A quick reference for making your first Claude API call, picking a model, and reading the response.
ANTHROPIC_API_KEY variable.client.messages.create() with a model, prompt, and max_tokens.stop_reason before reading content to handle responses safely.claude-opus-4-8 in production so a later model update never silently changes your app's behavior.max_tokens generously for long or open-ended replies, since Claude stops the moment it reaches that limit, even mid-sentence.max_tokens is required on every request — omitting it raises a validation error before Claude even sees your prompt.pip install anthropicInstalls the Python SDK from PyPI so you can start calling Claude.
pip install anthropicnpm install @anthropic-ai/sdkInstalls the TypeScript and Node SDK for JavaScript projects.
npm install @anthropic-ai/sdkANTHROPIC_API_KEYStores your API key as an environment variable instead of hard-coding it.
export ANTHROPIC_API_KEY="sk-ant-..."Anthropic()Creates a client that reads the API key from the environment automatically.
import anthropic
client = anthropic.Anthropic()messages.create()Sends a prompt to Claude with a model, max_tokens, and a messages array.
message = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
messages=[{"role": "user", "content": "Explain closures in one paragraph."}]
)system parameterGives Claude a persistent role or persona for the whole conversation.
message = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
system="You are a concise technical writer.",
messages=[{"role": "user", "content": "Explain closures."}]
)max_tokensCaps the response length; short answers need less, long docs need more.
# Short answers: 256-512 | Long docs: 4096-8192 | Max: 128000content[0].textReads the text of Claude's reply from the response object.
print(message.content[0].text)stop_reasonExplains why generation stopped: end_turn, max_tokens, tool_use, or refusal.
if message.stop_reason == "end_turn":
print(message.content[0].text)usageReports input and output token counts so you can track cost.
print(message.usage.input_tokens)
print(message.usage.output_tokens)message.modelConfirms which model actually served the request.
print(message.model) # e.g. "claude-opus-4-8"Claude Opus 4.8Best for hard reasoning and agentic tasks; the default for most new integrations.
claude-opus-4-8Claude Sonnet 5Near-Opus quality on coding and agentic work at a lower cost.
claude-sonnet-5Claude Haiku 4.5Fastest and cheapest option for high-volume tasks like tagging or routing.
claude-haiku-4-5Claude Fable 5Anthropic's most capable model for the hardest, longest-horizon tasks.
claude-fable-5Set the ANTHROPIC_API_KEY environment variable and the SDK reads it automatically when you create a client. Pass api_key directly to the constructor only if you manage multiple keys or a secrets manager.
Start with claude-opus-4-8 for most new integrations — it balances reasoning quality with cost. Switch to claude-haiku-4-5 for high-volume, latency-sensitive tasks like tagging or routing, and reach for claude-sonnet-5 as a faster, cheaper middle ground.
stop_reason explains why Claude stopped generating: end_turn means a normal finish, max_tokens means the response got cut off, tool_use means Claude called a tool, and refusal means the safety classifier declined the request. Always check it before reading content.
The Messages API requires model, max_tokens, and messages on every request. Missing any of these — most often max_tokens — fails validation before the request is sent, so double-check your payload against the required fields first.
Append each turn to the messages array in order, alternating user and assistant roles. Claude has no memory between calls, so you must resend the full history on every request.
messages = [
{"role": "user", "content": "What is a closure?"},
{"role": "assistant", "content": "A closure captures variables from its enclosing scope."},
{"role": "user", "content": "Give me a JavaScript example."},
]Send a Prompt, Read the Reply
Sends a single prompt to Claude, checks the stop reason, and prints the response text and token usage.
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
system="You are a concise technical writer.",
messages=[{"role": "user", "content": "Explain what a closure is, in two sentences."}]
)
if message.stop_reason == "end_turn":
print(message.content[0].text)
elif message.stop_reason == "max_tokens":
print("Response was cut off - increase max_tokens")
elif message.stop_reason == "refusal":
print("Request declined by safety classifier")
print(f"Tokens used: {message.usage.input_tokens} in / {message.usage.output_tokens} out")Always check stop_reason before reading content — it tells you whether the response is complete, truncated, or refused.