| Model (2,500 Thai chars / ~1 A4 page) | Measured Tokens / Page | Cost per 1,000 A4 Pages | Best Use Case |
|---|---|---|---|
| Gemini 2.5 Flash (1M Context) | ~920 tokens (1.4x) | ฿4.75 ($0.14) | Best overall for bulk Thai PDF/OCR extraction and high-volume RAG. |
| DeepSeek V3 / Qwen 2.5 (via Novita AI) | ~1,080 tokens (1.6x) | ฿20.50 ($0.59) | Sweet spot for structured JSON extraction, Thai reasoning, and agent steps. |
| OpenAI GPT-4o (o200k_base) | ~1,580 tokens (2.4x) | ฿136.30 ($3.95) | Use when multimodal vision/audio or strict enterprise tone is required. |
| Claude 3.7 Sonnet | ~2,120 tokens (3.2x) | ฿219.40 ($6.36) | #1 for complex coding, but high Thai token multiplier—feed compressed context only. |
Never feed raw multi-page Thai PDFs directly into $3/1M models. Use Gemini 2.5 Flash or DeepSeek V3 to extract clean JSON facts first, reducing downstream token volume by 75%.
System instructions written in concise English consume 2–3x fewer tokens on every call while still producing natural Thai responses when you append: "Always reply in natural, polite Thai."
// ตัวอย่างการตั้งค่า System Prompt ให้ประหยัด Token สูงสุด
const systemPrompt = `You are a precise financial document assistant.
Extract invoice_no, date, total_thb, and vat_thb as strict JSON.
Always reply in natural, polite Thai.`;Use an OpenAI-compatible router like Novita AI so your backend can switch between DeepSeek V3, Qwen 2.5, and Llama 3.3 by changing a single model string.
Thai script has no spaces between words and stacks vowels/tone marks vertically. Western-centric BPE tokenizers split Thai words into byte fragments (2–4 tokens per word), whereas Gemini 2.5 and Qwen 2.5 include dedicated multilingual vocabulary entries for whole Thai words.