Serverless models

Chat models

In the table below, models marked as "Turbo" are quantized to FP8 and those marked as "Lite" are INT4. All our other models are at full precision (FP16).

If you're not sure which chat model to use, we currently recommend Llama 3.3 70B Turbo (meta-llama/Llama-3.3-70B-Instruct-Turbo) to get started.

*Deprecated model, see Deprecations for more details

Chat Model Examples

• PDF to Chat App - Chat with your PDFs (blogs, textbooks, papers)
• Open Deep Research Notebook - Generate long form reports using a single prompt
• RAG with Reasoning Models Notebook - RAG with DeepSeek-R1
• Fine-tuning Chat Models Notebook - Tune Language models for conversation
• Building Agents - Agent workflows with language models

Image models

Use our Images endpoint for Image Models.

Note: Due to high demand, FLUX.1 [schnell] Free has a model specific rate limit of 10 img/min. Flux Pro 1 and Flux Pro 1.1 are limited to users Build Tier 2 and above. Flux models can also only be used with credits. Users are unable to call Flux with a zero or negative balance.

*Free model has reduced rate limits and performance compared to our paid Turbo endpoint for Flux Shnell named black-forest-labs/FLUX.1-schnell

Image Model Examples

• Blinkshot.io - A realtime AI image playground built with Flux Schnell
• Logo Creator - An logo generator that creates professional logos in seconds using Flux Pro 1.1
• PicMenu - A menu visualizer that takes a restaurant menu and generates nice images for each dish.
• Flux LoRA Inference Notebook - Using LoRA fine-tuned image generations models

How FLUX pricing works
For FLUX models (except for pro) pricing is based on the size of generated images (in megapixels) and the number of steps used (if the number of steps exceed the default steps).

• Default pricing: The listed per megapixel prices are for the default number of steps.
• Using more or fewer steps: Costs are adjusted based on the number of steps used only if you go above the default steps. If you use more steps, the cost increases proportionally using the formula below. If you use fewer steps, the cost does not decrease and is based on the default rate.

Here’s a formula to calculate cost:

Cost = MP × Price per MP × (Steps ÷ Default Steps)

Where:

• MP = (Width × Height ÷ 1,000,000)
• Price per MP = Cost for generating one megapixel at the default steps
• Steps = The number of steps used for the image generation. This is only factored in if going above default steps.

Vision models

If you're not sure which vision model to use, we currently recommend Llama 3.2 11B Turbo (meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo) to get started. For model specific rate limits, navigate here.

*Free model has reduced rate limits compared to paid version of Llama 3.2 Vision 11B named Llama-3.2-11B-Vision-Instruct-Turbo

Vision Model Examples

• LlamaOCR - A tool that takes documents (like receipts) and outputs markdown
• Wireframe to Code - A wireframe to app tool that takes in a UI mockup of a site and give you React code.
• Extracting Structured Data from Images - Extract information from images as JSON

Audio models

Use our Audio endpoint for audio models.

Audio Model Examples

• PDF to Podcast Notebook - Generate a NotebookLM style podcast given a PDF
• Audio Podcast Agent Workflow - Agent workflow to generate audio files given input content

Code models

Use our Completions endpoint for Code Models.

Code Model Examples

• LlamaCoder - An open source app to generate small apps with one prompt. Powered by Llama 3 405B.
• Code Generation Agent - An agent workflow to generate and iteratively improve code.

Embedding models

Embedding Model Examples

• Contextual RAG - An open source implementation of contextual RAG by Anthropic
• Code Generation Agent - An agent workflow to generate and iteratively improve code
• Multimodal Search and Image Generation - Search for images and generate more similar ones
• Visualizing Embeddings - Visualizing and clustering vector embeddings

Rerank models

Our Rerank API has built-in support for the following models, that we host via our serverless endpoints.

Rerank Model Examples

• Search and Reranking - Simple semantic search pipeline improved using a reranker
• Implementing Hybrid Search Notebook - Implementing semantic + lexical search along with reranking

Language models

Use our Completions endpoint for Language Models.

Moderation models

Use our Completions endpoint to run a moderation model as a standalone classifier, or use it alongside any of the other models above as a filter to safeguard responses from 100+ models, by specifying the parameter "safety_model": "MODEL_API_STRING"