GPT_API_free: Free GPT-4/DeepSeek/Claude API Key Application Tutorial

GPT_API_free Project Overview: Why Developers Need Free LLM APIs

The cost of calling LLM APIs has always been a pain point developers can't avoid. API pricing for OpenAI's GPT-4, Anthropic's Claude, and similar models sets a high bar for individual developers and small teams, especially during the learning, exploration, and prototype validation stages. Taking OpenAI as an example, its API uses a per-token billing model—GPT-4 Turbo costs approximately $10 per million input tokens and about $30 per million output tokens. Even the cheaper GPT-3.5 Turbo can quickly accumulate significant costs with long-term, high-frequency usage.

Tokens are the fundamental units that large language models use to process text, and understanding the token billing mechanism is crucial for evaluating API costs. The BPE (Byte Pair Encoding) algorithm used by OpenAI encodes common words as single tokens based on word frequency statistics from the corpus, while rare words are split into multiple subword tokens. This leads to significant differences in token efficiency across languages: in English, each word corresponds to approximately 1.3 tokens on average, while in Chinese, due to the large character set, each character typically requires 1-2 tokens, and Japanese kana may even require multiple tokens per character. This means processing the same amount of information in Chinese actually consumes more tokens than in English, resulting in higher costs. Additionally, API billing distinguishes between input tokens (Prompt content) and output tokens (model-generated content), with output tokens typically priced higher because the generation process is more computationally intensive than encoding. When designing systems, developers can effectively reduce token consumption by streamlining System Prompts, controlling conversation history length, and setting reasonable max_tokens limits. For developers who need to repeatedly debug prompts and run numerous experiments, every API call costs real money, creating considerable psychological and financial burden during the learning phase.

The open-source project chatanywhere/GPT_API_free on GitHub was created precisely to solve this problem—it provides free ChatGPT and DeepSeek API Keys, allowing developers to access mainstream large language models at zero cost.

As of now, the project has received over 37,700 Stars and 2,625 Forks, ranking among the top free API projects with highly active community engagement.

Models and API Interfaces Supported by GPT_API_free

Covered Mainstream LLM List

GPT_API_free doesn't just support a single model—it aggregates API interfaces for multiple leading large language models:

• GPT-4/GPT-3.5: OpenAI's models, currently the most widely used commercial LLMs
• DeepSeek-V3/R1: Representatives of Chinese-developed LLMs, with outstanding reasoning capabilities and excellent cost-performance ratio. DeepSeek-V3 adopts a Mixture of Experts (MoE) architecture with a total of 671B parameters but only activates 37B parameters per inference, achieving an excellent balance between performance and efficiency. DeepSeek-R1 focuses on reasoning ability, using reinforcement learning to train the model with Chain-of-Thought capability, delivering impressive performance in mathematical reasoning and code generation tasks. The core idea of MoE architecture is to divide the model into multiple "experts"