Compare Models
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Microsoft, NVIDIA
MT-NLG
OTHERMT-NLG (Megatron-Turing Natural Language Generation) uses the architecture of the transformer-based Megatron to generate coherent and contextually relevant text for a range of tasks, including completion prediction, reading comprehension, commonsense reasoning, natural language inferences, and word sense disambiguation. MT-NLG is the successor to Microsoft Turing NLG 17B and NVIDIA Megatron-LM 8.3B. The MT-NLG model is three times larger than GPT-3 (530B vs 175B). Following the original Megatron work, NVIDIA and Microsoft trained the model on over 4,000 GPUs. NVIDIA has announced an Early Access program for its managed API service to the MT-NLG model for organizations and researchers. -
Amazon
SageMaker
FREEAmazon SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices. Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. The solutions are fully customizable and supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing, object detection, and image classification models that can help with extracting and analyzing data, fraud detection, churn prediction and personalized recommendations.The Hugging Face LLM Inference DLCs on Amazon SageMaker, allows support the following models: BLOOM / BLOOMZ, MT0-XXL, Galactica, SantaCoder, GPT-Neox 20B (joi, pythia, lotus, rosey, chip, RedPajama, open assistant, FLAN-T5-XXL (T5-11B), Llama (vicuna, alpaca, koala), Starcoder / SantaCoder, and Falcon 7B / Falcon 40B. Hugging Face’s LLM DLC is a new purpose-built Inference Container to easily deploy LLMs in a secure and managed environment. -
OpenAI
text-davinci-003
$0.02Text-davinci-003 is recognized as GPT 3.5 and is a variant of the GPT-3 model. While both Davinci and text-davinci-003 are powerful models, they differ in a few key ways. Text-davinci-003 is a newer and more capable model explicitly designed for instruction-following tasks. Text-davinci-003 was trained on a more recent dataset containing data up to June 2021. It can do any language task with better quality, longer output, and consistent instruction-following than the Curie, Babbage, or Ada models. Text-davinci-003 supports a longer context window (max prompt plus completion length) than Davinci.For those requesting the OpenAI’s API, GPT-3.5-turbo may be a better choice for tasks that require high accuracy in math or zero-shot classification and sentiment analysis than text-davinci-003. To note, GPT-3.5-turbo performs at a similar capability to text-davinci-003 but at 10 percent the price per token. OpenAI recommends GPT-3.5-turbo for most use cases. -
OpenAI
text-embedding-ada-002
$0.0001An embedding API model, such as Ada, is a powerful tool that converts words into numerical representations, enabling computers to understand and process natural language more effectively. This process is crucial for developing machine learning algorithms and artificial intelligence systems that can interact with humans, analyze text, or make predictions based on text. OpenAI’s text embeddings is built for advanced search, clustering, topic modeling, and classification functionality.Access is available through a request to OpenAI’s API. -
TruthGPT
TruthGPT
OtherTruthGPT is a large language model (LLM), and according to Elon Musk, TruthGPT will be a “maximum truth-seeking” AI. In terms of how it works, it filters through thousands of datasets and draws educated conclusions to provide answers that are as unbiased as possible. TruthGPT is powered by $TRUTH, a tradable cryptocurrency on the Binance Smart Chain. $TRUTH holders will soon access additional benefits when using TruthGPT AI. When we learn more, we will update this section. -
Microsoft
VALL-E
OTHERVALL-E is a LLM for text to speech synthesis (TTS) developed by Microsoft (technically it is a neural codec language model). Its creators state that VALL-E could be used for high-quality text-to-speech applications, speech editing where a recording of a person could be edited and changed from a text transcript (making them say something they originally didn’t), and audio content creation when combined with other generative AI models. Studies indicate that VALL-E notably surpasses the leading zero-shot TTS system regarding speech authenticity and resemblance to the speaker. Furthermore, it has been observed that VALL-E is capable of retaining the emotional expression and ambient acoustics of the speaker within the synthesized output. Unfortunately, VALL-E is not available for any form of public consumption at this time. At the time of writing, VALL-E is a research project, and there is no customer onboarding queue or waitlist (but you can apply to be part of the first testers group). -
OpenAI
Whisper
0.006Whisper is an automatic speech recognition (ASR) system capable of transcribing in multiple languages as well as translating them into English. With Whisper, you can easily transcribe speech into text, allowing you to capture conversations and meetings for future reference. And if you need to communicate with someone who speaks a different language, Whisper can help with that too — it can translate many different languages into English, making it easier than ever to bridge the gap and ensure that everyone is on the same page.
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. The speech to text API has two endpoints (transcriptions and translations) and file uploads are currently limited to 25 MB, and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm. -
Yandex
YaLM
FREEYaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, books, and countless other sources in both English and Russian. Researchers and developers can use the corporate-size solution to solve the most complex problems associated with natural language processing.Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles. The model is published under the Apache 2.0 license that permits both research and commercial use.
