LLM Reference
Hugging Face Inference Endpoints

Hugging Face Inference Endpoints

Researched 25d agoMarketplaceTier 3

Hugging Face

CodingRAGAgentsLong contextVisionClassificationJSON / Tool useAI

Hugging Face Inference Endpoints offers 9 tracked models (1 with output token pricing). This catalog covers coding, rag, and agents; open any model detail page for benchmarks, batch tiers, and migration prompts.

Covers 7 workload areas across 9 tracked models; last verified 2026-06-11.

Use it for

  • Teams comparing token and batch pricing across this provider's models
  • Operators routing coding, rag, and agents workloads through this API

Do not use it for

  • Final benchmark picks without opening the relevant model detail page

Tracked models

9

Models available through this provider

Priced output routes

1

Models with output token pricing tracked

Cheapest output

$0.150

Mistral 7B v0.1 on this route

Batch-ready models

0

No batch pricing tracked

Latest model release

2026-06-03

33d since newest release

Freshness

2026-06-11

Researched 25d ago

fresh

Information

TypeMarketplace
TierTier 3
Models9
CompanyHugging Face
Founded2016
New York City, New York, United States

Hugging Face is a leading AI community and platform dedicated to democratizing artificial intelligence. They provide a comprehensive ecosystem for machine learning, focusing on natural language processing and deep learning. Their platform offers: 1. A vast repository of pre-trained models and datasets 2. Tools for model training, fine-tuning, and deployment 3. Collaborative spaces for AI researchers and developers 4. Open-source libraries like Transformers for state-of-the-art NLP Founded in 2016, Hugging Face has grown rapidly, now serving over 5 million users. They emphasize open-source development and community-driven innovation, fostering a collaborative environment for AI advancement. The platform supports various AI tasks, including text generation, image processing, and speech recognition, making it a versatile hub for both beginners and experts in the field of artificial intelligence.

Catalog freshness

The newest model tracked on this provider was released 2026-06-03 (33d ago).

Where this host wins

  • Coding: 1 tracked model with SWE-bench / HumanEval-style scores.
  • RAG: 2 tracked models with ruler / needle retrieval benchmarks.
  • Agentic: 2 tracked models with BFCL, tau-bench, and SWE-bench tool-use coverage.
  • Long-context: 2 tracked models with context-token or InfiniteBench-class signal.

Getting started

Official product, docs, and pricing links — confirm quotas and regions in the vendor docs.

Compliance notes

No verified compliance claims (SOC 2, ISO, HIPAA) tracked for this provider yet — check the vendor's trust center for current certifications.

Platform Overview

Hugging Face's AI platform serves as a comprehensive ecosystem for machine learning, centered around the Hugging Face Hub. This hub hosts an extensive collection of over 450,000 pre-trained models and 90,000 datasets, covering a wide range of AI tasks including natural language processing, computer vision, and audio processing. Users can easily access and utilize these resources for various applications such as text classification, translation, image generation, and speech recognition. The platform's Transformers library simplifies the implementation of these models, providing user-friendly interfaces for tasks like fine-tuning and model evaluation. The platform extends its capabilities through Spaces, which are customizable environments for deploying and showcasing machine learning applications. These Spaces enable users to create interactive demos and engage with AI technology without requiring extensive technical expertise. The platform also supports integration with popular machine learning frameworks like TensorFlow and PyTorch, enhancing its versatility for developers. By combining a vast repository of models and datasets with tools for collaboration and deployment, the platform empowers users to efficiently build, train, and deploy AI models while fostering a community-driven approach to AI development and innovation.

Compare per-model pricing, input and output token costs, batch availability, and benchmark coverage.

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