Getting Started with Coze: Credit System, Core Features & Pitfalls to Avoid

What Is Coze?

Coze is an AI low-code platform launched by ByteDance, available at coze.cn. Its core positioning is to enable users to build Agents, Workflows, and RAG (Retrieval-Augmented Generation) applications without deep programming expertise.

The concept of "low-code platforms" has been a significant trend in software development in recent years. Traditional application development requires developers to write extensive code, set up backend services, configure databases, and deploy environments—a process that can take weeks or even months. Low-code platforms compress this timeline to hours or even minutes through pre-built components, visual orchestration, and automated deployment. Gartner predicted as early as 2020 that by 2025, 70% of new applications globally would be built using low-code/no-code technologies. In the AI wave, Coze represents the convergence of "AI + low-code"—not only is the development process low-code, but the output itself is an AI-driven intelligent application. Compared to similar platforms like Dify and FastGPT, Coze leverages ByteDance's ecosystem advantages to differentiate through model resources, user base, and distribution channels.

In simple terms, Coze wraps complex LLM calls, workflow orchestration, and knowledge base management into visual modules. Users can build functional AI applications through drag-and-drop, configuration, or even a single natural language description. This is the key reason for its rapid rise in the AI application development space—lowering the barrier to entry and enabling quick onboarding.

Coze recently underwent a major redesign, adding a "Coze Programming" section with updated interfaces and interactions. Note that some post-redesign features are still in public beta with stability yet to be optimized. The official team has explicitly stated that beta usage does not consume user credits.

Credit System Explained: Understanding Coze's "Currency"

To understand how Coze works, you need to grasp its Credit mechanism. Building applications on Coze requires calling LLMs, and LLM calls have costs, so the platform uses credits as its billing unit.

To understand the pricing logic behind credits, you first need to know how LLMs are billed at the infrastructure level. Currently, mainstream LLMs (such as GPT-4, Doubao, etc.) charge API calls based on Tokens. A Token is the smallest unit of text the model processes—one Chinese character corresponds to roughly 1.5 to 2 Tokens, while one English word corresponds to about 1 to 1.5 Tokens. Taking GPT-4o as an example, the price for 1 million input Tokens is approximately $2.5, with output being more expensive. Coze's credits are essentially an abstraction layer over underlying Token consumption—the platform converts different models' call costs and usage into a unified credit system, so users don't need to worry about which model was called or how many Tokens were consumed, just the change in their credit balance. Image-related calls consume more credits because image generation models (such as Stable Diffusion, DALL-E, etc.) have significantly higher per-inference costs than text models.

Here are the specific rules:

• Free users: 500 credits per person per day
• Text-based calls: approximately 10+ credits per call
• Image-based calls: approximately 30-40 credits per call
• Personal plan: 9.9 CNY/month, approximately 30,000 credits per month, with additional credit packs available for purchase

Important Pitfall Warning

Do not directly run complex AI applications (such as video generation) in the template store. A single run of these applications can consume over 5,000 credits, and the system won't warn you about insufficient balance in advance—it starts consuming from zero, and only alerts you that it can't continue after your 500 credits are depleted. For free users, this essentially means one run exhausts the entire daily quota with no usable result.

Coze Programming Feature Test: Generate Complete Projects with a Single Sentence

The most noteworthy post-redesign feature is "Coze Programming." It's an upgrade and migration of the previous "AI Create Application" capability, now supporting complete code project generation directly from natural language.

Natural Language Programming isn't an entirely new concept, but it only became practical after the emergence of large language models. Earlier code generation tools (like GitHub Copilot) primarily handled line-level or function-level completions within code editors, while next-generation tools like Claude Artifacts, Cursor, and Bolt.new can generate complete multi-file projects from a natural language description. Coze Programming is a productized implementation of this technical direction: it calls LLMs in the backend for code generation while leveraging Coze's deployment capabilities to let generated projects be previewed and run online immediately. However, it's important to be realistic—the reliability of natural language code generation still has clear limitations. For complex business logic, multi-module interactions, and performance optimization scenarios, generated code often requires manual review and modification. We're still quite far from "say one sentence and ship a production-grade application."

Here's a practical example: simply type "Generate a TikTok-like Web UI application," enable the web search option, and hit enter. Coze will automatically name the project, display its thinking process, generate the corresponding code, and run the project so you can see the results.

From hands-on testing, the current Coze Programming feature is powerful but not yet mature and still needs optimization. However, compared to the previous version, this new entry point shows clear improvement in project generation completeness. Free users typically get only one opportunity per month for this kind of complete project creation, while paid users may get around 10.

Core Feature Modules Overview

Coze's main interface contains several key sections. Understanding their positioning helps you get started quickly.

Project Development & Resource Library

• Project Development: Manage all applications you've created
• Resource Library: Store reusable components for building low-code applications, including prompts, workflows, plugins, etc.—think of it as your "asset warehouse"
• Task Center & Performance Evaluation: Mostly paid features for workflow testing and effectiveness assessment

Changes in Space Configuration

Notably, Coze has relaxed restrictions on space management. Previously, individual users only had one default space, but now individuals can create multiple spaces, meaning free users gain more flexible project organization capabilities.

Three Main Application Types

The redesigned Coze supports generating three types of content through natural language:

1. Agent: A conversational AI assistant suitable for customer service, Q&A, and similar scenarios. AI Agents are one of the hottest directions in LLM applications today. Unlike traditional chatbots, an Agent isn't just a "you ask, I answer" dialogue system—it possesses the ability to perceive its environment, make autonomous decisions, and execute actions. A typical Agent architecture contains four core modules: a large language model (responsible for reasoning and decision-making), a memory system (storing conversation history and context), tool-calling capabilities (such as search engines, database queries, API calls, etc.), and planning capabilities (decomposing complex tasks into multiple sub-steps for sequential execution). On the Coze platform, users can define an Agent's role and behavioral boundaries through system prompts, inject domain-specific knowledge by attaching knowledge bases, and grant tool-calling abilities by binding plugins—thereby building intelligent assistants tailored to specific scenarios.

2. Workflow: Multi-step automated task orchestration suitable for content generation, data processing, etc. The concept of workflow orchestration originates from enterprise IT's BPM (Business Process Management) and RPA (Robotic Process Automation), but takes on new meaning in AI contexts. Traditional RPA primarily handles deterministic, rule-based repetitive operations (like auto-filling forms or data migration), while AI workflows introduce LLM reasoning capabilities, enabling each node in the process to make non-deterministic intelligent judgments—for example, performing sentiment analysis on text to decide which downstream branch to follow, or dynamically generating different content based on user input. Coze's workflow editor uses a DAG (Directed Acyclic Graph) visualization approach where users can drag nodes and connect them. Each node can be an LLM call, code execution, conditional logic, knowledge base retrieval, or external API request. This orchestration method is especially suited for complex tasks requiring multi-step collaboration, such as "read user-uploaded document → extract key information → call model to generate summary → translate to English → send email."

3. Web Application: A complete Web application with a frontend interface

Additionally, the bottom still retains "Low-code Mode" for accessing the traditional Agent development and workflow development interfaces, which are essentially the same as the old version. If you're uncomfortable with the new interface, you can switch back to the old version at any time.

Leverage Templates & Plugins: Stand on Others' Shoulders

One highly practical capability in the Coze ecosystem is the abundance of templates and plugins available for copying and reference.

In the template store, the official team provides numerous AI application samples with interfaces, such as "Video Generation" and "Story Generation." These applications can not only be test-run to see results, but more importantly, they can be copied to your personal space with one click, allowing you to deconstruct and learn the workflow implementation logic behind them.

For example, copy a "Story Generation" application to your personal space, and you'll find it's essentially a workflow where you can clearly see which models are called and how they're orchestrated. If a model used in the template has been discontinued, the system will prompt you to replace it—simply switch to a free model provided by Coze to continue using it.

This involves an important technical concept—RAG (Retrieval-Augmented Generation). In many template applications, you'll see "Knowledge Base" nodes embedded in workflows, which is a typical RAG application. The core problem RAG solves is that LLM training data has a cutoff date and cannot cover enterprise private data, so directly asking the model professional questions can easily produce "hallucinations" (fabricating information that sounds plausible but is actually incorrect). RAG works by first performing vector retrieval from an external knowledge base to find document fragments most relevant to the user's question after they ask it, then sending these fragments as context along with the user's question to the LLM, allowing the model to generate answers based on real materials. In Coze, users can upload PDFs, Word documents, web content, and more to build their own knowledge bases, then reference them in Agents or workflows, enabling AI applications to answer domain-specific questions. This is also one of the most common implementation patterns in enterprise AI applications.

Plugin Selection Tips

The plugin store contains both official and third-party plugins. Prioritize official plugins as they tend to be more stable. Given that Coze is currently in a feature upgrade period, even official features may occasionally throw errors—this is something to mentally prepare for at this stage.

Recommended Onboarding Path for Beginners

As an AI low-code platform, Coze's greatest value lies in enabling non-professional developers to quickly build AI applications. Whether you're building an intelligent customer service bot, a content generation tool, or orchestrating complex multi-step workflows, it provides a relatively intuitive visual path.

Recommended learning path:

1. Familiarize yourself with basic operations: Start by using the free 500 daily credits to experience core features
2. Copy templates to learn: Understand workflow logic by deconstructing official templates
3. Build hands-on: Gradually try building your own Agents and knowledge bases
4. Manage resources wisely: Avoid wasting daily quota on high-consumption applications

Coze is still iterating rapidly, with stability and feature completeness continuously improving. For users looking to get into AI application development at low cost, it's a platform worth investing time to learn.

Key Takeaways