GPT_API_free: Zero-Cost Access to GPT, DeepSeek, and Other LLM APIs

Project Overview: A Free LLM API Solution with 37,000+ Stars

In AI application development, API call costs have always been an unavoidable challenge for developers. Today's mainstream LLM APIs generally adopt a pay-per-token pricing model—tokens are the basic units that LLMs use to process text, where a single Chinese character typically corresponds to 1-2 tokens, and an English word approximately 1-4 tokens. Taking OpenAI as an example, GPT-4o's input price is $2.5 per million tokens and output price is $10 per million tokens; even the more economical GPT-4o-mini can rack up costs quickly with long-term, high-frequency usage. While DeepSeek^Ad, Claude, and other models each have different pricing strategies, the ongoing API expenses represent a significant burden for individual developers and small teams—especially during the development and debugging phase, where extensive trial-and-error calls can generate substantial bills.

The open-source GitHub project chatanywhere/GPT_API_free directly addresses this pain point—it provides free ChatGPT and DeepSeek API Keys, enabling developers to access GPT, DeepSeek, Claude, Gemini, Grok, and other mainstream LLMs at zero cost. The project has already earned over 37,700 Stars, making it one of the most popular open-source solutions in this space.

Core Features: One Endpoint to Access Multiple LLMs

Supported Model List

The biggest highlight of GPT_API_free is its broad model coverage. Rather than being limited to the GPT series alone, it aggregates today's most competitive LLMs:

• GPT Series: The full range of OpenAI models, including GPT-4. GPT-4 and its subsequent versions (GPT-4o, GPT-4o-mini, etc.) are built on the Transformer architecture—proposed by Google in the 2017 paper "Attention Is All You Need." Its core innovation is the Self-Attention mechanism, which allows the model to attend to all other words in the input sequence when processing each word, thereby capturing long-range semantic dependencies. The GPT series uses a Decoder-only variant of the Transformer, predicting the next token autoregressively. On top of this, OpenAI has conducted extensive alignment training using RLHF (Reinforcement Learning from Human Feedback), making outputs better aligned with human expectations. It leads the industry in general language understanding, creative writing, and complex instruction following.

• DeepSeek: A top-tier Chinese reasoning model with outstanding code generation and mathematical reasoning capabilities. Developed by DeepSeek AI, its DeepSeek-R1 model employs an innovative Mixture of Experts (MoE) architecture. Unlike traditional dense models that activate all parameters during each inference, MoE divides parameters into multiple "expert networks," with a lightweight Gating Network dynamically selecting only a few experts to process the current input. DeepSeek-R1 has a total of 671B parameters but activates only about 37B per inference, requiring just a fraction of the compute of a comparably capable dense model—this is the core technical reason why DeepSeek can offer API services at extremely low prices. DeepSeek-R1's performance on math competition problems and complex programming tasks approaches or even surpasses GPT-4 level, and its open-source strategy has had a profound impact on the entire industry.

• Claude: Made by Anthropic, excelling in long-text comprehension and safety. Claude series models (latest being Claude 3.5/4) feature ultra-long context windows—supporting up to 200K tokens, equivalent to processing approximately 150,000 Chinese characters at once. In terms of safety alignment, Anthropic proposed the Constitutional AI methodology: first, a set of explicit "constitutional" principles is defined for the model, then the model self-critiques and revises—it generates an initial response, evaluates whether its own output violates the constitutional principles, and then modifies any violations. This AI-supervising-AI approach significantly reduces dependence on human annotation while making safety rules more transparent and auditable—a key reason why Claude is highly favored in enterprise applications.

• Gemini: Google's multimodal LLM. Gemini was designed from the ground up with native multimodality as its core philosophy—unlike earlier approaches that "stitched together" independently trained visual encoders and language models via adapter layers, Gemini mixes text, image, audio, and video data from the pre-training stage, allowing the model to learn cross-modal semantic associations in a unified token space. This design enables the model to truly understand the intrinsic connections between different modalities, such as comprehending dynamic temporal relationships in video rather than merely performing surface-level feature alignment. Its latest version, Gemini 2.5 Pro, excels particularly in code generation and long-context reasoning, and deeply integrates Google Search capabilities for real-time information access.

• Grok: A real-time information model from xAI. Developed by Elon Musk's xAI company, Grok's unique advantage lies in its deep integration with the X (formerly Twitter) platform, enabling access to real-time social media data streams. This gives it an unmatched timeliness advantage over other models when answering questions about current events and real-time information queries.

This "one API endpoint, seamlessly switch between multiple models" approach