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KoishiAI
ไทย

How much Thai text fits in each AI model?

Every model takes a limited number of tokens at once (its context window), and Thai costs more tokens than English. Paste a document or enter a character count to see which models take all of it.

Your text never leaves your machine Detailed token counter →
Model context (tokens) fits ≈ Thai characters ≈ articles Your text
Llama 4 Scout 1,310,720 4,245,532 516 –
Qwen3.7 Flash 1,000,000 3,543,223 431 –
Qwen3.7 Max 1,000,000 3,543,223 431 –
Qwen3.7 Plus 1,000,000 3,543,223 431 –
Qwen3.8 27B 1,000,000 3,543,223 431 –
Qwen3.8 Flash 1,000,000 3,543,223 431 –
Qwen3.8 Max 1,000,000 3,543,223 431 –
Gemini 2.5 Flash-Lite 1,048,576 3,092,071 376 –
Gemini 3.1 Pro 1,048,576 3,092,071 376 –
Gemini 3.5 Flash-Lite 1,048,576 3,092,071 376 –
Gemini 3.7 Flash 1,048,576 3,092,071 376 –
Gemini 3.8 Flash 1,048,576 3,092,071 376 –
Amazon Nova 2 Lite 1,000,000 3,067,305 373 –
DeepSeek V4 Flash 1,048,576 3,017,935 367 –
DeepSeek V4 Pro 1,048,576 3,017,935 367 –
DeepSeek V4.1 Flash 1,048,576 3,017,935 367 –
GPT-5.5 1,050,000 2,848,583 346 –
GPT-5.6 Luna 1,050,000 2,848,583 346 –
GPT-5.6 Sol 1,050,000 2,848,583 346 –
GPT-5.6 Terra 1,050,000 2,848,583 346 –
GPT-6 Astra 1,050,000 2,848,583 346 –
GPT-6 Luna 1,050,000 2,848,583 346 –
GPT-6 Sol 1,050,000 2,848,583 346 –
GPT-4.1 mini 1,047,576 2,841,982 345 –
Kimi K3 1,048,576 1,778,385 216 –
Llama 4 Maverick 524,288 1,690,415 205 –
GLM-5.3 1,048,576 1,542,514 187 –
GLM-5.3 Flash 1,048,576 1,542,514 187 –
Grok 4.6 500,000 1,510,582 183 –
Grok 4.7 500,000 1,510,582 183 –
MiniMax M3 1,048,576 1,400,298 170 –
Claude Fable 5 1,000,000 1,360,767 165 –
Claude Fable 5.1 1,000,000 1,360,767 165 –
Claude Opus 5 1,000,000 1,360,767 165 –
Claude Opus 5.5 1,000,000 1,360,767 165 –
Claude Sonnet 5 1,000,000 1,360,767 165 –
GPT-5 nano 400,000 1,078,431 131 –
GPT-5.4 mini 400,000 1,078,431 131 –
Qwen3.6 35B-A3B 262,144 918,335 111 –
Mistral Medium 3.5 262,144 561,148 68 –
Qwen3 235B-A22B 262,144 549,476 66 –
Qwen3 30B-A3B 262,144 549,476 66 –
Kimi K2.5 262,144 435,648 53 –
Kimi K2.6 262,144 435,648 53 –
GPT-4o mini 128,000 337,690 41 –
Llama 3.3 70B 131,072 318,037 38 –
Claude Haiku 4.5 200,000 284,828 34 –
MiniMax M2.7 204,800 269,180 32 –

What a context window is

  • The most tokens a model takes in one exchange — instructions, attached documents, chat history and the answer it writes. That is why an answer reserve is subtracted.
  • Thai tokens per character differ a lot between models, so a bigger context does not always hold more Thai.
  • Fitting is not the same as reading well. “Lost in the Middle” (Liu et al., TACL 2023) found that the models of the time used information in the middle of long inputs worse than at the start or end. For very long documents, put the question and the key parts first or last.

How it is calculated

  • Thai tokens per character were measured on 109 real Thai articles on this site, which mix in English terms like typical tech news. Pure Thai may take slightly more.
  • GPT, Qwen and Typhoon use their real tokenizers; the other families use ratios measured through OpenRouter against GPT (see the token counter page).
  • Context sizes come from OpenRouter on every site rebuild; latest 2026-10-21. Some providers serve a smaller context than the model supports.
  • With a pasted document: GPT is counted exactly; the rest are estimated from your text’s share of Thai letters.