--- title: Chat Completions product: vercel url: /docs/ai-gateway/sdks-and-apis/openai-chat-completions/chat-completions type: reference prerequisites: - /docs/ai-gateway/sdks-and-apis/openai-chat-completions - /docs/ai-gateway/sdks-and-apis related: - /docs/ai-gateway/sdks-and-apis/openai-chat-completions/advanced - /docs/ai-gateway/sdks-and-apis/openai-chat-completions/structured-outputs summary: Create chat completions using the Chat Completions API with support for streaming, image attachments, and PDF documents. install_vercel_plugin: npx plugins add vercel/vercel-plugin --- # Chat Completions Create chat completions using various AI models available through the AI Gateway. Endpoint ``` POST /chat/completions ``` ### Basic chat completion Create a non-streaming chat completion. Example request #### TypeScript ```typescript filename="chat-completion.ts" import OpenAI from 'openai'; const apiKey = process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN; const openai = new OpenAI({ apiKey, baseURL: 'https://ai-gateway.vercel.sh/v1', }); const completion = await openai.chat.completions.create({ model: 'anthropic/claude-opus-4.6', messages: [ { role: 'user', content: 'Write a one-sentence bedtime story about a unicorn.', }, ], stream: false, }); console.log('Assistant:', completion.choices[0].message.content); console.log('Tokens used:', completion.usage); ``` #### Python ```python filename="chat-completion.py" import os from openai import OpenAI api_key = os.getenv('AI_GATEWAY_API_KEY') or os.getenv('VERCEL_OIDC_TOKEN') client = OpenAI( api_key=api_key, base_url='https://ai-gateway.vercel.sh/v1' ) completion = client.chat.completions.create( model='anthropic/claude-opus-4.6', messages=[ { 'role': 'user', 'content': 'Write a one-sentence bedtime story about a unicorn.' } ], stream=False, ) print('Assistant:', completion.choices[0].message.content) print('Tokens used:', completion.usage) ``` Response format ```json { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "anthropic/claude-opus-4.6", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Once upon a time, a gentle unicorn with a shimmering silver mane danced through moonlit clouds, sprinkling stardust dreams upon sleeping children below." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 15, "completion_tokens": 28, "total_tokens": 43 } } ``` ### Streaming chat completion Create a streaming chat completion that streams tokens as they are generated. Example request #### TypeScript ```typescript filename="streaming-chat.ts" import OpenAI from 'openai'; const apiKey = process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN; const openai = new OpenAI({ apiKey, baseURL: 'https://ai-gateway.vercel.sh/v1', }); const stream = await openai.chat.completions.create({ model: 'anthropic/claude-opus-4.6', messages: [ { role: 'user', content: 'Write a one-sentence bedtime story about a unicorn.', }, ], stream: true, }); for await (const chunk of stream) { const content = chunk.choices[0]?.delta?.content; if (content) { process.stdout.write(content); } } ``` #### Python ```python filename="streaming-chat.py" import os from openai import OpenAI api_key = os.getenv('AI_GATEWAY_API_KEY') or os.getenv('VERCEL_OIDC_TOKEN') client = OpenAI( api_key=api_key, base_url='https://ai-gateway.vercel.sh/v1' ) stream = client.chat.completions.create( model='anthropic/claude-opus-4.6', messages=[ { 'role': 'user', 'content': 'Write a one-sentence bedtime story about a unicorn.' } ], stream=True, ) for chunk in stream: content = chunk.choices[0].delta.content if content: print(content, end='', flush=True) ``` #### Streaming response format Streaming responses are sent as [Server-Sent Events (SSE)](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events), a web standard for real-time data streaming over HTTP. Each event contains a JSON object with the partial response data. The response format follows the OpenAI streaming specification: ```http data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"anthropic/claude-opus-4.6","choices":[{"index":0,"delta":{"content":"Once"},"finish_reason":null}]} data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"anthropic/claude-opus-4.6","choices":[{"index":0,"delta":{"content":" upon"},"finish_reason":null}]} data: [DONE] ``` **Key characteristics:** - Each line starts with `data:` followed by JSON - Content is delivered incrementally in the `delta.content` field - The stream ends with `data: [DONE]` - Empty lines separate events **SSE Parsing Libraries:** If you're building custom SSE parsing (instead of using the OpenAI SDK), these libraries can help: - **JavaScript/TypeScript**: [`eventsource-parser`](https://www.npmjs.com/package/eventsource-parser) - Robust SSE parsing with support for partial events - **Python**: [`httpx-sse`](https://pypi.org/project/httpx-sse/) - SSE support for HTTPX, or [`sseclient-py`](https://pypi.org/project/sseclient-py/) for requests For more details about the SSE specification, see the [W3C specification](https://html.spec.whatwg.org/multipage/server-sent-events.html). ### Image attachments Send images as part of your chat completion request. Example request #### TypeScript ```typescript filename="image-analysis.ts" import fs from 'node:fs'; import OpenAI from 'openai'; const apiKey = process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN; const openai = new OpenAI({ apiKey, baseURL: 'https://ai-gateway.vercel.sh/v1', }); // Read the image file as base64 const imageBuffer = fs.readFileSync('./path/to/image.png'); const imageBase64 = imageBuffer.toString('base64'); const completion = await openai.chat.completions.create({ model: 'anthropic/claude-opus-4.6', messages: [ { role: 'user', content: [ { type: 'text', text: 'Describe this image in detail.' }, { type: 'image_url', image_url: { url: `data:image/png;base64,${imageBase64}`, detail: 'auto', }, }, ], }, ], stream: false, }); console.log('Assistant:', completion.choices[0].message.content); console.log('Tokens used:', completion.usage); ``` #### Python ```python filename="image-analysis.py" import os import base64 from openai import OpenAI api_key = os.getenv('AI_GATEWAY_API_KEY') or os.getenv('VERCEL_OIDC_TOKEN') client = OpenAI( api_key=api_key, base_url='https://ai-gateway.vercel.sh/v1' ) # Read the image file as base64 with open('./path/to/image.png', 'rb') as image_file: image_base64 = base64.b64encode(image_file.read()).decode('utf-8') completion = client.chat.completions.create( model='anthropic/claude-opus-4.6', messages=[ { 'role': 'user', 'content': [ {'type': 'text', 'text': 'Describe this image in detail.'}, { 'type': 'image_url', 'image_url': { 'url': f'data:image/png;base64,{image_base64}', 'detail': 'auto' } } ] } ], stream=False, ) print('Assistant:', completion.choices[0].message.content) print('Tokens used:', completion.usage) ``` ### PDF attachments Send PDF documents as part of your chat completion request. Example request #### TypeScript ```typescript filename="pdf-analysis.ts" import fs from 'node:fs'; import OpenAI from 'openai'; const apiKey = process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN; const openai = new OpenAI({ apiKey, baseURL: 'https://ai-gateway.vercel.sh/v1', }); // Read the PDF file as base64 const pdfBuffer = fs.readFileSync('./path/to/document.pdf'); const pdfBase64 = pdfBuffer.toString('base64'); const completion = await openai.chat.completions.create({ model: 'anthropic/claude-opus-4.6', messages: [ { role: 'user', content: [ { type: 'text', text: 'What is the main topic of this document? Please summarize the key points.', }, { type: 'file', file: { data: pdfBase64, media_type: 'application/pdf', filename: 'document.pdf', }, }, ], }, ], stream: false, }); console.log('Assistant:', completion.choices[0].message.content); console.log('Tokens used:', completion.usage); ``` #### Python ```python filename="pdf-analysis.py" import os import base64 from openai import OpenAI api_key = os.getenv('AI_GATEWAY_API_KEY') or os.getenv('VERCEL_OIDC_TOKEN') client = OpenAI( api_key=api_key, base_url='https://ai-gateway.vercel.sh/v1' ) # Read the PDF file as base64 with open('./path/to/document.pdf', 'rb') as pdf_file: pdf_base64 = base64.b64encode(pdf_file.read()).decode('utf-8') completion = client.chat.completions.create( model='anthropic/claude-opus-4.6', messages=[ { 'role': 'user', 'content': [ { 'type': 'text', 'text': 'What is the main topic of this document? Please summarize the key points.' }, { 'type': 'file', 'file': { 'data': pdf_base64, 'media_type': 'application/pdf', 'filename': 'document.pdf' } } ] } ], stream=False, ) print('Assistant:', completion.choices[0].message.content) print('Tokens used:', completion.usage) ``` ### Parameters The chat completions endpoint supports the following parameters: #### Required parameters - `model` (string): The model to use for the completion (e.g., `anthropic/claude-opus-4.6`) - `messages` (array): Array of message objects with `role` and `content` fields #### Optional parameters - `stream` (boolean): Whether to stream the response. Defaults to `false` - `temperature` (number): Controls randomness in the output. Range: 0-2 - `max_tokens` (integer): Maximum number of tokens to generate - `top_p` (number): Nucleus sampling parameter. Range: 0-1 - `frequency_penalty` (number): Penalty for frequent tokens. Range: -2 to 2 - `presence_penalty` (number): Penalty for present tokens. Range: -2 to 2 - `stop` (string or array): Stop sequences for the generation - `tools` (array): Array of tool definitions for function calling - `tool_choice` (string or object): Controls which tools are called (`auto`, `none`, or specific function) - `providerOptions` (object): [Provider routing and configuration options](/docs/ai-gateway/sdks-and-apis/openai-chat-completions/advanced#provider-options) - `response_format` (object): Controls the format of the model's response - For OpenAI standard format: `{ type: "json_schema", json_schema: { name, schema, strict?, description? } }` - For legacy format: `{ type: "json", schema?, name?, description? }` - For plain text: `{ type: "text" }` - See [Structured outputs](/docs/ai-gateway/sdks-and-apis/openai-chat-completions/structured-outputs) for detailed examples ### Message format Messages support different content types: #### Text messages ```json { "role": "user", "content": "Hello, how are you?" } ``` #### Multimodal messages ```json { "role": "user", "content": [ { "type": "text", "text": "What's in this image?" }, { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD..." } } ] } ``` #### File messages ```json { "role": "user", "content": [ { "type": "text", "text": "Summarize this document" }, { "type": "file", "file": { "data": "JVBERi0xLjQKJcfsj6IKNSAwIG9iago8PAovVHlwZSAvUGFnZQo...", "media_type": "application/pdf", "filename": "document.pdf" } } ] } ``` --- [View full sitemap](/docs/sitemap)