feat: add responses api, websocket support, and fast mode
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Game_Time
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README.md
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README.md
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<div align="center">
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<h1>ChatMock
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<div align="center">
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<a href="https://github.com/RayBytes/ChatMock/stargazers"><img src="https://img.shields.io/github/stars/RayBytes/ChatMock" alt="Stars Badge"/></a>
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<a href="https://github.com/RayBytes/ChatMock/network/members"><img src="https://img.shields.io/github/forks/RayBytes/ChatMock" alt="Forks Badge"/></a>
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<a href="https://github.com/RayBytes/ChatMock/pulls"><img src="https://img.shields.io/github/issues-pr/RayBytes/ChatMock" alt="Pull Requests Badge"/></a>
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<a href="https://github.com/RayBytes/ChatMock/issues"><img src="https://img.shields.io/github/issues/RayBytes/ChatMock" alt="Issues Badge"/></a>
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<a href="https://github.com/RayBytes/ChatMock/graphs/contributors"><img alt="GitHub contributors" src="https://img.shields.io/github/contributors/RayBytes/ChatMock?color=2b9348"></a>
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<a href="https://github.com/RayBytes/ChatMock/blob/master/LICENSE"><img src="https://img.shields.io/github/license/RayBytes/ChatMock?color=2b9348" alt="License Badge"/></a>
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</div>
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</h1>
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<p><b>OpenAI & Ollama compatible API powered by your ChatGPT plan.</b></p>
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<p>Use your ChatGPT Plus/Pro account to call OpenAI models from code or alternate chat UIs.</p>
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<br>
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# ChatMock
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**Allows Codex to work in your favourite chat apps and coding tools.**
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[](https://pypi.org/project/chatmock/)
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[](https://pypi.org/project/chatmock/)
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[](LICENSE)
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[](https://github.com/RayBytes/ChatMock/stargazers)
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[](https://github.com/RayBytes/ChatMock/commits/main)
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[](https://github.com/RayBytes/ChatMock/issues)
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<br>
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</div>
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## What It Does
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<br>
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ChatMock runs a local server that creates an OpenAI/Ollama compatible API, and requests are then fulfilled using your authenticated ChatGPT login with the oauth client of Codex, OpenAI's coding CLI tool. This allows you to use GPT-5, GPT-5-Codex, and other models right through your OpenAI account, without requiring an api key. You are then able to use it in other chat apps or other coding tools. <br>
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This does require a paid ChatGPT account.
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## Quickstart
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### Homebrew
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## Install
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#### Homebrew
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```bash
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brew tap RayBytes/chatmock
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brew install chatmock
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```
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### CLI
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#### pipx / pip
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```bash
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pipx install chatmock
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```
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### GUI
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#### GUI
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Download from [releases](https://github.com/RayBytes/ChatMock/releases) (macOS & Windows)
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If you're on **macOS** or **Windows**, you can download the GUI app from the [GitHub releases](https://github.com/RayBytes/ChatMock/releases).
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#### Docker
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See [DOCKER.md](DOCKER.md)
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### Python
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If you wish to just simply run this as a python flask server, you are also freely welcome too.
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<br>
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Clone or download this repository, then cd into the project directory. Then follow the instrunctions listed below.
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1. Sign in with your ChatGPT account and follow the prompts
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```bash
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python chatmock.py login
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```
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You can make sure this worked by running `python chatmock.py info`
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2. After the login completes successfully, you can just simply start the local server
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## Getting Started
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```bash
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python chatmock.py serve
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# 1. Sign in with your ChatGPT account
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chatmock login
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# 2. Start the server
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chatmock serve
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```
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Then, you can simply use the address and port as the baseURL as you require (http://127.0.0.1:8000 by default)
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**Reminder:** When setting a baseURL in other applications, make you sure you include /v1/ at the end of the URL if you're using this as a OpenAI compatible endpoint (e.g http://127.0.0.1:8000/v1)
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The server runs at `http://127.0.0.1:8000` by default. Use `http://127.0.0.1:8000/v1` as your base URL for OpenAI-compatible apps.
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### Docker
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<br>
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Read [the docker instrunctions here](https://github.com/RayBytes/ChatMock/blob/main/DOCKER.md)
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## Usage
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# Examples
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### Python
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<details open>
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<summary><b>Python</b></summary>
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://127.0.0.1:8000/v1",
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api_key="key" # ignored
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api_key="anything" # not checked
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)
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resp = client.chat.completions.create(
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response = client.chat.completions.create(
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model="gpt-5.4",
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messages=[{"role": "user", "content": "hello world"}]
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messages=[{"role": "user", "content": "hello"}]
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)
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print(resp.choices[0].message.content)
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print(response.choices[0].message.content)
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```
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### curl
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</details>
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<details>
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<summary><b>cURL</b></summary>
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```bash
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curl http://127.0.0.1:8000/v1/chat/completions \
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-H "Authorization: Bearer key" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-5.4",
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"messages": [{"role":"user","content":"hello world"}]
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"messages": [{"role": "user", "content": "hello"}]
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}'
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```
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# What's supported
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</details>
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- Tool/Function calling
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- Vision/Image understanding
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- Thinking summaries (through thinking tags)
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- Thinking effort
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<br>
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## Notes & Limits
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## Supported Models
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- Requires an active, paid ChatGPT account.
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- Some context length might be taken up by internal instructions (but they dont seem to degrade the model)
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- Use responsibly and at your own risk. This project is not affiliated with OpenAI, and is a educational exercise.
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# Supported models
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- `gpt-5.4`
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- `gpt-5.4-mini`
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- `gpt-5.2`
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- `gpt-5.1`
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- `gpt-5`
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- `gpt-5.3-codex`
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- `gpt-5-codex`
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- `gpt-5.3-codex-spark`
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- `gpt-5.2-codex`
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- `gpt-5-codex`
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- `gpt-5.1-codex`
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- `gpt-5.1-codex-max`
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- `gpt-5.1-codex-mini`
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- `codex-mini`
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# Customisation / Configuration
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<br>
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### Thinking effort
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## Features
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- `--reasoning-effort` (choice of none,minimal,low,medium,high,xhigh)<br>
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GPT-5 has a configurable amount of "effort" it can put into thinking, which may cause it to take more time for a response to return, but may overall give a smarter answer. Applying this parameter after `serve` forces the server to use this reasoning effort by default, unless overrided by the API request with a different effort set. The default reasoning effort without setting this parameter is `medium`.<br>
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The `gpt-5.1` family (including codex) supports `low`, `medium`, and `high` while `gpt-5.1-codex-max` adds `xhigh`. The `gpt-5.2` and `gpt-5.3` families (including codex) support `low`, `medium`, `high`, and `xhigh`. `gpt-5.4` supports `none`, `low`, `medium`, `high`, and `xhigh`.
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- Tool / function calling
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- Vision / image input
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- Thinking summaries (via think tags)
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- Configurable thinking effort
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- Fast mode for supported models
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- Web search tool
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- OpenAI-compatible `/v1/responses` (HTTP + WebSocket)
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- Ollama-compatible endpoints
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- Reasoning effort exposed as separate models (optional)
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### Thinking summaries
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<br>
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- `--reasoning-summary` (choice of auto,concise,detailed,none)<br>
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Models like GPT-5 do not return raw thinking content, but instead return thinking summaries. These can also be customised by you.
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## Configuration
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### OpenAI Tools
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All flags go after `chatmock serve`. These can also be set as environment variables.
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- `--enable-web-search`<br>
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You can also access OpenAI tools through this project. Currently, only web search is available.
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You can enable it by starting the server with this parameter, which will allow OpenAI to determine when a request requires a web search, or you can use the following parameters during a request to the API to enable web search:
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<br><br>
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`responses_tools`: supports `[{"type":"web_search"}]` / `{ "type": "web_search_preview" }`<br>
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`responses_tool_choice`: `"auto"` or `"none"`
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| Flag | Env var | Options | Default | Description |
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|------|---------|---------|---------|-------------|
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| `--reasoning-effort` | `CHATGPT_LOCAL_REASONING_EFFORT` | none, minimal, low, medium, high, xhigh | medium | How hard the model thinks |
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| `--reasoning-summary` | `CHATGPT_LOCAL_REASONING_SUMMARY` | auto, concise, detailed, none | auto | Thinking summary verbosity |
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| `--reasoning-compat` | `CHATGPT_LOCAL_REASONING_COMPAT` | legacy, o3, think-tags | think-tags | How reasoning is returned to the client |
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| `--fast-mode` | `CHATGPT_LOCAL_FAST_MODE` | true/false | false | Priority processing for supported models |
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| `--enable-web-search` | `CHATGPT_LOCAL_ENABLE_WEB_SEARCH` | true/false | false | Allow the model to search the web |
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| `--expose-reasoning-models` | `CHATGPT_LOCAL_EXPOSE_REASONING_MODELS` | true/false | false | List each reasoning level as its own model |
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<details>
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<summary><b>Web search in a request</b></summary>
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#### Example usage
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```json
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{
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"model": "gpt-5.4",
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"messages": [{"role":"user","content":"Find current METAR rules"}],
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"stream": true,
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"messages": [{"role": "user", "content": "latest news on ..."}],
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"responses_tools": [{"type": "web_search"}],
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"responses_tool_choice": "auto"
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}
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```
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### Expose reasoning models
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</details>
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- `--expose-reasoning-models`<br>
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If your preferred app doesn’t support selecting reasoning effort, or you just want a simpler approach, this parameter exposes each reasoning level as a separate, queryable model. Each reasoning level also appears individually under /v1/models, so model pickers in your favorite chat apps will list all reasoning options as distinct models you can switch between.
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<details>
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<summary><b>Fast mode in a request</b></summary>
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```json
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{
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"model": "gpt-5.4",
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"input": "summarize this",
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"fast_mode": true
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}
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```
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</details>
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<br>
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## Notes
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If you wish to have the fastest responses, I'd recommend setting `--reasoning-effort` to low, and `--reasoning-summary` to none. <br>
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All parameters and choices can be seen by sending `python chatmock.py serve --h`<br>
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The context size of this route is also larger than what you get access to in the regular ChatGPT app.<br>
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When the model returns a thinking summary, the model will send back thinking tags to make it compatible with chat apps. **If you don't like this behavior, you can instead set `--reasoning-compat` to legacy, and reasoning will be set in the reasoning tag instead of being returned in the actual response text.**
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Use responsibly and at your own risk. This project is not affiliated with OpenAI.
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<br>
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## Star History
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