Models / Meta

Meta

Deploy Llama 4 Maverick and Scout on Together AI. Frontier multimodal performance, 10M token context, and 80%+ cost savings versus GPT-4o.

Why Meta on Together AI?

Designed for production workloads that need 
consistent performance and operational control.

Open source freedom, enterprise grade

Full model ownership — download the weights, deploy on Together AI’s cloud, or run on-premises. Your data never trains our models and never leaves your control.

Frontier multimodal performance

Llama 4 Maverick beats GPT-4o and Gemini 2.0 Flash on key benchmarks at just $0.27/1M tokens — an 80%+ cost reduction versus closed-source alternatives.

Built for scale, ready for enterprise

SOC 2 Type II certified, HIPAA compliant, with dedicated endpoints, monthly reserved capacity, and up to 40% savings at volume.

Meet the Meta family

Explore top-performing models across text, image, video, code, and voice.

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Llama 4 Maverick

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NIM Llama 3.1 Nemotron 70B Instruct

Free

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Llama 3.3 70B Instruct Turbo Free

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Llama 3.1 405B

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Llama 3.3 70B

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Llama 4 Scout

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NIM Mixtral 8x7B Instruct v0.1

New

Moderation

Llama Guard 4 12B

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NIM Llama 3.1 70B Instruct

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NIM Llama 3.1 8B Instruct

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LLaMA-2

Vision

NIM Llama 3.2 11B Vision Instruct

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Llama 3 70B Instruct Reference

Vision

NIM Llama 3.2 90B Vision Instruct

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NIM Llama 3.3 70B Instruct

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NIM Llama 3.3 Nemotron Super 49B v1

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NIM Mistral-NeMo 12B Instruct

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NIM Mixtral 8x22B Instruct v0.1

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Llama 3.1 8B

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Llama 3.2 3B Instruct Turbo

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Llama 3.1 70B

Moderation

Llama Guard (7B)

Moderation

Llama Guard 2 8B

Moderation

Llama Guard 3 11B Vision Turbo

Moderation

Llama Guard 3 8B

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Llama 3 8B Instruct Lite

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LLaMA-2 Chat (13B)

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LLaMA-2 Chat (7B)

Deployment options

Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.

  • Serverless

  • Inference

Serverless Inference

Real-time

A fully managed inference API that automatically scales with request volume.

Best for

Variable or unpredictable traffic

Rapid prototyping and iteration

Cost-sensitive or early-stage production workloads

Batch

Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.

Best for

Classifying large datasets

Offline summarization

Synthetic data generation

Dedicated Inference

Dedicated Model Inference

An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.

Best for

Predictable or steady traffic

Latency-sensitive applications

High-throughput production workloads

Dedicated Container Inference

Run inference with your own engine and model on fully-managed, scalable infrastructure.

Best for

Generative media models

Non-standard runtimes

Custom inference pipelines