Claude AI Complete Guide — Features, API, and Best Practices 2026

Introduction#

Why This Matters#

Claude has become the preferred LLM for many development teams, particularly for tasks requiring detailed code analysis, handling long documents, and producing transparent, well-reasoned responses. Understanding how Claude differs from ChatGPT — and where each model genuinely leads — saves weeks of trial-and-error when building production AI features. This guide covers the Claude ecosystem from web interface to API to production deployment.

Claude Model Family#

Anthropic maintains three production model tiers in 2026:

Claude 3.5 Sonnet — The primary production model. Best balance of capability, speed, and cost. Used for code review, complex analysis, document summarization, and most general tasks. Context: 200K tokens.

Claude 3 Opus — Maximum capability. Used for the most complex reasoning tasks where quality matters more than cost. Context: 200K tokens.

Claude 3.5 Haiku — Fast and cheap. Used for high-volume classification, extraction, and real-time applications requiring sub-second responses. Context: 200K tokens.

All three models share the same 200K-token context window, which is a significant advantage over GPT-4o's 128K limit for long-document workflows.

Key Differences from ChatGPT#

Context window: Claude Sonnet (200K) vs GPT-4o (128K). This matters for analyzing large codebases or long documents without chunking.

Constitutional AI: Claude is trained to be honest about uncertainty. It says "I'm not confident about this" rather than generating a plausible-sounding but incorrect answer. This makes hallucinations more transparent.

Code analysis: Many developers find Claude provides more nuanced code review feedback, particularly on logic errors, edge cases, and security patterns.

No built-in image generation: Claude handles image input (analyzing screenshots, diagrams) but does not generate images. For image generation, GPT-4o with DALL-E 3 is the option.

Web Interface Setup#

Access Claude at claude.ai. Free tier provides limited Claude 3.5 Sonnet access. Claude Pro ($20/month) provides higher usage limits, priority access, and Projects.

Projects — Organize conversations by topic. Each project maintains its own system prompt and file context. Create separate projects for each codebase you work on regularly.

File uploads — Upload code files, PDFs, and images. Claude analyzes them within the conversation context. For codebases, create a zip of your source files and upload it directly.

Claude API — Getting Started#

pip install anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

Get your API key at console.anthropic.com. Free trial credit included on new accounts.

from anthropic import Anthropic
 
client = Anthropic()
 
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system="You are a senior Python developer. Be concise and precise.",
    messages=[
        {
            "role": "user",
            "content": "Review this function for security issues:\n\ndef login(username, password):\n    user = db.query(f'SELECT * FROM users WHERE username = {username}')\n    if user and user.password == password:\n        return generate_token(user.id)",
        }
    ],
)
 
print(response.content[0].text)

Key API differences from OpenAI: system is a top-level parameter (not a message role), and content is a list of content blocks rather than a string.

Multi-Turn Conversations#

from anthropic import Anthropic
 
client = Anthropic()
 
class ClaudeSession:
    def __init__(self, system_prompt: str = ""):
        self.client = Anthropic()
        self.system = system_prompt
        self.messages = []
 
    def chat(self, user_message: str) -> str:
        self.messages.append({"role": "user", "content": user_message})
 
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2048,
            system=self.system,
            messages=self.messages,
        )
 
        reply = response.content[0].text
        self.messages.append({"role": "assistant", "content": reply})
        return reply
 
session = ClaudeSession(
    system_prompt=(
        "You are a code review assistant. "
        "For every piece of code, check: security, error handling, performance, clarity."
    )
)
 
print(session.chat("Here is a user registration endpoint: [code]"))
print(session.chat("Now check the password reset flow: [code]"))

Using Claude for Long Documents#

Claude's 200K-token context is the right tool for long-document analysis — legal contracts, technical specifications, large codebases.

import anthropic
from pathlib import Path
 
client = anthropic.Anthropic()
 
def analyze_codebase(src_dir: str, question: str) -> str:
    """Analyze an entire codebase with Claude."""
    # Collect all source files
    source_files = list(Path(src_dir).rglob("*.py"))
 
    content_parts = []
    for file_path in source_files[:30]:  # Limit to 30 files for token safety
        try:
            code = file_path.read_text(encoding="utf-8")
            content_parts.append(
                f"=== {file_path.relative_to(src_dir)} ===\n{code}"
            )
        except Exception:
            pass
 
    combined = "\n\n".join(content_parts)
 
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=4096,
        messages=[
            {
                "role": "user",
                "content": f"Codebase contents:\n\n{combined}\n\nQuestion: {question}",
            }
        ],
    )
 
    return response.content[0].text
 
result = analyze_codebase(
    "./src",
    "What are the top 3 security vulnerabilities in this codebase?"
)

Tool Use (Function Calling)#

import anthropic
import json
 
client = anthropic.Anthropic()
 
tools = [
    {
        "name": "run_tests",
        "description": "Execute the test suite for a module and return results",
        "input_schema": {
            "type": "object",
            "properties": {
                "module": {"type": "string"},
                "verbose": {"type": "boolean", "default": False},
            },
            "required": ["module"],
        },
    }
]
 
def run_agent(task: str) -> str:
    messages = [{"role": "user", "content": task}]
 
    while True:
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            tools=tools,
            messages=messages,
        )
 
        if response.stop_reason == "end_turn":
            return response.content[0].text
 
        if response.stop_reason == "tool_use":
            messages.append({"role": "assistant", "content": response.content})
 
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    if block.name == "run_tests":
                        result = f"Tests passed: 42/45 for {block.input['module']}"
                    else:
                        result = "Unknown tool"
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": result,
                    })
 
            messages.append({"role": "user", "content": tool_results})

Common Mistakes#

• Using Claude for tasks requiring image generation — Claude analyzes images but does not generate them
• Not using the system parameter — it significantly affects output quality and consistency
• Treating all 200K tokens as free — longer prompts cost proportionally more; send only what is needed
• Not saving message history properly — the API is stateless, you must manage history
• Using Opus for routine tasks — it costs 5x more than Sonnet with marginal improvement on most tasks

Best Practices#

• Use Haiku for real-time applications and high-volume classification; Sonnet for most tasks; Opus for maximum reasoning
• Set clear system prompts — Claude responds noticeably better with explicit behavioral guidance
• For code review, ask Claude to "identify issues and provide corrected versions" rather than just "find problems"
• Use Claude's uncertainty signals — when it says it is not confident, verify independently
• Cache prompt prefixes using Anthropic's prompt caching feature to reduce costs on repeated system prompts

Key Takeaways#

• Claude 3.5 Sonnet is the default production choice; Haiku for speed/cost; Opus for maximum reasoning
• All Claude models share a 200K-token context window, outperforming GPT-4o's 128K for long-document tasks
• The system parameter is a top-level parameter in Claude's API, not a message role like in OpenAI's API
• Constitutional AI training makes Claude more likely to express uncertainty than to confidently hallucinate
• Claude does not generate images — it only analyzes them; use OpenAI for image generation tasks
• Tool use (function calling) in Claude uses stop_reason == "tool_use" rather than OpenAI's finish_reason == "tool_calls"
• Prompt caching reduces costs significantly for applications that reuse the same long system prompt repeatedly
• Projects in the web interface maintain system prompts and file context across conversations in a topic