GPT_API_free: Complete Tutorial for Getting Free ChatGPT/DeepSeek API Keys

Project Overview: Zero-Cost Access to Major LLM APIs

In LLM application development, API call costs have always been a core pain point for developers. While APIs from major models like OpenAI, DeepSeek, and Claude are powerful, the ongoing usage fees represent a significant expense for individual developers and small teams. Taking OpenAI as an example, GPT-4's API is priced at approximately $30 per million input tokens, and even the more economical GPT-3.5-Turbo costs $0.5 per million tokens. During development phases that require frequent iterative testing, these costs accumulate quickly.

The open-source project chatanywhere/GPT_API_free on GitHub was created to solve exactly this problem — it provides free ChatGPT and DeepSeek API Keys, enabling developers to access multiple major LLMs at zero cost.

As of now, the project has earned 37,700+ Stars and 2,600+ Forks, making it one of the most popular free API proxy projects on GitHub.

GPT_API_free Technical Architecture: How the API Reverse Proxy Works

GPT_API_free is essentially an API Reverse Proxy service. A reverse proxy is a server architecture pattern where client requests are not sent directly to the target server but first pass through a proxy server, which then forwards them to the actual backend service.

It's worth noting that reverse proxies are fundamentally different from forward proxies in their design philosophy — a forward proxy makes requests on behalf of the client (like VPN tools), while a reverse proxy stands on the server side and exposes a unified entry point. Nginx, Caddy, and Envoy are common reverse proxy implementations in the industry. In GPT_API_free's specific scenario, the proxy layer also handles request header rewriting, API Key pool rotation, rate limiting, and circuit breaking. It has essentially evolved into a lightweight API Gateway — which is the technical foundation that enables it to support multiple model providers simultaneously.

In this scenario, developers' API requests are first sent to GPT_API_free's servers, which then forward them to the actual API endpoints of model providers like OpenAI and DeepSeek. The advantages of this architecture include:

• Request aggregation and load balancing
• Unified authentication mechanism
• Helping developers in China bypass network access restrictions

However, it should be noted that this approach introduces additional network hop latency and single-point-of-failure risks.

What LLMs Does GPT_API_free Support?

Multi-Model Aggregation Coverage

GPT_API_free doesn't support just a single model — it has built a multi-model aggregated API gateway. Currently supported models include:

• GPT Series: GPT-3.5-Turbo, GPT-4, and other OpenAI models
• DeepSeek: A high cost-performance Chinese inference model using Mixture of Experts (MoE) architecture. MoE is key to understanding DeepSeek's cost efficiency — unlike traditional Dense models that activate all parameters during inference, MoE distributes parameters across multiple "expert" sub-networks and only activates a small subset (typically 2-8 experts) per inference. DeepSeek-V3 has 671B total parameters but only activates approximately 37B parameters per inference, which directly explains its extremely low inference cost. This architecture enables it to achieve near GPT-4 performance on mathematical reasoning and code generation tasks at a fraction of the cost — with input pricing around ¥1/million tokens, it has directly disrupted the industry's pricing structure
• Claude: A safety-oriented LLM from Anthropic, trained using their original Constitutional AI (CAI) method. Unlike traditional RLHF which relies on large amounts of manually labeled preference data, CAI works by presetting a set of "constitutional