--- title: "OpenAI" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/openai-1 source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:31:28.318Z license: CC-BY-4.0 attribution: "OpenAI — Claudary (https://claudary.paisolsolutions.com/skills/openai-1)" --- # OpenAI import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # OpenAI LiteLLM supports OpenAI Chat + Embedding calls. :::tip **We recommend using `litellm.responses()` / Responses API** for the latest OpenAI models (GPT-5, gpt-5-codex, o3-mini, etc.) ::: ### Required API Keys ```python import os os.environ["OPENAI_API_KEY"] = "your-api-key" ``` ### Usage ```python import os from litellm import completion os.environ["OPENAI_API_KEY"] = "your-api-key" # openai call response = completion( model = "gpt-4o", messages=[{ "content": "Hello, how are you?","role": "user"}] ) ``` :::info Metadata passthrough (preview) When `litellm.enable_preview_features = True`, LiteLLM forwards only the values inside `metadata` to OpenAI. ```python completion( model="gpt-4o", messages=[{"role": "user", "content": "hi"}], metadata= {"custom_meta_key": "value"}, ) ``` ::: ### Usage - LiteLLM Proxy Server Here's how to call OpenAI models with the LiteLLM Proxy Server ### 1. Save key in your environment ```bash export OPENAI_API_KEY="" ``` ### 2. Start the proxy ```yaml model_list: - model_name: gpt-3.5-turbo litellm_params: model: openai/gpt-3.5-turbo # The `openai/` prefix will call openai.chat.completions.create api_key: os.environ/OPENAI_API_KEY - model_name: gpt-3.5-turbo-instruct litellm_params: model: text-completion-openai/gpt-3.5-turbo-instruct # The `text-completion-openai/` prefix will call openai.completions.create api_key: os.environ/OPENAI_API_KEY ``` Use this to add all openai models with one API Key. **WARNING: This will not do any load balancing** This means requests to `gpt-4`, `gpt-3.5-turbo` , `gpt-4-turbo-preview` will all go through this route ```yaml model_list: - model_name: "*" # all requests where model not in your config go to this deployment litellm_params: model: openai/* # set `openai/` to use the openai route api_key: os.environ/OPENAI_API_KEY ``` ```bash $ litellm --model gpt-3.5-turbo # Server running on http://0.0.0.0:4000 ``` ### 3. Test it ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data ' { "model": "gpt-3.5-turbo", "messages": [ { "role": "user", "content": "what llm are you" } ] } ' ``` ```python import openai client = openai.OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ]) print(response) ``` ```python from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) from langchain.schema import HumanMessage, SystemMessage chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy model = "gpt-3.5-turbo", temperature=0.1 ) messages = [ SystemMessage( content="You are a helpful assistant that im using to make a test request to." ), HumanMessage( content="test from litellm. tell me why it's amazing in 1 sentence" ), ] response = chat(messages) print(response) ``` ### Optional Keys - OpenAI Organization, OpenAI API Base ```python import os os.environ["OPENAI_ORGANIZATION"] = "your-org-id" # OPTIONAL os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL ``` ### OpenAI Chat Completion Models | Model Name | Function Call | |-----------------------|-----------------------------------------------------------------| | gpt-5 | `response = completion(model="gpt-5", messages=messages)` | | gpt-5-mini | `response = completion(model="gpt-5-mini", messages=messages)` | | gpt-5-nano | `response = completion(model="gpt-5-nano", messages=messages)` | | gpt-5-chat | `response = completion(model="gpt-5-chat", messages=messages)` | | gpt-5-chat-latest | `response = completion(model="gpt-5-chat-latest", messages=messages)` | | gpt-5-2025-08-07 | `response = completion(model="gpt-5-2025-08-07", messages=messages)` | | gpt-5-mini-2025-08-07 | `response = completion(model="gpt-5-mini-2025-08-07", messages=messages)` | | gpt-5-nano-2025-08-07 | `response = completion(model="gpt-5-nano-2025-08-07", messages=messages)` | | gpt-5-pro | `response = completion(model="gpt-5-pro", messages=messages)` | | gpt-5.2 | `response = completion(model="gpt-5.2", messages=messages)` | | gpt-5.2-2025-12-11 | `response = completion(model="gpt-5.2-2025-12-11", messages=messages)` | | gpt-5.2-chat-latest | `response = completion(model="gpt-5.2-chat-latest", messages=messages)` | | gpt-5.3-chat-latest | `response = completion(model="gpt-5.3-chat-latest", messages=messages)` | | gpt-5.4 | `response = completion(model="gpt-5.4", messages=messages)` | | gpt-5.4-2026-03-05 | `response = completion(model="gpt-5.4-2026-03-05", messages=messages)` | | gpt-5.2-pro | `response = completion(model="gpt-5.2-pro", messages=messages)` | | gpt-5.2-pro-2025-12-11 | `response = completion(model="gpt-5.2-pro-2025-12-11", messages=messages)` | | gpt-5.4-pro | `response = completion(model="gpt-5.4-pro", messages=messages)` | | gpt-5.4-pro-2026-03-05 | `response = completion(model="gpt-5.4-pro-2026-03-05", messages=messages)` | | gpt-5.1 | `response = completion(model="gpt-5.1", messages=messages)` | | gpt-5.1-codex | `response = completion(model="gpt-5.1-codex", messages=messages)` | | gpt-5.1-codex-mini | `response = completion(model="gpt-5.1-codex-mini", messages=messages)` | | gpt-5.1-codex-max | `response = completion(model="gpt-5.1-codex-max", messages=messages)` | | gpt-4.1 | `response = completion(model="gpt-4.1", messages=messages)` | | gpt-4.1-mini | `response = completion(model="gpt-4.1-mini", messages=messages)` | | gpt-4.1-nano | `response = completion(model="gpt-4.1-nano", messages=messages)` | | o4-mini | `response = completion(model="o4-mini", messages=messages)` | | o3-mini | `response = completion(model="o3-mini", messages=messages)` | | o3 | `response = completion(model="o3", messages=messages)` | | o1-mini | `response = completion(model="o1-mini", messages=messages)` | | o1-preview | `response = completion(model="o1-preview", messages=messages)` | | gpt-4o-mini | `response = completion(model="gpt-4o-mini", messages=messages)` | | gpt-4o-mini-2024-07-18 | `response = completion(model="gpt-4o-mini-2024-07-18", messages=messages)` | | gpt-4o | `response = completion(model="gpt-4o", messages=messages)` | | gpt-4o-2024-08-06 | `response = completion(model="gpt-4o-2024-08-06", messages=messages)` | | gpt-4o-2024-05-13 | `response = completion(model="gpt-4o-2024-05-13", messages=messages)` | | gpt-4-turbo | `response = completion(model="gpt-4-turbo", messages=messages)` | | gpt-4-turbo-preview | `response = completion(model="gpt-4-0125-preview", messages=messages)` | | gpt-4-0125-preview | `response = completion(model="gpt-4-0125-preview", messages=messages)` | | gpt-4-1106-preview | `response = completion(model="gpt-4-1106-preview", messages=messages)` | | gpt-3.5-turbo-1106 | `response = completion(model="gpt-3.5-turbo-1106", messages=messages)` | | gpt-3.5-turbo | `response = completion(model="gpt-3.5-turbo", messages=messages)` | | gpt-3.5-turbo-0301 | `response = completion(model="gpt-3.5-turbo-0301", messages=messages)` | | gpt-3.5-turbo-0613 | `response = completion(model="gpt-3.5-turbo-0613", messages=messages)` | | gpt-3.5-turbo-16k | `response = completion(model="gpt-3.5-turbo-16k", messages=messages)` | | gpt-3.5-turbo-16k-0613| `response = completion(model="gpt-3.5-turbo-16k-0613", messages=messages)` | | gpt-4 | `response = completion(model="gpt-4", messages=messages)` | | gpt-4-0314 | `response = completion(model="gpt-4-0314", messages=messages)` | | gpt-4-0613 | `response = completion(model="gpt-4-0613", messages=messages)` | | gpt-4-32k | `response = completion(model="gpt-4-32k", messages=messages)` | | gpt-4-32k-0314 | `response = completion(model="gpt-4-32k-0314", messages=messages)` | | gpt-4-32k-0613 | `response = completion(model="gpt-4-32k-0613", messages=messages)` | These also support the `OPENAI_BASE_URL` environment variable, which can be used to specify a custom API endpoint. ### OpenAI Web Search Models OpenAI has two ways to use web search, depending on the endpoint: | Approach | Endpoint | Models | How to enable | |----------|----------|--------|---------------| | **Search Models** | `/chat/completions` | `gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview` | Pass `web_search_options` parameter | | **Web Search Tool** | `/responses` | `gpt-5`, `gpt-4.1`, `gpt-4o`, and other regular models | Pass `web_search_preview` tool | ```python showLineNumbers from litellm import completion response = completion( model="openai/gpt-5-search-api", messages=[{"role": "user", "content": "What is the capital of France?"}], web_search_options={ "search_context_size": "medium" # Options: "low", "medium", "high" } ) ``` ```python showLineNumbers from litellm import responses response = responses( model="openai/gpt-5", input="What is the capital of France?", tools=[{ "type": "web_search_preview", "search_context_size": "low" }] ) ``` ```yaml model_list: # Search model for /chat/completions - model_name: gpt-5-search-api litellm_params: model: openai/gpt-5-search-api api_key: os.environ/OPENAI_API_KEY # Regular model for /responses with web_search_preview tool - model_name: gpt-5 litellm_params: model: openai/gpt-5 api_key: os.environ/OPENAI_API_KEY ``` For full details, see the [Web Search guide](../completion/web_search.md). ## OpenAI Vision Models | Model Name | Function Call | |-----------------------|-----------------------------------------------------------------| | gpt-4o | `response = completion(model="gpt-4o", messages=messages)` | | gpt-4-turbo | `response = completion(model="gpt-4-turbo", messages=messages)` | | gpt-4-vision-preview | `response = completion(model="gpt-4-vision-preview", messages=messages)` | #### Usage ```python import os from litellm import completion os.environ["OPENAI_API_KEY"] = "your-api-key" # openai call response = completion( model = "gpt-4-vision-preview", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What’s in this image?" }, { "type": "image_url", "image_url": { "url": "https://awsmp-logos.s3.amazonaws.com/seller-xw5kijmvmzasy/c233c9ade2ccb5491072ae232c814942.png" } } ] } ], ) ``` ## PDF File Parsing OpenAI has a new `file` message type that allows you to pass in a PDF file and have it parsed into a structured output. [Read more](https://platform.openai.com/docs/guides/pdf-files?api-mode=chat&lang=python) ```python import base64 from litellm import completion with open("draconomicon.pdf", "rb") as f: data = f.read() base64_string = base64.b64encode(data).decode("utf-8") completion = completion( model="gpt-4o", messages=[ { "role": "user", "content": [ { "type": "file", "file": { "filename": "draconomicon.pdf", "file_data": f"data:application/pdf;base64,{base64_string}", } }, { "type": "text", "text": "What is the first dragon in the book?", } ], }, ], ) print(completion.choices[0].message.content) ``` 1. Setup config.yaml ```yaml model_list: - model_name: openai-model litellm_params: model: gpt-4o api_key: os.environ/OPENAI_API_KEY ``` 2. Start the proxy ```bash litellm --config config.yaml ``` 3. Test it! ```bash curl -X POST 'http://0.0.0.0:4000/chat/completions' \\ -H 'Content-Type: application/json' \\ -H 'Authorization: Bearer sk-1234' \\ -d '{ "model": "openai-model", "messages": [ {"role": "user", "content": [ { "type": "file", "file": { "filename": "draconomicon.pdf", "file_data": f"data:application/pdf;base64,{base64_string}", } } ]} ] }' ``` ## OpenAI Fine Tuned Models | Model Name | Function Call | |---------------------------|-----------------------------------------------------------------| | fine tuned `gpt-4-0613` | `response = completion(model="ft:gpt-4-0613", messages=messages)` | | fine tuned `gpt-4o-2024-05-13` | `response = completion(model="ft:gpt-4o-2024-05-13", messages=messages)` | | fine tuned `gpt-3.5-turbo-0125` | `response = completion(model="ft:gpt-3.5-turbo-0125", messages=messages)` | | fine tuned `gpt-3.5-turbo-1106` | `response = completion(model="ft:gpt-3.5-turbo-1106", messages=messages)` | | fine tuned `gpt-3.5-turbo-0613` | `response = completion(model="ft:gpt-3.5-turbo-0613", messages=messages)` | ## [BETA] Route all .completions requests to Responses API (better quality) When enabled, LiteLLM sends OpenAI traffic from `litellm.completion()` and the proxy `/chat/completions` endpoint through the [Responses API](https://platform.openai.com/docs/api-reference/responses) instead of Chat Completions. That path generally matches Ope --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/openai-1) · https://claudary.paisolsolutions.com