The Efficiency Benchmark
Comparing the architectural precision of Asai Tokenizer against global LLM tokenizers. Our specialized algorithm treats Tamil as a first-class citizen, reducing sequence length and maximizing semantic density.
Current Test Sample
Loading...
Tokenizer Leaderboard
| Tokenizer Model | No. of Tokens | Efficiency Ratio | Speed |
|---|---|---|---|
|
stars
Asai Tokenizer (Our Model)
Optimized Native Support
|
08 |
|
0.42 ms |
|
api
GPT-4o (ChatGPT)
|
11 |
|
1.12 ms |
|
psychology
Claude 3.5 Sonnet
|
14 |
|
1.45 ms |
|
language
Gemini Pro
|
13 |
|
1.28 ms |
|
data_object
Llama 3 (70B)
|
19 |
|
1.92 ms |
|
terminal
Qwen 2.5
|
12 |
|
1.10 ms |
Why Efficiency Matters?
For languages like Tamil, inefficient tokenization leads to "The Tax of Translation." Every extra token is more compute cost, slower latency, and a shorter effective context window for the user.
payments
Cost Reduction
Lower token count directly reduces API usage costs by up to 60%.
speed
Faster Response
Fewer tokens mean the model generates answers significantly faster.
view_in_ar
Context Utility
Fit 3x more Tamil literature into the same 128k context window.
Visualizing Token Density: The Tamil Symmetry