██▁██▁█
tiktoken 131.95 ms/iter 131.85 ms █
(130.24 ms … 138.35 ms) 133.42 ms █ █
(576.00 kb … 768.00 kb) 720.00 kb █▁██▁█▁█▁▁█▁█▁▁▁▁▁▁▁█Run this yourself with bun bench/versus.