AiGanakWhat Malayalam costs on LLM APIs
The same customer messages take 1.73× to 4.50× as many tokens in Malayalam as in English, depending on the model, and API bills follow tokens. Gemini 3.x counts the fewest tokens for Malayalam; Claude 5.x the most.
The same 12 business messages take 1.85× on OpenAI GPT-6, 4.50× on Claude 5.x, 1.73× on Gemini 3.x, 2.00× on Gemini 4 Argon as many tokens in Malayalam as in English (official token counters, 4 November 2026), so they cost that many times as much. Gemini 3.x uses the fewest tokens for Malayalam, Claude 5.x the most. Cheapest listed model for 100,000 messages and replies a month: GPT-6 Luna at $2.56, against $1.38 in English on the same model (list prices checked 2 November 2026).
Malayalam against English, by model family
1.00× = English on the same model. Tokens are what you are billed for, so 2.00× means the same conversation costs twice as much.
| Model family | Malayalam tokens | English tokens | Multiple of English | Malayalam tokens per message |
|---|---|---|---|---|
| OpenAI GPT-6 | 511 | 276 | 1.85× | 42.6 |
| Claude 5.x | 1822 | 405 | 4.50× | 151.8 |
| Gemini 3.x | 504 | 292 | 1.73× | 42.0 |
| Gemini 4 Argon | 584 | 292 | 2.00× | 48.7 |
Models counted together: OpenAI GPT-6: gpt-6-astra, gpt-6.1-sol, gpt-6.1-terra, gpt-6-luna; Claude 5.x: claude-opus-5-5, claude-sonnet-5-5, claude-haiku-5-5, claude-fable-5-1; Gemini 3.x: gemini-3.1-pro-preview, gemini-3.8-flash, gemini-3.5-flash-lite; Gemini 4 Argon: gemini-4-argon. Corpus, counts and counting script are open on GitHub under CC BY 4.0. Native-speaker corrections are welcome.
Malayalam in the index
- On Claude 5.x, Malayalam takes 3.62× as many tokens as on Gemini 3.x for the same text, the widest gap between model families for this language.
- Rank among the 22 languages measured (1 = most tokens): 6th on OpenAI GPT-6, 4th on Claude 5.x, 6th on Gemini 3.x, 5th on Gemini 4 Argon.
- On Gemini 3.x, the most token-efficient family for Malayalam, Malayalam takes 1.73× English against an average of 1.71× for the other 11 Indian languages in the index.
Monthly cost of 100,000 messages in Malayalam
Each message goes in once and gets a reply of the same length, at each model's standard input and output list price, no caching. The cheapest listed model for Malayalam is GPT-6 Luna at $2.56 a month.
| Model | In Malayalam | In English |
|---|---|---|
| GPT-6 Luna OpenAI | $2.56 | $1.38 |
| Claude Haiku 5.5 Anthropic | $9.11 | $2.03 |
| Gemini 3.5 Flash-Lite Google | $11.76 | $6.81 |
| Gemini 3.8 Flash Google | $18.90 | $10.95 |
| GPT-6.1 Terra OpenAI | $25.55 | $13.80 |
| GPT-6.1 Sol OpenAI | $51.10 | $27.60 |
| Gemini 4 Argon Google | $58.40 | $29.20 |
| Gemini 3.1 Pro (preview) Google | $58.80 | $34.07 |
| Claude Sonnet 5.5 Anthropic | $182.20 | $40.50 |
| GPT-6 Astra OpenAI | $255.50 | $138.00 |
| Claude Opus 5.5 Anthropic | $364.40 | $81.00 |
| Claude Fable 5.1 Anthropic | $911.00 | $202.50 |
Price per token and tokens per message both matter, and quality on your own task decides whether a cheaper model is usable. Gemini 4 Argon's announced price rise from 1 December 2026 is not included; see the price changes log.
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Malayalam LLM cost: questions
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Does Malayalam cost more than English on LLM APIs?
Yes. In AiGanak's measurement with official token counters (4 November 2026), the same 12 business messages took 1.85× on OpenAI GPT-6, 4.50× on Claude 5.x, 1.73× on Gemini 3.x, 2.00× on Gemini 4 Argon as many tokens in Malayalam as in English, and API bills follow tokens.
Which LLM is cheapest for Malayalam?
Gemini 3.x used the fewest tokens for Malayalam in our test. Counting price as well, the cheapest listed model for 100,000 messages and same-length replies a month is GPT-6 Luna at $2.56, against $1.38 for English on the same model (list prices checked 2 November 2026). Test quality on your own examples before choosing.
How can I reduce LLM costs for Malayalam users?
Compare model families, because Claude 5.x counts the most tokens for Malayalam. Keep system prompts and tool definitions in English, cache the fixed part of the prompt, and at high volume consider a small model with a tokenizer trained for your language.
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