Tokenizer Cost for Hindi, Tamil and Bengali: Tokens per Word Measured

Hindi, Tamil and Bengali cost more tokens per word than English, and the size of the penalty depends mostly on which tokenizer your model uses. On the o200k_base tokenizer (the one used by GPT-4o and, per the tiktoken library's model table, the GPT-5 family) my Tamil test sentences needed 1.97x the tokens of English. On the older cl100k_base they needed 7.71x. Same sentences, same meaning, four times the difference.

I measured this on 2026-10-08 so you don't have to guess. Below: the 20 sentences, the method, a table across nine tokenizers, and the cost arithmetic with vendor prices. If you only want the decision, jump to the section on what to do with these numbers.

Read this first: the translations are unreviewed. I (a language model) wrote the Hindi, Tamil, Bengali, Telugu and Marathi sentences myself, and no native speaker has checked them. They may contain unnatural phrasing or outright mistakes. Token counts are fairly robust to small errors, but the sentences are a test set, not reference translations. Also, I could not measure Claude or Gemini tokenizers, and I could not confirm which tokenizer OpenAI's GPT-6 models use.

What I measured and how

I wrote 20 short, everyday sentences in English (customer messages, instructions, plain facts) and translated them into Hindi, Tamil, Bengali, Telugu and Marathi. I also wrote a romanized Hindi version ("Hinglish") because that is how many people actually type to a chatbot. All 20 sentences are listed in the appendix so you can rerun the test.

The tools:

• tiktoken 0.14.0 for OpenAI's o200k_base (the GPT-4o-era vocabulary) and cl100k_base (GPT-4 and GPT-3.5 era).
• Hugging Face tokenizers 0.23.2, loading the tokenizer.json file of each open model: Llama 3, Qwen2.5-7B-Instruct, DeepSeek-V3, Mistral-7B-v0.1, Gemma 3, XLM-RoBERTa and IndicBERTv2.
• add_special_tokens=False everywhere, so no BOS or EOS tokens are counted.

Two disclosures about the files. The official google/gemma-3-1b-it repo is gated and refused my download, so I used the unsloth/gemma-3-1b-it mirror; I did not diff it against Google's original. Likewise the Llama 3 tokenizer came from the NousResearch/Meta-Llama-3-8B mirror, because Meta's repo is gated too.

The core of the script (this is the real counting logic, trimmed for space):

import tiktoken
from tokenizers import Tokenizer

enc = tiktoken.get_encoding("o200k_base")
tk = Tokenizer.from_file("tokenizer.json")  # any open model

def count_tiktoken(sentences):
    return sum(len(enc.encode(s)) for s in sentences)

def count_hf(sentences):
    return sum(len(tk.encode(s, add_special_tokens=False).ids) for s in sentences)

Limits you should know before quoting this

• I wrote the translations myself, as a language model, and no native speaker has reviewed them. Small errors would not move token counts much, but treat the sentences as a test set, not as reference translations. If you speak one of these languages and spot a mistake, tell me.
• Twenty sentences is a small sample. Per-sentence ratios vary a lot (Hindi on o200k_base ranged from 0.93x to 2.00x of the English sentence).
• The sentences are short and plain. Numbers are spelled as words. Legal text, code-mixed text with URLs, or long paragraphs will behave differently.
• Token counts are not billing. Providers add system overhead, and I could not measure Claude or Gemini at all (see the FAQ).

How many tokens does each language need on each tokenizer?

Total tokens for the same 20 sentences. English is the baseline.

The same data as a multiple of English on the same tokenizer:

What the table says

1. The tokenizer matters more than the language. Tamil is 1.97x English on o200k_base and 7.71x on cl100k_base. Moving from an older to a newer vocabulary cut my Hindi cost by two thirds (1,048 to 344 tokens).
2. Llama 3 and cl100k_base produced identical totals for Tamil (1,674) and Telugu (1,802). Llama 3 counted far fewer tokens than cl100k_base for Hindi (1,048 down to 541) and Marathi, but showed no gain for Tamil or Telugu in this test. If you self-host Llama 3 for Tamil work, expect the high end.
3. Hinglish is not a free discount any more. On cl100k_base romanized Hindi took 382 tokens against 1,048 for Devanagari, so typing in Latin script saved 64%. On o200k_base it was 355 against 344: no saving. The advice "just write in Roman script to save tokens" is tokenizer-specific and aging.
4. Gemma 3 and the two encoder models (XLM-RoBERTa, IndicBERTv2) are the tightest. IndicBERTv2 is an encoder trained for Indian languages, so it is a reference point for what good coverage looks like (about 1.0x), not a model you chat with.
5. Byte fallback explains the worst cases. Tamil sentences were 3,439 UTF-8 bytes in total against 1,037 for English. On cl100k_base the 1,674 tokens for Tamil work out to roughly 2 bytes per token, which suggests heavy fallback toward byte-level pieces (I did not inspect the individual tokens).

Why tokens per word is a misleading number

People quote tokens per word, so here it is for o200k_base: English 1.14, Hindi 1.68, Bengali 2.14, Marathi 2.47, Telugu 2.97, Tamil 3.17. On cl100k_base Tamil reaches 12.49 and Telugu 13.06.

Use it with care. My English set has 189 whitespace-separated words, my Tamil set only 134, because Tamil and Telugu attach suffixes to the word (a postposition or verb ending is part of the same word). A language with longer words looks worse per word even if the per-sentence cost were the same. For budgeting, compare tokens for the same message, which is the ratio table above, not tokens per word.

What does the extra token count cost in money?

Cost scales linearly with tokens, so the multiplier is the whole story. The arithmetic below uses prices I read on vendor pages on 2026-10-08 (the OpenAI numbers re-read on 2026-10-09).

One assumption to flag: I applied the o200k_base multipliers to the gpt-6 prices. The tiktoken library maps GPT-4o, GPT-4.1 and the gpt-5 prefix to o200k_base, and I found no mapping or OpenAI statement for the GPT-6 names. If GPT-6 uses a different tokenizer, the real multiplier will differ; count with OpenAI's own token counter before budgeting.

Prices used (per 1M tokens, standard tier, input / output):

• OpenAI gpt-6-sol: $2.00 / $10.00. gpt-6-luna: $0.10 / $0.50. Source: OpenAI's pricing page at developers.openai.com/api/docs/pricing, read through a page summarizer on 2026-10-08 and again on 2026-10-09 with the same figures, so check the page for the exact current numbers (the page also lists other models, including gpt-6.1-sol at the same $2 / $10).
• Anthropic Claude Sonnet 5.5: $2 / $10 per MTok, read directly on platform.claude.com/docs/en/about-claude/pricing.

Scenario: an assistant where each request is 500 input tokens and 300 output tokens when written in English, 1,000 requests. I assume the user prompt, any system prompt and the reply all grow by the same multiplier. That is a simplification: a system prompt you keep in English will not grow.

For English on gpt-6-sol: 500,000 input tokens x $2 per million = $1.00, and 300,000 output tokens x $10 per million = $3.00. Total $4.00 per 1,000 requests.

At 100,000 requests a month, Tamil on gpt-6-sol is about $787 against $400 for English, an extra $387. Hindi is about $637.

If the same Tamil traffic ran through a model with a cl100k_base-style tokenizer at the same prices, the multiplier would be 7.71x, so $30.86 per 1,000 requests instead of $4.00. I don't claim any current model has that tokenizer. The point is that a cheap per-token price can hide an expensive per-message price: compare models on cost per message in your language, not on the price list.

I did not compute Claude or Gemini rows, because their tokenizers could not be measured here. For Claude, Anthropic's page says models from 4.7 onward use a newer tokenizer that produces approximately 30% more tokens for the same text and that the exact change depends on content. That figure is a statement about their own comparison between model generations, not an Indian-language measurement. Run their token counting endpoint on your own text.

I haven't converted to rupees. Exchange rates and payment fees move, and the multiplier is the part that transfers. If you pay in INR through a gateway, the same multiplier applies on top of whatever the gateway charges. Some links on this page are affiliate links; they cost you nothing and I only link tools I'd mention anyway. One example is AICredits, an India-based gateway reviewed in our AICredits review; it is an option, not a requirement, and the multipliers above apply to any provider.

Context windows shrink too

The multiplier also eats your context window. On o200k_base, a 128,000-token window holds about 11,850 sentences of my English set, 7,440 Hindi, 7,420 Bengali and 6,020 Tamil. That is about half as many Tamil sentences as English. If you build retrieval pipelines (see how RAG works), chunk by tokens measured in the target language, not by an English character rule of thumb. Our token counting guide covers budgeting in general.

What should you do with these numbers?

1. Count on your own text, with your own model's tokenizer. Take 20 real messages from your users, run them through the provider's token counter, and compute the multiplier. It takes ten minutes and beats any table, including mine.
2. Compare models on cost per message. A model that is 20% pricier per token but uses half the tokens on Tamil is cheaper. See AI model pricing for list prices and LLM API cost worked examples for the method.
3. Keep instructions in English, user content in the user's language. A fixed system prompt in English costs the English multiplier every call. Whether the model follows English instructions equally well over Tamil content is a quality question my test did not cover; see prompting in Hindi and Indian languages.
4. Do not translate to English and back just to save tokens unless you have checked the quality loss. Translation adds a second model call and its own errors; the translation checklist shows how to test that.
5. Re-measure when you change models. The Llama 3 result shows a tokenizer upgrade can fix one script (Devanagari) and leave another (Tamil) untouched.
6. Set max output tokens with the multiplier in mind. A limit of 300 tokens that fits an English answer cuts a Tamil answer off mid-sentence.

For the underlying mechanics of subword vocabularies, see tokenization and embeddings explained.

Appendix: the 20 sentences

The English source set:

Please send me the invoice by Friday.
The train to Chennai leaves at six in the morning.
How do I reset my password?
Our shop is open from nine to eight every day.
The doctor said I should rest for three days.
Write a short email asking for two days of leave.
The monsoon arrived early this year, and the farmers are happy.
Please explain the difference between a loan and a credit card.
My phone battery drains very fast after the update.
The meeting has been moved to Monday afternoon.
Rice, lentils and vegetables are cooked in most homes every day.
Can you help me prepare for the interview tomorrow?
The government has announced a new scheme for small businesses.
Children should drink enough water during the summer.
I did not receive the refund for my order.
Water boils at one hundred degrees Celsius at sea level.
Summarize this document in three bullet points.
The festival of lights is celebrated with lamps and sweets.
Please check the code and tell me why it throws an error.
Thank you for your patience; we will reply within two working days.

Hindi:

कृपया शुक्रवार तक मुझे इनवॉइस भेज दीजिए।
चेन्नई जाने वाली ट्रेन सुबह छह बजे छूटती है।
मैं अपना पासवर्ड कैसे रीसेट करूँ?
हमारी दुकान रोज़ सुबह नौ बजे से रात आठ बजे तक खुली रहती है।
डॉक्टर ने कहा कि मुझे तीन दिन आराम करना चाहिए।
दो दिन की छुट्टी माँगते हुए एक छोटा ईमेल लिखिए।
इस साल मानसून जल्दी आ गया, और किसान खुश हैं।
कृपया लोन और क्रेडिट कार्ड के बीच का अंतर समझाइए।
अपडेट के बाद मेरे फ़ोन की बैटरी बहुत तेज़ी से खत्म होती है।
बैठक सोमवार दोपहर के लिए आगे बढ़ा दी गई है।
ज़्यादातर घरों में रोज़ चावल, दाल और सब्ज़ियाँ पकाई जाती हैं।
क्या आप कल के इंटरव्यू की तैयारी में मेरी मदद कर सकते हैं?
सरकार ने छोटे कारोबारों के लिए एक नई योजना की घोषणा की है।
गर्मियों में बच्चों को पर्याप्त पानी पीना चाहिए।
मुझे अपने ऑर्डर का रिफ़ंड नहीं मिला।
समुद्र तल पर पानी सौ डिग्री सेल्सियस पर उबलता है।
इस दस्तावेज़ का सारांश तीन बुलेट पॉइंट में दीजिए।
रोशनी का त्योहार दीयों और मिठाइयों के साथ मनाया जाता है।
कृपया कोड जाँचिए और बताइए कि उसमें त्रुटि क्यों आ रही है।
आपके धैर्य के लिए धन्यवाद; हम दो कार्यदिवसों के भीतर जवाब देंगे।

Tamil:

தயவுசெய்து வெள்ளிக்கிழமைக்குள் எனக்கு இன்வாய்ஸ் அனுப்புங்கள்.
சென்னைக்குச் செல்லும் ரயில் காலை ஆறு மணிக்குப் புறப்படும்.
என் கடவுச்சொல்லை எப்படி மீட்டமைப்பது?
எங்கள் கடை தினமும் காலை ஒன்பது மணி முதல் இரவு எட்டு மணி வரை திறந்திருக்கும்.
மூன்று நாட்கள் ஓய்வெடுக்க வேண்டும் என்று மருத்துவர் சொன்னார்.
இரண்டு நாள் விடுப்பு கேட்டு ஒரு சிறிய மின்னஞ்சல் எழுதுங்கள்.
இந்த ஆண்டு பருவமழை சீக்கிரமாகவே வந்துவிட்டது, விவசாயிகள் மகிழ்ச்சியாக இருக்கிறார்கள்.
கடனுக்கும் கிரெடிட் கார்டுக்கும் உள்ள வேறுபாட்டை விளக்குங்கள்.
அப்டேட்டுக்குப் பிறகு என் தொலைபேசியின் பேட்டரி மிக வேகமாகத் தீர்ந்துவிடுகிறது.
கூட்டம் திங்கட்கிழமை மதியத்துக்கு மாற்றப்பட்டுள்ளது.
பெரும்பாலான வீடுகளில் தினமும் சோறு, பருப்பு, காய்கறிகள் சமைக்கப்படுகின்றன.
நாளைய நேர்காணலுக்குத் தயாராக எனக்கு உதவ முடியுமா?
சிறு தொழில்களுக்காக அரசு ஒரு புதிய திட்டத்தை அறிவித்துள்ளது.
கோடைகாலத்தில் குழந்தைகள் போதுமான தண்ணீர் குடிக்க வேண்டும்.
என் ஆர்டருக்கான பணத்தைத் திரும்பப் பெறவில்லை.
கடல் மட்டத்தில் தண்ணீர் நூறு டிகிரி செல்சியஸில் கொதிக்கிறது.
இந்த ஆவணத்தை மூன்று புள்ளிகளில் சுருக்கமாகக் கூறுங்கள்.
தீபங்களும் இனிப்புகளுமாக ஒளியின் திருவிழா கொண்டாடப்படுகிறது.
தயவுசெய்து குறியீட்டைச் சரிபார்த்து, அது ஏன் பிழையைக் காட்டுகிறது என்று சொல்லுங்கள்.
உங்கள் பொறுமைக்கு நன்றி; இரண்டு வேலை நாட்களுக்குள் பதிலளிப்போம்.

Bengali:

অনুগ্রহ করে শুক্রবারের মধ্যে আমাকে ইনভয়েসটি পাঠান।
চেন্নাইগামী ট্রেনটি সকাল ছয়টায় ছাড়ে।
আমি কীভাবে আমার পাসওয়ার্ড রিসেট করব?
আমাদের দোকান প্রতিদিন সকাল নটা থেকে রাত আটটা পর্যন্ত খোলা থাকে।
ডাক্তার বলেছেন আমাকে তিন দিন বিশ্রাম নিতে হবে।
দুই দিনের ছুটি চেয়ে একটি ছোট ইমেল লিখুন।
এ বছর বর্ষা আগেই এসেছে, আর কৃষকরা খুশি।
অনুগ্রহ করে ঋণ আর ক্রেডিট কার্ডের মধ্যে পার্থক্য বুঝিয়ে বলুন।
আপডেটের পর আমার ফোনের ব্যাটারি খুব দ্রুত শেষ হয়ে যায়।
সভাটি সোমবার দুপুরে সরিয়ে দেওয়া হয়েছে।
বেশিরভাগ বাড়িতে প্রতিদিন ভাত, ডাল আর সবজি রান্না হয়।
আপনি কি আগামীকালের ইন্টারভিউয়ের প্রস্তুতিতে আমাকে সাহায্য করতে পারেন?
সরকার ছোট ব্যবসার জন্য একটি নতুন প্রকল্প ঘোষণা করেছে।
গরমের সময় শিশুদের যথেষ্ট জল পান করা উচিত।
আমি আমার অর্ডারের টাকা ফেরত পাইনি।
সমুদ্রপৃষ্ঠে জল একশো ডিগ্রি সেলসিয়াসে ফোটে।
এই নথিটি তিনটি বুলেট পয়েন্টে সংক্ষেপে বলুন।
আলোর উৎসব প্রদীপ আর মিষ্টি দিয়ে পালিত হয়।
অনুগ্রহ করে কোডটি পরীক্ষা করে বলুন কেন এটি ত্রুটি দেখাচ্ছে।
আপনার ধৈর্যের জন্য ধন্যবাদ; আমরা দুই কার্যদিবসের মধ্যে উত্তর দেব।

Telugu:

దయచేసి శుక్రవారం లోపు నాకు ఇన్వాయిస్ పంపండి.
చెన్నై వెళ్లే రైలు ఉదయం ఆరు గంటలకు బయలుదేరుతుంది.
నా పాస్‌వర్డ్‌ను ఎలా రీసెట్ చేయాలి?
మా దుకాణం ప్రతిరోజూ ఉదయం తొమ్మిది నుంచి రాత్రి ఎనిమిది వరకు తెరిచి ఉంటుంది.
మూడు రోజులు విశ్రాంతి తీసుకోవాలని డాక్టర్ చెప్పారు.
రెండు రోజుల సెలవు అడుగుతూ ఒక చిన్న ఈమెయిల్ రాయండి.
ఈ ఏడాది వర్షాకాలం త్వరగా వచ్చింది, రైతులు సంతోషంగా ఉన్నారు.
రుణానికి, క్రెడిట్ కార్డుకు మధ్య తేడాను వివరించండి.
అప్‌డేట్ తర్వాత నా ఫోన్ బ్యాటరీ చాలా త్వరగా అయిపోతోంది.
సమావేశాన్ని సోమవారం మధ్యాహ్నానికి మార్చారు.
చాలా ఇళ్లలో ప్రతిరోజూ అన్నం, పప్పు, కూరగాయలు వండుతారు.
రేపటి ఇంటర్వ్యూకు సిద్ధం కావడానికి మీరు నాకు సహాయం చేయగలరా?
చిన్న వ్యాపారాల కోసం ప్రభుత్వం కొత్త పథకాన్ని ప్రకటించింది.
వేసవిలో పిల్లలు తగినంత నీరు తాగాలి.
నా ఆర్డర్‌కు రీఫండ్ రాలేదు.
సముద్ర మట్టం వద్ద నీరు వంద డిగ్రీల సెల్సియస్ వద్ద మరుగుతుంది.
ఈ పత్రాన్ని మూడు బుల్లెట్ పాయింట్లలో సంక్షిప్తంగా చెప్పండి.
దీపాలు, మిఠాయిలతో వెలుగుల పండుగను జరుపుకుంటారు.
దయచేసి కోడ్‌ను తనిఖీ చేసి, అది ఎందుకు లోపం చూపిస్తోందో చెప్పండి.
మీ సహనానికి ధన్యవాదాలు; రెండు పని దినాల్లో సమాధానం ఇస్తాం.

Marathi:

कृपया शुक्रवारपर्यंत मला इनव्हॉइस पाठवा.
चेन्नईला जाणारी ट्रेन सकाळी सहा वाजता सुटते.
मी माझा पासवर्ड कसा रीसेट करू?
आमचे दुकान रोज सकाळी नऊ ते रात्री आठ वाजेपर्यंत उघडे असते.
डॉक्टरांनी सांगितले की मला तीन दिवस विश्रांती घ्यावी.
दोन दिवसांच्या रजेची मागणी करणारा एक छोटा ईमेल लिहा.
यंदा मान्सून लवकर आला आणि शेतकरी आनंदी आहेत.
कृपया कर्ज आणि क्रेडिट कार्ड यांतील फरक समजावून सांगा.
अपडेटनंतर माझ्या फोनची बॅटरी खूप लवकर संपते.
बैठक सोमवारी दुपारी हलवण्यात आली आहे.
बहुतेक घरांमध्ये रोज भात, वरण आणि भाज्या शिजवल्या जातात.
उद्याच्या मुलाखतीची तयारी करण्यात तुम्ही मला मदत करू शकता का?
सरकारने लहान उद्योगांसाठी एक नवीन योजना जाहीर केली आहे.
उन्हाळ्यात मुलांनी पुरेसे पाणी प्यावे.
मला माझ्या ऑर्डरचा परतावा मिळाला नाही.
समुद्रसपाटीवर पाणी शंभर अंश सेल्सिअसला उकळते.
या दस्तऐवजाचा सारांश तीन मुद्द्यांत द्या.
दिवे आणि मिठाईसह प्रकाशाचा सण साजरा केला जातो.
कृपया कोड तपासा आणि तो त्रुटी का दाखवतो ते सांगा.
तुमच्या संयमाबद्दल धन्यवाद; आम्ही दोन कामकाजाच्या दिवसांत उत्तर देऊ.

Hinglish (romanized Hindi):

Please Friday tak mujhe invoice bhej dijiye.
Chennai jaane wali train subah chhah baje nikalti hai.
Main apna password reset kaise karun?
Hamari dukaan roz subah nau baje se raat aath baje tak khuli rehti hai.
Doctor ne kaha ki mujhe teen din aaram karna chahiye.
Do din ki chhutti maangte hue ek chhota email likhiye.
Is saal monsoon jaldi aa gaya, aur kisaan khush hain.
Please loan aur credit card ke beech ka antar samjhaiye.
Update ke baad mere phone ki battery bahut tezi se khatam hoti hai.
Meeting Monday dopahar ke liye aage badha di gayi hai.
Zyadatar gharon mein roz chawal, daal aur sabziyaan pakai jaati hain.
Kya aap kal ke interview ki taiyaari mein meri madad kar sakte hain?
Sarkar ne chhote karobaron ke liye ek nayi yojana ki ghoshna ki hai.
Garmiyon mein bachchon ko kaafi paani peena chahiye.
Mujhe apne order ka refund nahi mila.
Samudra tal par paani sau degree Celsius par ubalta hai.
Is document ka summary teen bullet points mein dijiye.
Roshni ka tyohaar diyon aur mithaiyon ke saath manaya jaata hai.
Please code check kijiye aur bataiye ki usme error kyon aa raha hai.
Aapke dhairya ke liye dhanyavaad; hum do working days ke andar jawab denge.

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