mnfst/awesome-free-llm-apis is a curated list of permanent free LLM APIs, providing developers with access to various language models for text inference.
Source: per README View on GitHub →This project is gaining attention due to its comprehensive collection of free LLM APIs, addressing the need for cost-effective access to advanced language models. Its unique technical choice is the inclusion of OpenAI SDK-compatible endpoints, making it easier for developers to integrate these APIs into their projects.
Source: Synthesis of README and project traitsDetailed information on various LLM APIs, including base URLs, supported models, context limits, max output sizes, modalities, and rate limits.
Source: Provider APIs section in READMEListing of third-party platforms that host open-weight models, with details on available models, context, max output, modalities, and rate limits.
Source: Inference providers section in READMEA glossary providing definitions for technical terms related to LLMs and their APIs.
Source: Glossary section in READMEThe architecture is inferred to be a simple static list-based structure, with a README.md at the root, and a data.json file likely containing the structured information about the APIs and providers. The project does not use any external dependencies as indicated by the absence of dependency files.
Source: Code tree + dependency filesinfra: Not specified, but the project is likely to be hosted on GitHub and accessed via web browsers | key_deps: Not enough information | language: JavaScript | framework: Not specified, but likely uses Node.js for server-side operations
Source: Dependency files + code treeThis project is for developers looking for cost-effective access to LLM APIs for text inference. It is useful in scenarios such as prototyping AI applications, educational purposes, and small-scale projects where budget constraints are a concern.
Source: READMENot enough information
Source: GitHub Releasesmnfst/awesome-free-llm-apis is a valuable resource for developers seeking free access to LLM APIs. It is particularly suited for those working on budget-conscious projects or exploring AI capabilities without significant financial investment.