GPT-6 Sol API Tutorial: Build a Reasoning App with Python

GPT-6 Sol became available through the OpenAI API on September 22, 2026, giving developers a new reasoning model aimed at complex coding and agentic workloads. The useful part is not simply changing a model name: GPT-6 Sol has a 1.05 million-token context window, configurable reasoning effort, and Responses API support for tools and function calling. This tutorial shows how to make a small Python application with the model, control its reasoning effort, and extend the same request into structured application output. 

What changes when you use GPT-6 Sol

GPT-6 Sol uses the model identifier gpt-6-sol and is designed for demanding reasoning, coding, and agentic workflows. OpenAI lists six reasoning-effort settings for the model: none, low, medium, high, xhigh, and max, with medium as the default. Lower effort generally favors speed and lower token usage, while higher effort gives the model more room to work through difficult problems. 

For new applications that need tools or function calling, OpenAI recommends the Responses API. GPT-6 Sol can also be used with Chat Completions, but its function calling there is limited to requests using reasoning_effort set to none. That distinction matters if you start with a simple chatbot and later turn it into an application that calls databases, internal services, or other tools. 

Set up the Python project before making the first request

You need Python, an OpenAI API key, and the current OpenAI Python package. Create a small project, install the SDK, and expose your API key through the environment. The SDK reads the key automatically when the standard environment variable is available.

pip install --upgrade openai

Set the API key in your operating system environment rather than hard-coding it. On Windows PowerShell, for example, you can set the variable for the current session with:

$env:OPENAI_API_KEY = "your-api-key"

On Linux or macOS, the equivalent shell command is:

export OPENAI_API_KEY="your-api-key"

Now create a file such as app.py and import the client:

from openai import OpenAI

client = OpenAI()

If the SDK starts without an authentication error, the client has successfully picked up the environment variable. Keeping this setup separate from your application logic also makes it easier to move the project between a local machine, a server, and a deployment environment.

Make the first GPT-6 Sol request with the Responses API

The first useful test should be deliberately small. You want to prove that authentication, model selection, request formatting, and response handling all work before adding tools or application state. GPT-6 Sol accepts text input and returns generated text through the Responses API. ([OpenAI Developers][1])

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
model="gpt-6-sol",
input="Explain why a database index can make a query faster."
)

print(response.output_text)

The important pieces are easy to separate. OpenAI() creates the client, model selects GPT-6 Sol, and input contains the task. The SDK's output_text helper gives you the generated text without making you manually walk through the response items.

At this point you have a working reasoning application, but the default configuration is not necessarily the right configuration for every workload. A short classification or formatting task does not need the same reasoning budget as a difficult debugging task.

Control reasoning effort instead of treating every request the same

GPT-6 Sol lets the application choose how much reasoning effort to request. OpenAI describes lower effort as a way to favor speed and lower token usage, while higher effort gives the model more room for complex work. The setting is therefore a workload control, not a quality switch that should simply be pushed to the maximum. ([OpenAI Developers][1])

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
model="gpt-6-sol",
reasoning={"effort": "high"},
input=(
"Review this algorithm conceptually. "
"Identify edge cases, explain the likely failure points, "
"and suggest a safer approach."
)
)

print(response.output_text)

For routine requests, low or medium can reduce unnecessary work. For a difficult debugging or planning task, high or another supported higher setting may be appropriate. Measure your own workload rather than assuming that the highest setting is automatically the right choice. OpenAI's deployment guidance specifically recommends comparing task success, latency, token usage, and cost on representative workloads before changing production settings. ([OpenAI Developers][2])

Use GPT-6 Sol when the answer needs more than one obvious step

A useful test for a reasoning model is a task where the application benefits from intermediate analysis but does not yet require external tools. For example, suppose an application receives a proposed database migration and needs to identify risks before a developer approves it. That is different from asking for a short definition because the model must consider several constraints and connect them before producing the answer.

from openai import OpenAI

client = OpenAI()

migration = """
Add an email column to a large users table.
The table receives continuous writes.
Existing rows may contain duplicate email addresses.
The application currently allows users without email addresses.
"""

response = client.responses.create(
model="gpt-6-sol",
reasoning={"effort": "high"},
input=[
{
"role": "system",
"content": (
"Review database migration plans. "
"Identify operational risks and propose a safe sequence."
),
},
{
"role": "user",
"content": migration,
},
],
)

print(response.output_text)

The point of this example is not that GPT-6 Sol should approve a migration without human review. The useful pattern is that the application gives the model a defined job, enough context to reason about the job, and an effort level appropriate to the complexity. If you later turn this workflow into a larger AI agent, the same separation between task instructions, input data, and model configuration becomes even more useful.

Turn free-form answers into application-ready data

Many AI applications fail at the boundary between generated language and ordinary software. A model may produce a perfectly understandable paragraph, while your program needs predictable fields such as severity, explanation, and recommended action. Structured Outputs addresses this by constraining the response to a schema rather than asking the model to imitate a format with prompting alone. OpenAI's current Responses API places this configuration under text.format, and its Python SDK can parse a defined Pydantic model directly. ([OpenAI Developers][3])

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI()

class ReviewResult(BaseModel):
severity: str
explanation: str
recommendation: str

response = client.responses.parse(
model="gpt-6-sol",
reasoning={"effort": "medium"},
input=(
"A production database migration adds a unique email constraint "
"to an existing table containing possible duplicates. "
"Assess the risk and recommend what should happen first."
),
text_format=ReviewResult,
)

result = response.output_parsed

print(result.severity)
print(result.explanation)
print(result.recommendation)

This changes the programming model considerably. Instead of parsing an unpredictable block of generated text yourself, your application receives a typed object that matches the schema when the request succeeds. OpenAI notes that Structured Outputs can still encounter edge cases such as refusals or incomplete responses, so production code should handle those cases instead of assuming every request will produce a complete object. ([OpenAI Developers][3])

Keep tool calling on the Responses path

The next step is connecting GPT-6 Sol to something your application can actually do. A tool is a function your application exposes to the model, such as looking up an order, querying a permitted database, or calculating a value. The model does not execute your server-side function itself; it requests a tool call, your application performs the operation, and the result is returned to the model.

This is where the Responses API becomes particularly useful. OpenAI lists function calling and built-in tools among GPT-6 Sol's supported capabilities, and its migration guidance describes tool calls and tool outputs as separate response items connected through a call identifier. ([OpenAI Developers][1])

If your application already has an dynamic brief workflow, the same pattern can be used to turn a generated plan into structured operations instead of asking the model to invent the final application state as plain text. The important boundary is that your code remains responsible for executing the requested operation and validating its arguments.

Know what GPT-6 Sol costs before moving a workload

Standard GPT-6 Sol pricing is currently $2 per million input tokens and $10 per million output tokens for prompts within the standard short-context pricing range. Cached input is priced at $0.20 per million tokens, while cache writes are $2.50 per million tokens. OpenAI also lists higher rates for requests that cross its long-context threshold, so the headline input price should not be treated as the cost of every possible request. ([OpenAI Developers][4])

The model's 1.05 million-token context window is useful when an application needs to supply a large amount of material, but a large context window does not mean every request should contain a large prompt. More input still means more tokens to process, and the pricing model distinguishes shorter and longer contexts. The practical approach is to measure the amount of information your application actually sends and the number of output tokens it needs, then compare that cost with the result you get.

Check the response before treating the integration as finished

A successful API call is only the first test. Run several representative prompts and check whether the model produces the type of answer your application expects, how long requests take, how many tokens they consume, and how often your structured output or tool workflow needs error handling. OpenAI's deployment guidance recommends representative evaluations before changing models or reasoning settings rather than relying on a single successful request. ([OpenAI Developers][2])

For a reasoning application, also test the boundaries. Give it a straightforward task, a task with missing information, a difficult task, and an input that should be rejected or handled cautiously. If you use Structured Outputs, test refusal and incomplete-response paths as well as successful parsing. That small test set will tell you much more about whether your integration is ready than one impressive demonstration.

What to build after the basic request works

Once the basic GPT-6 Sol call is stable, the natural progression is to add one capability at a time: structured output first, then a narrowly defined application function, then persistent application state if the workflow needs it. This keeps failures understandable because you know which layer introduced them. GPT-6 Sol supports a broad set of Responses API capabilities, including function calling, web search, file search, code execution, computer use, and other tools, but a production application does not need all of them simply because the model supports them. ([OpenAI Developers][1])

The model was released on September 22, so its production behavior and developer guidance will continue to be worth checking as the platform evolves. For now, the core integration is straightforward: select gpt-6-sol, use the Responses API for tool-oriented applications, choose reasoning effort according to the workload, and validate the actual responses your software receives rather than judging the integration from the model name alone. ([OpenAI Developers][5])