Claude Code Sends 33k Tokens Before Reading Your Prompt—OpenCode Sends 7k: The Hidden Cost of Vibe Coding

The Hidden Token Tax in AI-Assisted Development

If you've been vibe coding—building apps by describing what you want in natural language and letting an AI agent do the heavy lifting—you've probably noticed something strange. Sometimes the AI seems to "think" for an unusually long time before writing a single line of code. Other times, it starts hallucinating context, mixing up variables from unrelated projects, or producing bloated outputs that are hard to debug.

I've spent the last two years building production systems with AI coding agents—first with Claude Code, then with OpenCode. What I discovered changed how I think about token efficiency and, more importantly, why your AI assistant might be burning through your compute budget before it even reads your instructions.

Here's the raw data: Claude Code sends approximately 33,000 tokens in its system prompt before processing your user input. OpenCode sends around 7,000. That 26,000-token difference is not trivial—it directly impacts response latency, cost, and output quality.

What Are System Prompts and Why Do They Matter?

Every AI coding tool starts with a hidden "system prompt"—a set of instructions that defines the agent's behavior, capabilities, and constraints. This prompt is prepended to every conversation, even before you type a single character. Think of it as the AI's internal operating manual.

The size of that manual matters for three reasons:

1. Latency: More tokens mean longer processing time before the first response. With Claude Code, you might wait 10-15 seconds before seeing any output. With OpenCode, that wait drops to 2-4 seconds.

2. Cost: If you're paying per token (and most API-based tools do), that 33k token system prompt is charged on every request. Run 100 sessions a day, and you're burning through thousands of tokens just on overhead.

3. Context window: Every token in the system prompt reduces the available space for your actual code and conversation. With Claude Code's 200k token context window, 33k of overhead means only 167k tokens remain for your work. OpenCode's 7k overhead leaves 193k tokens of usable space.

How I Measured the Token Counts

I used a simple approach: I captured the raw HTTP requests from both tools using a local proxy (mitmproxy), extracted the system messages, and counted tokens using each model's tokenizer (Claude uses Anthropic's tokenizer; OpenCode uses OpenAI's tiktoken).

Source: Measured on July 10, 2026, using default configurations of both tools. Costs based on API pricing at the time of measurement.

The Practical Impact on Vibe Coding

Case 1: Building a CRUD API

I asked both tools to build a simple CRUD API for a task management app using Python and FastAPI. Here's what happened:

• Claude Code: Spent 18 seconds "thinking" (processing the 33k system prompt plus my request), then generated a complete project with 12 files, including a Docker setup, CI/CD pipeline, and unit tests. The output was thorough but included unnecessary boilerplate (e.g., Redis caching, which I didn't ask for).

• OpenCode: Started generating code in 3 seconds. Produced a minimal but complete API in 3 files. It asked if I wanted Docker and tests, rather than assuming.

Takeaway: Claude Code's larger system prompt includes more aggressive scaffolding templates. Great if you need enterprise-grade boilerplate. Bad if you want to iterate fast.

Case 2: Debugging a React Component

I gave both tools a broken React component that wasn't rendering correctly. The bug was a missing dependency in useEffect.

• Claude Code: Analyzed the component for 12 seconds, then suggested refactoring the entire component into a custom hook with three sub-components. The fix worked, but introduced unnecessary complexity.

• OpenCode: Identified the missing dependency in 2 seconds, suggested adding it, and offered to explain why. The fix was one line.

Takeaway: The larger system prompt in Claude Code seems to encourage more comprehensive—but often over-engineered—solutions. OpenCode's leaner prompt leads to more focused responses.

Why Does Claude Code Send So Many Tokens?

Claude Code's system prompt includes:

• Extensive behavioral rules: Detailed instructions on how to structure code, handle errors, and interact with the user.
• Contextual examples: Multiple in-context examples of desired output formats.
• Safety and constraint definitions: Guidelines to prevent harmful or unethical code generation.
• Tool definitions: Descriptions of available tools (e.g., file system access, shell commands).

Anthropic's focus on safety and alignment means they pack more guardrails into the system prompt. While this can prevent certain classes of errors, it also introduces overhead.

Why OpenCode Sends 7,000 Tokens

OpenCode takes a minimalist approach:

• Core instructions only: Basic rules for code generation and interaction.
• No in-context examples: Relies on the model's pre-training.
• Simpler tool definitions: Fewer tools, each with shorter descriptions.

This design philosophy prioritizes speed and efficiency over comprehensive guardrails. The trade-off is that OpenCode may occasionally produce less safe or less structured code—but in practice, I've found the difference negligible.

How to Optimize Your AI Coding Workflow

Regardless of which tool you use, here are practical steps to reduce token waste:

1. Trim Your Own Prompts

Every word you add costs tokens. Be concise. Instead of:

"Please write a Python function that takes a list of numbers and returns the sum, but make sure to handle empty lists gracefully, and also add comments explaining each step, and use type hints..."

Write:

"Python sum function: list of numbers, handle empty, type hints."

2. Use Explicit Context Windows

Both tools allow you to start fresh conversations. If you're working on unrelated tasks, don't carry over old context. Each conversation accumulates tokens from previous exchanges.

3. Disable Unnecessary Features

Claude Code has options to disable certain tool integrations (e.g., web search, file system access). If you're only generating code, turn off what you don't need. This reduces the system prompt size.

4. Pre-Process Your Input

If you're providing large files, consider summarizing them first. Instead of pasting a 500-line file, extract the relevant 20 lines and describe the rest.

5. Monitor Your Token Usage

Use tools like OpenCost or custom scripts to track tokens per session. I built a simple dashboard using the OpenAI API logs that shows me exactly how many tokens each agent uses. This helped me identify sessions where I was wasting tokens on unnecessary back-and-forth.

The Future of Vibe Coding Tools

The token efficiency gap between Claude Code and OpenCode reflects a broader trend in AI development tools:

• Heavyweight agents (Claude Code, GitHub Copilot Chat) prioritize safety, comprehensiveness, and enterprise readiness. They're ideal for complex, high-stakes projects where code quality and security are paramount.

• Lightweight agents (OpenCode, Cursor) prioritize speed, iteration, and developer experience. They're better for prototyping, learning, and rapid development.

Neither approach is inherently better. The choice depends on your use case:

Real-World Results: What I've Built

Since switching to a hybrid approach—using Claude Code for initial architecture and OpenCode for iterative development—I've:

• Reduced average development time per feature by 40%
• Cut API costs by 60% (from ~$200/month to ~$80/month)
• Increased code quality (fewer hallucinations, fewer unnecessary files)

One concrete example: I built a real-time data pipeline that ingests social media analytics from multiple APIs, processes them, and exposes a REST endpoint. I used Claude Code to design the architecture (it produced a solid plan with proper error handling and monitoring), then switched to OpenCode to implement each module. The total development time was 4 hours—something that would have taken 2 days manually.

Conclusion

Vibe coding is powerful, but it's not magic. The hidden token tax in your AI agent's system prompt directly impacts speed, cost, and output quality. Understanding that Claude Code sends 33,000 tokens before reading your prompt—while OpenCode sends only 7,000—can help you choose the right tool for the job and optimize your workflow.

My advice: Don't commit to a single tool. Use heavyweight agents for planning and architecture, lightweight agents for execution. Monitor your token usage. And always, always ask yourself: "Is the AI's overhead worth the output?"

For most rapid development tasks, the answer is no—and that's why I reach for OpenCode first.