OpenAI API

Use the OpenAI API via direct curl calls to access GPT models, DALL-E image generation, Whisper transcription, embeddings, and text-to-speech.

Official docs: https://platform.openai.com/docs/api-reference

When to Use

Use this skill when you need to:

Chat completions with GPT-4o, GPT-4, or GPT-3.5 models Image generation with DALL-E 3 Audio transcription with Whisper Text-to-speech audio generation Text embeddings for semantic search and RAG Vision tasks (analyze images with GPT-4o) Prerequisites Sign up at OpenAI Platform and create an account Go to API Keys and generate a new secret key Add billing information and set usage limits export OPENAI_API_KEY="sk-..."

Pricing (as of 2025) Model Input (per 1M tokens) Output (per 1M tokens) GPT-4o $2.50 $10.00 GPT-4o-mini $0.15 $0.60 GPT-4 Turbo $10.00 $30.00 text-embedding-3-small $0.02 - text-embedding-3-large $0.13 - Rate Limits

Rate limits vary by tier (based on usage history). Check your limits at Platform Settings.

Important: When using $VAR in a command that pipes to another command, wrap the command containing $VAR in bash -c '...'. Due to a Claude Code bug, environment variables are silently cleared when pipes are used directly.

bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"' | jq .

How to Use

All examples below assume you have OPENAI_API_KEY set.

Base URL: https://api.openai.com/v1

Basic Chat Completion

Send a simple chat message:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Hello, who are you?"}] }

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.content'

Available models:

gpt-4o: Latest flagship model (128K context) gpt-4o-mini: Fast and affordable (128K context) gpt-4-turbo: Previous generation (128K context) gpt-3.5-turbo: Legacy model (16K context) o1: Reasoning model for complex tasks o1-mini: Smaller reasoning model 2. Chat with System Prompt

Use a system message to set behavior:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [ {"role": "system", "content": "You are a helpful assistant that responds in JSON format."}, {"role": "user", "content": "List 3 programming languages with their main use cases."} ] }

Then run:

Streaming Response

Get real-time token-by-token output:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Write a haiku about programming."}], "stream": true }

Then run:

curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json

Streaming returns Server-Sent Events (SSE) with delta chunks.

JSON Mode

Force the model to return valid JSON:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [ {"role": "system", "content": "Return JSON only."}, {"role": "user", "content": "Give me info about Paris: name, country, population."} ], "response_format": {"type": "json_object"} }

Then run:

Vision (Image Analysis)

Analyze an image with GPT-4o:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "What is in this image?"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"}} ] } ], "max_tokens": 300 }

Then run:

Function Calling (Tools)

Define functions the model can call:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "What is the weather in Tokyo?"}], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get current weather for a location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "City name"} }, "required": ["location"] } } } ] }

Then run:

Generate Embeddings

Create vector embeddings for text:

Write to /tmp/openai_request.json:

{ "model": "text-embedding-3-small", "input": "The quick brown fox jumps over the lazy dog." }

Then run:

bash -c 'curl -s "https://api.openai.com/v1/embeddings" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.data[0].embedding[:5]'

This extracts the first 5 dimensions of the embedding vector.

Embedding models:

text-embedding-3-small: 1536 dimensions, fastest text-embedding-3-large: 3072 dimensions, most capable 8. Generate Image (DALL-E 3)

Create an image from text:

Write to /tmp/openai_request.json:

{ "model": "dall-e-3", "prompt": "A white cat sitting on a windowsill, digital art", "n": 1, "size": "1024x1024" }

Then run:

bash -c 'curl -s "https://api.openai.com/v1/images/generations" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.data[0].url'

Parameters:

size: 1024x1024, 1792x1024, or 1024x1792 quality: standard or hd style: vivid or natural 9. Audio Transcription (Whisper)

Transcribe audio to text:

bash -c 'curl -s "https://api.openai.com/v1/audio/transcriptions" -H "Authorization: Bearer ${OPENAI_API_KEY}" -F "file=@audio.mp3" -F "model=whisper-1"' | jq '.text'

Supports: mp3, mp4, mpeg, mpga, m4a, wav, webm (max 25MB).

Text-to-Speech

Generate audio from text:

Write to /tmp/openai_request.json:

{ "model": "tts-1", "input": "Hello! This is a test of OpenAI text to speech.", "voice": "alloy" }

Then run:

curl -s "https://api.openai.com/v1/audio/speech" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json --output speech.mp3

Voices: alloy, echo, fable, onyx, nova, shimmer

Models: tts-1 (fast), tts-1-hd (high quality)

List Available Models

Get all available models:

bash -c 'curl -s "https://api.openai.com/v1/models" -H "Authorization: Bearer ${OPENAI_API_KEY}"' | jq -r '.data[].id' | sort | head -20

Check Token Usage

Extract usage from response:

Write to /tmp/openai_request.json:

{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Hi!"}] }

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.usage'

This returns token counts for both input and output.

Response includes:

prompt_tokens: Input token count completion_tokens: Output token count total_tokens: Sum of both Guidelines Choose the right model: Use gpt-4o-mini for most tasks, gpt-4o for complex reasoning, o1 for advanced math/coding Set max_tokens: Prevent runaway generation and control costs Use streaming for long responses: Better UX for real-time applications JSON mode requires system prompt: Include JSON instructions when using response_format Vision requires gpt-4o models: Only gpt-4o and gpt-4o-mini support image input Batch similar requests: Use embeddings API batch input for efficiency Monitor usage: Check dashboard regularly to avoid unexpected charges

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