In this tutorial, we’ll explore the new capabilities introduced in OpenAI’s latest model, GPT-5. The update brings several powerful features, including the Verbosity parameter, Free-form Function Calling, Context-Free Grammar (CFG), and Minimal Reasoning. We’ll look at what they do and how to use them in practice. Check out the Full Codes here.
Installing the libraries
!pip install pandas openaiTo get an OpenAI API key, visit https://platform.openai.com/settings/organization/api-keys and generate a new key. If you’re a new user, you may need to add billing details and make a minimum payment of $5 to activate API access. Check out the Full Codes here.
import osfrom getpass import getpassos.environ['OPENAI_API_KEY'] = getpass('Enter OpenAI API Key: ')Verbosity Parameter
The Verbosity parameter lets you control how detailed the model’s replies are without changing your prompt.
- low → Short and concise, minimal extra text.medium (default) → Balanced detail and clarity.high → Very detailed, ideal for explanations, audits, or teaching. Check out the Full Codes here.
from openai import OpenAIimport pandas as pdfrom IPython.display import displayclient = OpenAI()question = "Write a poem about a detective and his first solve"data = []for verbosity in ["low", "medium", "high"]: response = client.responses.create( model="gpt-5-mini", input=question, text={"verbosity": verbosity} ) # Extract text output_text = "" for item in response.output: if hasattr(item, "content"): for content in item.content: if hasattr(content, "text"): output_text += content.text usage = response.usage data.append({ "Verbosity": verbosity, "Sample Output": output_text, "Output Tokens": usage.output_tokens })# Create DataFramedf = pd.DataFrame(data)# Display nicely with centered headerspd.set_option('display.max_colwidth', None)styled_df = df.style.set_table_styles( [ {'selector': 'th', 'props': [('text-align', 'center')]}, # Center column headers {'selector': 'td', 'props': [('text-align', 'left')]} # Left-align table cells ])display(styled_df)The output tokens scale roughly linearly with verbosity: low (731) → medium (1017) → high (1263).
Free-Form Function Calling
Free-form function calling lets GPT-5 send raw text payloads—like Python scripts, SQL queries, or shell commands—directly to your tool, without the JSON formatting used in GPT-4. Check out the Full Codes here.
This makes it easier to connect GPT-5 to external runtimes such as:
- Code sandboxes (Python, C++, Java, etc.)SQL databases (outputs raw SQL directly)Shell environments (outputs ready-to-run Bash)Config generators
from openai import OpenAIclient = OpenAI()response = client.responses.create( model="gpt-5-mini", input="Please use the code_exec tool to calculate the cube of the number of vowels in the word 'pineapple'", text={"format": {"type": "text"}}, tools=[ { "type": "custom", "name": "code_exec", "description": "Executes arbitrary python code", } ])print(response.output[1].input)
This output shows GPT-5 generating raw Python code that counts the vowels in the word pineapple, calculates the cube of that count, and prints both values. Instead of returning a structured JSON object (like GPT-4 typically would for tool calls), GPT-5 delivers plain executable code. This makes it possible to feed the result directly into a Python runtime without extra parsing.
Context-Free Grammar (CFG)
A Context-Free Grammar (CFG) is a set of production rules that define valid strings in a language. Each rule rewrites a non-terminal symbol into terminals and/or other non-terminals, without depending on the surrounding context.
CFGs are useful when you want to strictly constrain the model’s output so it always follows the syntax of a programming language, data format, or other structured text — for example, ensuring generated SQL, JSON, or code is always syntactically correct.
For comparison, we’ll run the same script using GPT-4 and GPT-5 with an identical CFG to see how both models adhere to the grammar rules and how their outputs differ in accuracy and speed. Check out the Full Codes here.
from openai import OpenAIimport reclient = OpenAI()email_regex = r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$"prompt = "Give me a valid email address for John Doe. It can be a dummy email"# No grammar constraints -- model might give prose or invalid formatresponse = client.responses.create( model="gpt-4o", # or earlier input=prompt)output = response.output_text.strip()print("GPT Output:", output)print("Valid?", bool(re.match(email_regex, output)))
from openai import OpenAIclient = OpenAI()email_regex = r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$"prompt = "Give me a valid email address for John Doe. It can be a dummy email"response = client.responses.create( model="gpt-5", # grammar-constrained model input=prompt, text={"format": {"type": "text"}}, tools=[ { "type": "custom", "name": "email_grammar", "description": "Outputs a valid email address.", "format": { "type": "grammar", "syntax": "regex", "definition": email_regex } } ], parallel_tool_calls=False)print("GPT-5 Output:", response.output[1].input)
This example shows how GPT-5 can adhere more closely to a specified format when using a Context-Free Grammar.
With the same grammar rules, GPT-4 produced extra text around the email address (“Sure, here’s a test email you can use for John Doe: johndoe@example.com”), which makes it invalid according to the strict format requirement.
GPT-5, however, output exactly john.doe@example.com, matching the grammar and passing validation. This demonstrates GPT-5’s improved ability to follow CFG constraints precisely. Check out the Full Codes here.
Minimal Reasoning
Minimal reasoning mode runs GPT-5 with very few or no reasoning tokens, reducing latency and delivering a faster time-to-first-token.
It’s ideal for deterministic, lightweight tasks such as:
- Data extractionFormattingShort rewritesSimple classification
Because the model skips most intermediate reasoning steps, responses are quick and concise. If not specified, the reasoning effort defaults to medium. Check out the Full Codes here.
import timefrom openai import OpenAIclient = OpenAI()prompt = "Classify the given number as odd or even. Return one word only."start_time = time.time() # Start timerresponse = client.responses.create( model="gpt-5", input=[ { "role": "developer", "content": prompt }, { "role": "user", "content": "57" } ], reasoning={ "effort": "minimal" # Faster time-to-first-token },)latency = time.time() - start_time # End timer# Extract model's text outputoutput_text = ""for item in response.output: if hasattr(item, "content"): for content in item.content: if hasattr(content, "text"): output_text += content.textprint("--------------------------------")print("Output:", output_text)print(f"Latency: {latency:.3f} seconds")
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