Code execution

Python Node.js Go REST

The Gemini API code execution feature enables the model to generate and run Python code and learn iteratively from the results until it arrives at a final output. You can use this code execution capability to build applications that benefit from code-based reasoning and that produce text output. For example, you could use code execution in an application that solves equations or processes text.

Code execution is available in both AI Studio and the Gemini API. In AI Studio, you can enable code execution in the right panel under Tools. The Gemini API provides code execution as a tool, similar to function calling. After you add code execution as a tool, the model decides when to use it.

The code execution environment includes the following libraries: altair, chess, cv2, matplotlib, mpmath, numpy, pandas, pdfminer, reportlab, seaborn, sklearn, statsmodels, striprtf, sympy, and tabulate. You can't install your own libraries.

Get started with code execution

A code execution notebook is also available:

View on ai.google.dev

Try a Colab notebook

View notebook on GitHub

This section assumes that you've completed the setup and configuration steps shown in the quickstart.

Enable code execution on the model

You can enable code execution on the model, as shown here:

from google import genai
from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(
  model='gemini-2.0-flash',
  contents='What is the sum of the first 50 prime numbers? '
           'Generate and run code for the calculation, and make sure you get all 50.',
  config=types.GenerateContentConfig(
    tools=[types.Tool(
      code_execution=types.ToolCodeExecution
    )]
  )
)

In a notebook you can display everything in Markdown format with this helper function:

def display_code_execution_result(response):
  for part in response.candidates[0].content.parts:
    if part.text is not None:
      display(Markdown(part.text))
    if part.executable_code is not None:
      code_html = f'<pre style="background-color: #BBBBEE;">{part.executable_code.code}</pre>' # Change code color
      display(HTML(code_html))
    if part.code_execution_result is not None:
      display(Markdown(part.code_execution_result.output))
    if part.inline_data is not None:
      display(Image(data=part.inline_data.data, format="png"))
    display(Markdown("---"))

display_code_execution_result(response)

The output might look something like this:

Okay, I need to calculate the sum of the first 50 prime numbers. I will use a python script to generate the first 50 primes and then sum them up.

```python
def is_prime(n):
  """Checks if a number is prime."""
  if n <= 1:
    return False
  for i in range(2, int(n**0.5) + 1):
    if n % i == 0:
      return False
  return True

def sum_of_primes(n):
  """Calculates the sum of the first n prime numbers."""
  primes = []
  i = 2
  while len(primes) < n:
    if is_prime(i):
      primes.append(i)
    i += 1
  return sum(primes)

# Calculate the sum of the first 50 prime numbers
sum_of_first_50_primes = sum_of_primes(50)

print(f"The sum of the first 50 prime numbers is: {sum_of_first_50_primes}")
```

**Output:**

```
The sum of the first 50 prime numbers is: 5117
```

Use code execution in chat

You can also use code execution as part of a chat.

from google import genai
from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

chat = client.chats.create(
    model='gemini-2.0-flash',
    config=types.GenerateContentConfig(
      tools=[types.Tool(
        code_execution=types.ToolCodeExecution
      )]
    )
)

response = chat.send_message("Can you run some code to bogo-sort this list of numbers?: [2,34,1,65,4]")
display_code_execution_result(response)

The conversation could continue like this:

I understand that you want me to bogo-sort the list [2, 34, 1, 65, 4]. Bogo-sort is a notoriously inefficient sorting algorithm. It works by repeatedly shuffling the list until it happens to be sorted. Due to its extremely poor performance, it's impractical for lists of any significant size.

However, for demonstration purposes, I can run a simplified version for a very limited number of iterations, just to show the process. Should I go ahead?

response = chat.send_message("Yes, please create the code.")
display_code_execution_result(response)

Okay,I will proceed with a function that shuffles the list and checks if it's sorted. I'll run it for a maximum of 10 iterations. ...

Input/output (I/O)

Starting with Gemini 2.0 Flash, code execution supports file input and graph output. Using these new input and output capabilities, you can upload CSV and text files, ask questions about the files, and have Matplotlib graphs generated as part of the response.

I/O pricing

When using code execution I/O, you're charged for input tokens and output tokens:

Input tokens:

• User prompt

Output tokens:

• Code generated by the model
• Code execution output in the code environment
• Summary generated by the model

I/O details

When you're working with code execution I/O, be aware of the following technical details:

• The maximum runtime of the code environment is 30 seconds.
• If the code environment generates an error, the model may decide to regenerate the code output. This can happen up to 5 times.
• The maximum file input size is limited by the model token window. In AI Studio, using Gemini Flash 2.0, the maximum input file size is 1 million tokens (roughly 2MB for text files of the supported input types). If you upload a file that's too large, AI Studio won't let you send it.

Billing

There's no additional charge for enabling code execution from the Gemini API. You'll be billed at the current rate of input and output tokens based on the Gemini model you're using.

Here are a few other things to know about billing for code execution:

• You're only billed once for the input tokens you pass to the model, and you're billed for the final output tokens returned to you by the model.
• Tokens representing generated code are counted as output tokens. Generated code can include text and multimodal output like images.
• Code execution results are also counted as output tokens.

The billing model is shown in the following diagram:

• You're billed at the current rate of input and output tokens based on the Gemini model you're using.
• If Gemini uses code execution when generating your response, the original prompt, the generated code, and the result of the executed code are labeled intermediate tokens and are billed as input tokens.
• Gemini then generates a summary and returns the generated code, the result of the executed code, and the final summary. These are billed as output tokens.
• The Gemini API includes an intermediate token count in the API response, so you know why you're getting additional input tokens beyond your initial prompt.

Limitations

• The model can only generate and execute code. It can't return other artifacts like media files.
• In some cases, enabling code execution can lead to regressions in other areas of model output (for example, writing a story).
• There is some variation in the ability of the different models to use code execution successfully.