Why does the API differ from the web app?

It’s the same model. Official web apps wrap it in a hidden system prompt, web search, memory, and tuned settings, while the API gives you only the model. LaoZhang API forwards requests unchanged, without injecting prompts, so it behaves like the official API.

What the web app adds

Capability	Official web app	Calling the API
System prompt	Injected every turn, not published	None until you write one
Web search	Triggered automatically	Off by default
Math and code execution	Built-in sandbox	You add a tool
Memory	History saved for you	Stateless; send prior turns each time
Long conversations	Summarized and trimmed for you	You truncate or summarize
Settings and reasoning effort	Tuned for you; some apps switch models automatically	Defaults apply
Formatting	Rendered Markdown, citations, and code highlighting	Plain text or JSON

Common differences

It doesn’t know recent news. The model’s knowledge stops at its training cutoff; the web app fills the gap with web search. With the API, call a search service yourself and put the results in context.
It gets arithmetic or word counts wrong. The web app quietly runs code for calculations, while the API model works it out in its head. Give it a code-execution tool, or ask it to show its working.
Answers are shorter or less structured. The web app’s system prompt sets structure and length. Put the style you want in your own system prompt.
It forgets what you said earlier. Each API request is a new conversation. Send earlier turns in messages, and use prompt caching to cut the cost of the repeated prefix.
Answers vary each time. That’s sampling randomness. Lower temperature or constrain the output format in the prompt.
Reasoning feels shallower. Web apps often default to higher reasoning effort. Raise reasoning_effort or the equivalent and leave enough output budget; see max_tokens.

The model names the wrong model or version

Asking a model “Which model are you?” through the API often gets the version wrong. That doesn’t mean you called the wrong model:

A model is named after training finishes, so it never learned its own name; its training data only contains earlier model names, so it guesses one of those.
The web app gets it right because its hidden system prompt tells the model who it is.
The model parameter in your request selects the model; the model itself can’t read that field.

If you need it to identify itself correctly, say so in the system prompt:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LAOZHANG_API_KEY"],
    base_url="https://api.laozhang.ai/v1",
)

response = client.chat.completions.create(
    model="gpt-6-sol",
    messages=[
        {"role": "system", "content": "You are GPT-6 Sol, a model made by OpenAI."},
        {"role": "user", "content": "Which model are you?"},
    ],
)
print(response.choices[0].message.content)

Confirm which model you called

Don’t rely on the model’s own answer. Check:

the model field in the response JSON;
the model name and charge for the request in call logs.

Get closer to the web app with the API

Write a system prompt

Set the role, tone, output format, length, and limits. This step makes the biggest difference.

Keep the conversation history

Append each user message and model reply to messages. As the conversation grows, summarize it or keep only recent turns plus key facts.

Add tools you need

Add search for current information, code execution for exact calculations, and retrieval for internal documents. Tool-call formats are covered in Chat Completions and Claude protocol.

Set parameters explicitly

Don’t rely on defaults; set temperature, the output limit, and reasoning effort.

If you’d rather not build this yourself, use a third-party client that already has these features. Set its API URL to https://api.laozhang.ai/v1 and enter your LaoZhang API key; see Invalid API key or 404 for setup tips. The API can’t reproduce a web app exactly: vendors don’t publish their system prompts, some web features have no API, and web apps keep running experiments and switching models. In return, the API puts the prompt, settings, and context in your hands, so results are reproducible, which is what you need when building a product.

​What the web app adds

​Common differences

​The model names the wrong model or version

​Confirm which model you called

​Get closer to the web app with the API

​Related pages

What the web app adds

Common differences

The model names the wrong model or version

Confirm which model you called

Get closer to the web app with the API

Related pages