Compare Models
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Stanford University
Alpaca
FREEStanford University released an instruction-following language model called Alpaca, which was fine-tuned from Meta’s LLaMA 7B model. The Alpaca model was trained on 52K instruction-following demonstrations generated in the style of self-instruct using text-davinci-003. Alpaca aims to help the academic community engage with the models by providing an open source model that rivals OpenAI’s GPT-3.5 (text-davinci-003) models. To this end, Alpaca has been kept small and cheap (fine-tuning Alpaca took 3 hours on 8x A100s which is less than $100 of cost) to reproduce. All training data and techniques have been released. The Alpaca license explicitly prohibits commercial use, and the model can only be used for research/personal projects, and users need to follow LLaMA’s license agreement. -
Anthropic
Claude 2 – API version
$0.03268Anthropic’s Claude 2 much larger context window (launching with 100k for now but will go up to 200K).will make it possible to feed it entire books or have it generate entire books at once.Claude 2 scored 76.5 percent on the multiple choice section of the Bar exam and in the 90th percentile on the reading and writing portion of the GRE. Its coding skills have improved from its predecessor scoring 71.2 percent on a Python coding test compared to Claude’s 56 percent.Claude 2 is also 63% cheaper on inputs and 46% cheaper on outputs than the GPT-4 8K context version (the default version of the OpenAI model). -
OpenAI
Claude 2 (Web Browser Version)
FREEAnthropic’s Claude 2 is now available to the public if you’re in the US or UK. For the web browser version. just click “Talk to Claude,” and you’ll be prompted to provide an email address. After you confirm the address you enter, you’ll be ready to go.Claude 2 scored 76.5 percent on the multiple choice section of the Bar exam and in the 90th percentile on the reading and writing portion of the GRE. Its coding skills have improved from its predecessor scoring 71.2 percent on a Python coding test compared to Claude’s 56 percent. While the Google-backed Anthropic initially launched Claude in March, the chatbot was only available to businesses by request or as an app in Slack. With Claude 2, Anthropic is building upon the chatbot’s existing capabilities with a number of improvements. -
Anthropic
Claude Instant
$0.00551Claude Instant is a faster and less expensive model than Claude-v1 that can handle casual dialog, text analysis and summarization, and document Q&A. Optimized for low latency, it handles high throughput use cases at lower costs that other Claude family of models. Anthropic is an AI startup founded by former OpenAI employees. Anthropic specializes in developing general AI systems and language models, with a company ethos of responsible AI usage.API access can be gained after application. -
Anthropic
Claude Instant v1
$0.03268A powerful model, Claude-v1 can handle sophisticated dialog, creative content generation, and detailed instructions. Optimized for superior performance on tasks that require complex reasoning, Claude is Anthropic’s best-in-class offering.API access can be gained after application. -
Databricks
Dolly 2.0
FREEDolly 2.0 by Databricks, is the first open source, instruction-following Large Language Model, fine-tuned on a human-generated instruction dataset and is licensed for research and commercial use, which means any organization can create, own, and customize powerful LLMs that can talk to people without paying for API access or sharing data with third parties.Dolly 2.0 is a 12B parameter language model based on the EleutherAI pythia model family and fine-tuned exclusively on a new, high-quality human generated instruction following dataset (crowdsourced among Databricks employees – so cool). Dolly-v2-12b is not a state-of-the-art model, but it does exhibit surprisingly high-quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in smaller model sizes: dolly-v2-7b, a 6.9 billion parameter based on pythia-6.9b and dolly-v2-3b, a 2.8 billion parameter based on pythia-2.8b.Dolly 2.0 can be used for brainstorming, classification, open Q&A, closed Q&A, content generation, information extraction, and summarization. You can access the Dolly 2.0 can training code, the dataset, and the model weights on Hugging Face. -
Cohere
Generate
$0.015Cohere is a Canadian startup that provides high-performance and secure LLMs for the enterprise. Their models work on public, private, or hybrid clouds.Cohere Generate can be used for tasks such as copywriting, named entity recognition, paraphrasing, and summarization. It can be particularly useful for automating time-consuming and repetitive copywriting tasks and re-wording text to suit a specific reader or context.Cohere Generate is available as an API that can be integrated into various libraries using Python, Node, or Go software development kits (SDKs).We have shown the price of the Cohere Generate Default version, but a Cohere Generate Custom model is available but is double the price (0.030 per 1/k tokens). However, custom models can lead to some of the best-performing NLP models for many tasks. -
EleutherAI
GPT-J
FREEEleutherAI is a leading non-profit research institute focused on large-scale artificial intelligence research. EleutherAI has trained and released several LLMs and the codebases used to train them. GPT-J can be used for code generation, making a chat bot, story writing, language translation and searching. GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for, which is generating text from a prompt. EleutherAI has a web page where you can test to see how the GPT-J works, or you can run GPT-J on google colab, or use the Hugging Face Transformers library. -
EleutherAI
GPT-NeoX-20B
FREEEleutherAI has trained and released several LLMs and the codebases used to train them. EleutherAI is a leading non-profit research institute focused on large-scale artificial intelligence research. GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. Its architecture intentionally resembles that of GPT-3, and is almost identical to that of GPT-J- 6B. Its training dataset contains a multitude of English-language texts, reflecting the general-purpose nature of this model. It is a transformer-based language model and is English-language only, and thus cannot be used for translation or generating text in other languages. It is freely and openly available to the public through a permissive license. -
Cohere
Summarize
$0.015Cohere is a Canadian startup that provides high-performance and secure LLMs for the enterprise. Their models work on public, private, or hybrid clouds and is available as an API that can be integrated into various libraries using Python, Node, or Go software development kits (SDKs).Cohere Summarize generates a succinct version of a provided text. This summary relays the most important messages of the text, and a user can configure the results with a variety of parameters to support unique use cases. It can instantly encapsulate the key points of a document and provides text summarization capabilities at scale. -
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. -
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.
