AI LLM Model Pricing Overview (Free to Use!) Key Players: OpenAI, Anthropic, Google, Cohere, Mistral, and Meta These providers offer diverse models tailored for specific tasks and capabilities. Understanding their pricing structure is crucial for businesses and developers. Tokens: The Pricing Unit LLM pricing is based on "tokens." Typically, 1,000 tokens equal about 750 words. In English, one token is approximately four characters. For example, "This paragraph is 5 tokens" is 5 tokens long. Different languages may affect token counts. Context Length Context length refers to the number of tokens a model can process at once, impacting performance and cost. For instance, a model with an 8K token context can handle 8,000 tokens in a single pass. Longer contexts allow for more complex tasks and continuous conversations but are costlier. Language Models and Pricing 1. OpenAI: - GPT-4o: Advanced multimodal model, 2x faster and 50% cheaper than GPT-4 Turbo, with 128K context and upcoming audio support. - GPT-4: High general knowledge and precision, slower and more expensive. GPT-4 Turbo is 3x cheaper with 128K context. - GPT-3.5 Turbo: Optimized for dialogue, fast, and cost-effective. 2. Anthropic: - Claude 3: Includes Haiku, Sonnet, and Opus models. Opus matches GPT-4, while Haiku is cost-effective and outperforms GPT-3.5 Turbo. Sonnet balances cost and performance. Claude 3 has a 200K context window. 3. Meta: - Llama 3: Open-source, similar to GPT-3.5 Turbo, free for research and commercial use, but primarily English. 4. Google: - Gemini: Successor to PaLM 2, with Ultra, Pro, and Flash models. Gemini Ultra rivals GPT-4, Pro matches GPT-3.5, and Flash offers 1M context with multimodal support. 5. Cohere, Mistral, and Other Models: - Mistral: Small, fast, and cheap open-source models like Mistral 7B and Mixtral 8x7B, comparable to GPT-3.5 Turbo. Mistral Large is near GPT-4 level for reasoning tasks. Customization and Embedding Models OpenAI offers fine-tuning for custom models, billed per token used. Embedding models support advanced search, clustering, and classification tasks, essential for applications like AI support chatbots using Retrieval Augmented Generation (RAG).

We just added the new GPT-4o mini model to it! Keeping it cutting edge with all the newest models.

This is really useful, thank you~

Can it handle voice interactions or is it strictly text-based? Curious about its versatility.

How does it handle complex queries compared to traditional Help Scout setups?

This is such a useful tool for developers working with various LLM APIs. Coincidentally, I was wondering about this same question earlier today. How often do you update the pricing information? Also, are there plans to add more models in the future? Thanks for providing such a helpful resource, Clara!

This is helpful. I know this product will have an extremely niche audience but will save a lot of decision making time. Congratulations on the launch.

Congrats on the launch! Is there a place for us to recommend more providers? For example groq could be too in the list!

Well done on the developer documentation.

Super helpful for optimizing budgets. Well done, Clara!

Now this is useful! Will give it a shot when we are consider our next move in adopting a new LLM model. Thanks for making this!