GPT_API_free: Complete Guide to Free ChatGPT/DeepSeek and Other LLM API Access

GPT_API_free Project Overview: A Free LLM API Solution with 37,700+ Stars

In AI application development, API call costs remain an unavoidable challenge for developers. Current mainstream LLM APIs universally adopt a token-based pricing model — charging based on the number of input and output text tokens.

Understanding the Underlying Logic of Token Pricing: A token is the basic unit that large language models use to process text. Unlike the intuitive human concept of "characters" or "words," tokens are determined by a tokenizer — in English, one token corresponds to roughly 0.75 words, while in Chinese, one character typically maps to 1-2 tokens. OpenAI uses the tiktoken tokenizer based on the BPE (Byte Pair Encoding) algorithm, which compresses high-frequency character combinations into single tokens to balance vocabulary size and encoding efficiency. More critically: the model processes not only the question you send, but the entire conversation history (all messages within the Context Window), meaning token consumption in multi-turn conversations grows linearly with each turn, and API costs for long conversations far exceed those of single Q&A exchanges.

Take OpenAI's GPT-4o as an example — input pricing is approximately $2.5 per million tokens, and output pricing is approximately $10 per million tokens. The higher-end GPT-4 Turbo once cost as much as $10/million tokens for input and $30/million tokens for output. For developers who need to iterate and test frequently, a few dozen calls per day could easily amount to tens of dollars. This doesn't even account for the separate billing systems and top-up thresholds of Claude 3.5 Sonnet, Gemini Pro, and other models.

The open-source project chatanywhere/GPT_API_free on GitHub offers a practical solution — free access to multiple mainstream LLM APIs. It has already garnered over 37,700 Stars and 2,625 Forks, making it one of the most popular open-source projects in this space.

The core idea behind this project is straightforward: provide developers with free API Keys supporting ChatGPT, DeepSeek^Ad, Claude, Gemini, Grok, and other major large language models, allowing you to run through AI application development and testing workflows without spending a dime.

Which LLMs Are Supported? Covering Five Major Model Families

Multi-Model Aggregation: One Key to Call All Major LLMs

GPT_API_free supports models covering the most representative camps in the current AI landscape:

• OpenAI Series: GPT-4, ChatGPT, and more — the benchmarks of the LLM field. OpenAI's GPT series is based on autoregressive language models built on the Transformer architecture. From GPT-3's 175 billion parameters to GPT-4's multimodal capabilities (supporting image input), it has consistently led the industry. GPT-4 delivers the most balanced performance in reasoning, code generation, and complex instruction following, making it the default baseline model for most developers.

• DeepSeek: A Chinese-developed LLM that has shown impressive recent performance with exceptional cost-effectiveness. Developed by DeepSeek AI, its DeepSeek-V3 employs a Mixture of Experts (MoE) architecture — a key technical breakthrough in recent large model scaling. Unlike traditional Dense models that activate all parameters during inference, MoE distributes parameters across multiple "expert" sub-networks, with a lightweight Router/Gating Network dynamically selecting the most relevant few experts for each inference pass. DeepSeek-V3 has a total parameter count of 671 billion, but only activates approximately 37 billion parameters per inference — inference costs are comparable to a Dense model of equivalent active parameters, yet model capacity far exceeds the latter. DeepSeek-R1 focuses specifically on reasoning capabilities, achieving mathematical and programming reasoning levels approaching OpenAI's o1 through reinforcement learning training, while API pricing is merely a fraction of GPT-4's — earning it the industry nickname "price killer."

• Anthropic Claude: Outstanding in long-text processing and safety. Claude is developed by Anthropic, founded by former OpenAI researchers. Its core technical feature is Constitutional AI — an AI alignment training method that internalizes predefined "constitutional principles" (harmlessness, honesty, helpfulness) through a two-stage process: first, supervised learning teaches the model to critique and revise its own outputs based on principles, then reinforcement learning is conducted using AI-generated preference data. Compared to traditional RLHF, this approach reduces dependence on human annotation of harmful content while making model values more transparent and interpretable — this is the technical foundation behind Claude's strong reputation for safety and controllability. Claude 3.5 Sonnet supports a 200K token ultra-long context window (approximately 150,000 words), making it particularly suitable for long document analysis, codebase comprehension, and similar scenarios.

• Google Gemini: Google's latest-generation multimodal LLM. Gemini was designed from the ground up as a natively multimodal architecture, capable of simultaneously understanding and generating text, images, audio, video, and code. Gemini 1.5 Pro supports a context window of up to 1 million tokens, the longest among currently publicly available models, making it ideal for processing ultra-long documents and complex multi-turn conversations.

• xAI Grok: The AI model under Elon Musk's umbrella. Grok is developed by xAI, with its unique advantage being real-time access to the X (formerly Twitter) platform's data stream for the latest internet information. Grok 2 excels in mathematical reasoning and code generation, and xAI has also open-sourced the early version Grok-1's 314 billion parameter weights.

Developers don't need to register separate accounts on each platform or deal with different authentication processes — they can call all models through a unified interface.

Technical Advantages of a Unified API Interface

The technical foundation of a unified API interface is that the OpenAI API format has become the de facto industry standard. Most LLM providers (including domestic ones like Tongyi Qianwen and Zhipu GLM) offer OpenAI-compatible API endpoints, with a unified request format of /v1/chat/completions, using the same JSON structure to pass messages, temperature, max_tokens, and other parameters.

GPT_API_free is essentially an API Gateway — a core component in microservices architecture that serves as a unified entry point between clients and backend services. In this context, it handles protocol translation, request routing, load balancing, authentication, and rate limiting/circuit breaking. When a developer sends a standard OpenAI-format request, the gateway identifies the target model based on the model parameter, forwards the request to the corresponding official API endpoint (such as Anthropic's /v1/messages or Google's generateContent interface), and then converts the response back into a unified OpenAI-compatible format before returning it to the caller. This "adapter pattern" approach significantly simplifies multi-model integration for developers.