Same Model, Different Results

Run Claude Sonnet in three different tools and you get three different experiences. Same weights. Same training data. Different output.

This is the part that throws people off. If the model is identical, what changes from tool to tool? In Cursor it seems to "understand" your project. In a raw API call it can stumble because the context strategy changes. The model is one layer in the stack. The wrapper around it, the context fed into it, and the agent loop running it shape the result.

As Builder.io put it, in 2026 the best LLM for coding is a full stack, not just a model. That framing changes how I evaluate tools, and it should probably change how you evaluate them too.

Three Tools, One Model

Here are three tools that all run Claude Sonnet under the hood. Each one solves the same problem in a very different way.

Cursor is IDE-native. It lives inside your editor and builds a vector index of your codebase. Ask a question and it pulls relevant files into context next to your prompt. Its Composer agent mode can generate code at roughly 250 tokens per second and apply multi-file edits directly in your workspace. You stay in the editor the whole time.

Claude Code is terminal-first. It uses Anthropic's Model Context Protocol, so it can read files, run commands, search your codebase, and call external services. It supports extended thinking for harder reasoning and can use the full 200k context window. You stay in terminal and the agent decides what it needs to inspect.

Antigravity is Google's agent-first platform. It is documentation-driven, with artifact-based context and a skills/workflows system for structured deliverables. Instead of living in your editor or terminal, it builds an understanding of your project from explicit docs and generates code against that spec.

Same Claude Sonnet model under all three. The day-to-day experience is radically different.

The Reason for the Difference

Four factors explain most of the divergence.

Context strategy is the biggest one. Each tool decides what code to show the model, and that choice changes the outcome. Cursor uses vector indexing to retrieve semantically relevant files. Claude Code scopes context dynamically and reads files on demand as the agent explores. Antigravity uses artifact-based context and builds understanding through structured documentation over raw code. As Coderide.ai notes: "Without context, the AI guesses. With context, it knows."

Tool harness determines what actions the model can take. Cursor gives the model over ten tools for searching, editing, and running terminal commands inside the IDE. Claude Code uses MCP with dynamic tool loading, so the model can call external services, databases, or custom integrations. Each toolbox enables one style of work and limits another. A model that can only edit files will behave differently from one that can run your test suite and read the output.

Prompt engineering is the invisible layer. Each product wraps your request in its own system prompts, instructions, and formatting guidelines before it reaches the model. Small wording changes in those prompts can change the output a lot. OpenAI's prompt engineering guide covers this well, and every serious AI product spends real effort tuning these hidden prompts. You are never talking directly to the model. You are talking to the product's interpretation of the model.

Agent loop design governs how the orchestrator decides what to do next, when to stop, and how to recover from errors. A tool that retries failed edits three times will usually outperform one that gives up immediately. A tool that validates its own output by running your linter will catch mistakes that a single-pass tool misses. These orchestration choices compound across a session.

Finding What Fits

The practical takeaway is straightforward. Stop asking "which tool is best" and start asking "which tool fits how I work."

Think about your own patterns. Do you think in terminals or GUIs? Do you like staying inside your editor, or moving across tools? Is your codebase a monorepo or a set of smaller services? Are you exploring a new idea or converging on a specific implementation? Those preferences usually point to a different tool choice.

The Qodo comparison of Claude Code and Cursor found something worth internalizing: "output quality is mostly determined by how clearly and structured you plan and describe the task." The tool matters. The way you communicate with it often matters more. A clear, well-structured prompt in any of these tools will usually beat a vague request in the "best" one.

If you have not already, run the same task through multiple tools. Pick a real task from your current project, something non-trivial and well-defined, then run it through two or three options. Watch where each tool excels and where it struggles. That direct comparison will teach you more than any benchmark.

The Toolchain is the Product

The model race will continue. There will be a new release every few months, each one slightly better than the last. The tool you use to harness that model is the actual product you are buying. The context strategy, the agent loop, the tool integrations, and the prompt engineering are where real differentiation lives.

Figuring out what works for you, your projects, and your goals is the real skill in this environment. The model is the engine. The toolchain is the car. Nobody picks a car from the engine spec sheet alone.