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.

Organization	Model Name	API Model String	Context length	Quantization
Qwen	Qwen3 235B A22B Throughput	Qwen/Qwen3-235B-A22B-fp8-tput	40960	FP8
Meta	Llama 4 Maverick (17Bx128E)	meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	1048576	FP8
Meta	Llama 4 Scout (17Bx16E)	meta-llama/Llama-4-Scout-17B-16E-Instruct	1048576	FP16
DeepSeek	DeepSeek-R1	deepseek-ai/DeepSeek-R1	128000	FP8
DeepSeek	DeepSeek-V3-0324	deepseek-ai/DeepSeek-V3	128000	FP8
DeepSeek	DeepSeek R1 Distill Llama 70B	deepseek-ai/DeepSeek-R1-Distill-Llama-70B	131072	FP16
DeepSeek	DeepSeek R1 Distill Qwen 1.5B	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	131072	FP16
DeepSeek	DeepSeek R1 Distill Qwen 14B	deepseek-ai/DeepSeek-R1-Distill-Qwen-14B	131072	FP16
Perplexity AI	Perplexity AI R1-1776	perplexity-ai/r1-1776	163840	FP16
mistralai	Mistral Small 3 Instruct (24B)	mistralai/Mistral-Small-24B-Instruct-2501	32768	FP16
Meta	Llama 3.1 8B Instruct Turbo	meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo	131072	FP8
Meta	Llama 3.3 70B Instruct Turbo	meta-llama/Llama-3.3-70B-Instruct-Turbo	131072	FP8
Nvidia	Llama 3.1 Nemotron 70B	nvidia/Llama-3.1-Nemotron-70B-Instruct-HF	32768	FP16
Qwen	Qwen 2.5 7B Instruct Turbo	Qwen/Qwen2.5-7B-Instruct-Turbo	32768	FP8
Qwen	Qwen 2.5 72B Instruct Turbo	Qwen/Qwen2.5-72B-Instruct-Turbo	32768	FP8
Qwen	Qwen2.5 Vision Language 72B Instruct	Qwen/Qwen2.5-VL-72B-Instruct	32768	FP8
Qwen	Qwen 2.5 Coder 32B Instruct	Qwen/Qwen2.5-Coder-32B-Instruct	32768	FP16
Qwen	QwQ-32B	Qwen/QwQ-32B	32768	FP16
Qwen	Qwen 2 Instruct (72B)	Qwen/Qwen2-72B-Instruct	32768	FP16
Qwen	Qwen2 VL 72B Instruct	Qwen/Qwen2-VL-72B-Instruct	32768	FP16
Meta	Llama 3.1 405B Instruct Turbo	meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo	130815	FP8
Meta	Llama 3.2 3B Instruct Turbo	meta-llama/Llama-3.2-3B-Instruct-Turbo	131072	FP16
Meta	Llama 3 8B Instruct Lite	meta-llama/Meta-Llama-3-8B-Instruct-Lite	8192	INT4
Meta	Llama 3 8B Instruct Reference	meta-llama/Llama-3-8b-chat-hf	8192	FP16
Meta	Llama 3 70B Instruct Reference	meta-llama/Llama-3-70b-chat-hf	8192	FP16
Google	Gemma 2 27B	google/gemma-2-27b-it	8192	FP16
Google	Gemma 2 9B	google/gemma-2-9b-it*	8192	FP16
Google	Gemma Instruct (2B)	google/gemma-2b-it*	8192	FP16
Gryphe	MythoMax-L2 (13B)	Gryphe/MythoMax-L2-13b*	4096	FP16
mistralai	Mistral (7B) Instruct	mistralai/Mistral-7B-Instruct-v0.1	8192	FP16
mistralai	Mistral (7B) Instruct v0.2	mistralai/Mistral-7B-Instruct-v0.2	32768	FP16
mistralai	Mistral (7B) Instruct v0.3	mistralai/Mistral-7B-Instruct-v0.3	32768	FP16
mistralai	Mixtral-8x7B Instruct (46.7B)	mistralai/Mixtral-8x7B-Instruct-v0.1*	32768	FP16
mistralai	Mixtral-8x22B Instruct (141B)	mistralai/Mixtral-8x22B-Instruct-v0.1*	65536	FP16
NousResearch	Nous Hermes 2 - Mixtral 8x7B-DPO (46.7B)	NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO	32768	FP16

*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.

Organization	Model Name	Model String for API	Default steps
Black Forest Labs	Flux.1 [schnell] (free)*	black-forest-labs/FLUX.1-schnell-Free	N/A
Black Forest Labs	Flux.1 [schnell] (Turbo)	black-forest-labs/FLUX.1-schnell	4
Black Forest Labs	Flux.1 Dev	black-forest-labs/FLUX.1-dev	28
Black Forest Labs	Flux.1 Canny	black-forest-labs/FLUX.1-canny	28
Black Forest Labs	Flux.1 Depth	black-forest-labs/FLUX.1-depth	28
Black Forest Labs	Flux.1 Redux	black-forest-labs/FLUX.1-redux	28
Black Forest Labs	Flux1.1 [pro]	black-forest-labs/FLUX.1.1-pro	-
Black Forest Labs	Flux.1 [pro]	black-forest-labs/FLUX.1-pro	-

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.

Organization	Model Name	API Model String	Context length
Meta	Llama 4 Maverick (17Bx128E)	meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	524288
Meta	Llama 4 Scout (17Bx16E)	meta-llama/Llama-4-Scout-17B-16E-Instruct	327680
Meta	(Free) Llama 3.2 11B Vision Instruct Turbo*	meta-llama/Llama-Vision-Free	131072
Meta	Llama 3.2 11B Vision Instruct Turbo	meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo	131072
Meta	Llama 3.2 90B Vision Instruct Turbo	meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo	131072
Qwen	Qwen2 Vision Language 72B Instruct	Qwen/Qwen2-VL-72B-Instruct	32768
Qwen	Qwen2.5 Vision Language 72B Instruct	Qwen/Qwen2.5-VL-72B-Instruct	32768

*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.

Organization	Model Name	Model String for API
Cartesia	Cartesia Sonic 2	Cartesia/Sonic-2

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.

Organization	Model Name	Model String for API	Context length
Qwen	Qwen 2.5 Coder 32B Instruct	Qwen/Qwen2.5-Coder-32B-Instruct	32768

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

Model Name	Model String for API	Model Size	Embedding Dimension	Context Window
M2-BERT-80M-2K-Retrieval	togethercomputer/m2-bert-80M-2k-retrieval*	80M	768	2048
M2-BERT-80M-8K-Retrieval	togethercomputer/m2-bert-80M-8k-retrieval*	80M	768	8192
M2-BERT-80M-32K-Retrieval	togethercomputer/m2-bert-80M-32k-retrieval	80M	768	32768
UAE-Large-v1	WhereIsAI/UAE-Large-V1*	326M	1024	512
BGE-Large-EN-v1.5	BAAI/bge-large-en-v1.5	326M	1024	512
BGE-Base-EN-v1.5	BAAI/bge-base-en-v1.5	102M	768	512

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.

Organization	Model Name	Model Size	Model String for API	Max Doc Size (tokens)	Max Docs
Salesforce	LlamaRank	8B	Salesforce/Llama-Rank-v1	8192	1024

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.

Organization	Model Name	Model String for API	Context length
Meta	LLaMA-2 (70B)	meta-llama/Llama-2-70b-hf	4096
mistralai	Mixtral-8x7B (46.7B)	mistralai/Mixtral-8x7B-v0.1	32768

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"

Organization	Model Name	Model String for API	Context length
Meta	Llama Guard (8B)	meta-llama/Meta-Llama-Guard-3-8B	8192
Meta	Llama Guard 4 (12B)	meta-llama/Llama-Guard-4-12B	1048576