01What Pi Agent vs Claude Code Means

Pi Agent vs Claude Code is a comparison of two AI agent harnesses. A harness is the software wrapper that controls how an AI model plans, uses tools, and completes tasks. Think of the model as the engine and the harness as the car you put it in.

In a public test by Composio, the same model—DeepSeek V4 Flash—was run through eight different harnesses on 30 hard agentic tasks. Pi Agent passed 20 tasks out of 30, while Claude Code passed 15. This shows that the harness can dramatically change the results you get from the same AI.

02How the Pi Agent vs Claude Code Test Worked

The test used one model, DeepSeek V4 Flash, and pushed it through eight different agent harnesses. Each harness had to complete 30 tasks that required the agent to use tools and finish jobs on its own. The tasks were designed to test real-world abilities.

The harness decides how the model plans, how it uses tools, how it handles mistakes, and when it stops. So the same model might perform well in one harness but poorly in another. This is why the comparison matters.

The harness is not the model itself. It is the layer that connects the AI to tools and guides its actions. A good harness can make a mid-tier model shine, while a bad one can drag down a top model.

03Key Results from the Pi Agent vs Claude Code Test

Here are the main numbers from the test:

  • Pi Agent passed 20 out of 30 tasks.
  • DeepAgents (from LangChain) passed 16 out of 30.
  • Hermes Agent passed 15 out of 30.
  • PrimeAgent passed 15 out of 24 valid runs—6 runs were thrown out because the grader couldn't score them.

Speed also differed. Pi Agent finished tasks in about 132 seconds median. Hermes took about 175 seconds.

DeepAgents took about 187 seconds. PrimeAgent took about 242 seconds.

Pi Agent was both the most successful and one of the fastest. But Claude Code and OpenCode were quicker in some cases. Still, Pi Agent came out on top for pass rate and efficiency combined.

04The Harness Multiplier: Why Setup Matters

Across all eight harnesses, the exact same model scored between 47% and 67% task success. That is a 20-point swing based only on the wrapper. The AI never got smarter or worse—only the harness changed.

This is what Composio calls the harness multiplier. The tool you wrap around your AI multiplies the result up or down. Pick the right harness and the same model becomes more reliable and faster.

Pick the wrong one and you get more failures and slower runs with the same intelligence underneath.

Most people argue about which model is best—GPT versus Claude versus DeepSeek versus Gemini. But the harness is quietly deciding half the outcome. Composio's conclusion was simple: benchmark the model-harness pair you actually use, not the model in isolation.

05Why the Light Setup Won

Pi Agent ran with almost nothing added. It was a fresh, vanilla install. The only extra was the MCP server plugin, which is a standard connector for outside tools.

There was no custom setup, no tuning, no special configuration.

In contrast, PrimeAgent ran with the heaviest sessions of all eight harnesses. Some sessions hit 3.5 million tokens and 33 tool calls. That is like an agent writing a to-do list as long as a phone book before starting a job.

Two runs couldn't be graded because of the size. Four more never recorded at all. Six runs were lost total.

The heavy setup choked on its own weight. The light setup passed the most tasks in the least time. More layers did not mean better results—it meant the opposite.

A clean harness gives the model a short path from task to done. A bloated one sends it wandering.

06Three Beliefs That Stop You from Testing

Julian Goldie points out three common beliefs that hold people back:

  • Belief 1: "I need the most powerful model." This test shows the opposite. The model was fixed, yet results swung 20 points based on the harness. A lightweight model in the right harness passed more tasks than most would expect.
  • Belief 2: "This is technical, so it doesn't apply to me." If you are not technical, your choice of harness matters even more because you can't fix a broken setup. The winning harness was vanilla—what a non-technical person would end up with anyway.
  • Belief 3: "Agents are unreliable, so I'll wait." The spread of 47% to 67% shows that some pairings work well right now. Waiting doesn't improve your setup—testing does.

People who get results today are testing pairings and keeping the ones that pass.

07How to Build Your Own Mini Test

You don't need 30 tasks like Composio. Julian suggests you make your own test with three tasks. Follow these steps:

  1. Write down three real tasks from your actual week. Things you do by hand, like sorting leads, writing a first draft of an email, or pulling details from a spreadsheet.
  2. Run those same three tasks through two different harnesses. Use the same model and the same instructions word for word. Change only one variable at a time.
  3. Score the results: Did it finish the job (pass/fail)? How long did it take? How much cleanup did you have to do?
  4. Keep the winner and retest when new versions ship. Harness updates can flip results quickly.

The last column—cleanup—is the honest one most people skip. An agent that finishes but needs 20 minutes of fixing didn't really pass.

08What the Test Doesn't Tell You

Julian is honest about the limits of this test. It used only one model (DeepSeek V4 Flash) and one set of 30 tasks. Composio said clearly that different harnesses suit different models.

Pi Agent won for DeepSeek, but that does not mean it wins for every model.

Claude Code was quicker overall. Hermes has its own strengths and is built for a wider range of agentic work than pure task running. The lesson is not that everyone should switch to Pi.

The lesson is that the pairing is the product, so test yours.

Don't read a benchmark headline as "this model is better." Always ask: Which harness was it run on? That answer matters more than the model name at the top of the chart.