THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Explore AI models 10

Provider Input Output License

Discover models worth your attention

banglabert

csebuetnlp

This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an ELECTRA discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali. For finetuning on different downstream tasks such as `Sentiment classification`, `Named Entity Recognition`, `Natural Language Inference` etc., refer to the scripts in the official GitHub repository. **Note**: This model was pretrained using a specific normalization pipeline available here. All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglabert_large

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglabert_small

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglat5

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglat5_nmt_bn_en

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Translation

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglat5_nmt_en_bn

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Translation

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglat5_small

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

banglishbert

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

mT5_m2o_arabic_crossSum

csebuetnlp

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Summarization

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

mT5_multilingual_XLSum

csebuetnlp

This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts, see the paper and the official repository. Language | ROUGE-1 / ROUGE-2 / ROUGE-L ---------|---------------------------- Amharic | 20.0485 / 7.4111 / 18.0753 Arabic | 34.9107 / 14.7937 / 29.1623 Azerbaijani | 21.4227 / 9.5214 / 19.3331 Bengali | 29.5653 / 12.1095 / 25.1315 Burmese | 15.9626 / 5.1477 / 14.1819 Chinese (Simplified) | 39.4071 / 17.7913 / 33.406 Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184 English | 37.601 / 15.1536 / 29.8817 French | 35.3398 / 16.1739 / 28.2041 Gujarati | 21.9619 / 7.7417 / 19.86 Hausa | 39.4375 / 17.6786 / 31.6667 Hindi | 38.5882 / 16.8802 / 32.0132 Igbo | 31.6148 / 10.1605 / 24.5309 Indonesian | 37.0049 / 17.0181 / 30.7561 Japanese | 48.1544 / 23.8482 / 37.3636 Kirundi | 31.9907 / 14.3685 / 25.8305 Korean | 23.6745 / 11.4478 / 22.3619 Kyrgyz | 18.3751 / 7.9608 / 16.5033 Marathi | 22.0141 / 9.5439 / 19.9208 Nepali | 26.6547 / 10.2479 / 24.2847 Oromo | 18.7025 / 6.1694 / 16.1862 Pashto | 38.4743 / 15.5475 / 31.9065 Persian | 36.9425 / 16.1934 / 30.0701 Pidgin | 37.9574 / 15.1234 / 29.872 Portuguese | 37.1676 / 15.9022 / 28.5586 Punjabi | 30.6973 / 12.2058 / 25.515 Russian | 32.2164 / 13.6386 / 26.1689 Scottish Gaelic | 29.0231 / 10.9893 / 22.8814 Serbian (Cyrillic) | 23.7841 / 7.9816 / 20.1379 Serbian (Latin) | 21.6443 / 6.6573 / 18.2336 Sinhala | 27.2901 / 13.3815 / 23.4699 Somali | 31.5563 / 11.5818 / 24.2232 Spanish | 31.5071 / 11.8767 / 24.0746 Swahili | 37.6673 / 17.8534 / 30.9146 Tamil | 24.3326 / 11.0553 / 22.0741 Telugu | 19.8571 / 7.0337 / 17.6101 Thai | 37.3951 / 17.275 / 28.8796 Tigrinya | 25.321 / 8.0157 / 21.1729 Turkish | 32.9304 / 15.5709 / 29.2622 Ukrainian | 23.9908 / 10.1431 / 20.9199 Urdu | 39.5579 / 18.3733 / 32.8442 Uzbek | 16.8281 / 6.3406 / 15.4055 Vietnamese | 32.8826 / 16.2247 / 26.0844 Welsh | 32.6599 / 11.596 / 26.1164 Yoruba | 31.6595 / 11.6599 / 25.0898

Summarization

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

Find your ideal AI model.

Explore AI models 10

banglabert

banglabert_large

banglabert_small

banglat5

banglat5_nmt_bn_en

banglat5_nmt_en_bn

banglat5_small

banglishbert

mT5_m2o_arabic_crossSum

mT5_multilingual_XLSum

No matching models

Great models. Clear costs.

Understand before you choose

Follow trends. Find tools. Explore models.