Claude 4 Released: What Developers Need to Know

When I first got my hands on Claude 4’s extended thinking feature, I genuinely stopped what I was doing and just watched it work through a complex refactoring problem. It wasn’t just giving me an answer—it was showing me how it was reasoning through each step. I could see it considering different approaches, weighing tradeoffs, and ultimately arriving at a solution I wouldn’t have thought of myself. That’s when it clicked: this isn’t just another incremental LLM update. Claude 4 represents a fundamental shift in how we can work with AI as developers.

Anthropic dropped Claude 4 in May 2025, and since then, they’ve been on a tear—releasing Opus 4.1, then the entire 4.5 lineup by late 2025. Meanwhile, Claude 3 Opus was retired just last week on January 5, 2026. If you haven’t made the jump yet, now’s the time. The window for gradual migration is closing, and honestly, once you experience what Claude 4 can do, you won’t want to go back anyway.

Here’s the thing: there’s a lot of noise out there about Claude 4. Marketing speak, benchmark wars, the usual hype cycle that accompanies every major AI release. What I want to give you is the practical developer’s perspective—what actually matters when you’re building with it, how to get started today, and where Claude 4 genuinely excels (and where it doesn’t). I’ve spent the past several months integrating Claude 4 into my workflows, and I’ve learned a few things worth sharing. Let’s get into it.

What Is Claude 4? The Quick Overview

Claude 4 is Anthropic’s fourth-generation family of large language models, and it’s positioned as their most capable release to date. The initial launch on May 22, 2025 introduced Claude Opus 4 and Claude Sonnet 4, followed by updates throughout the year culminating in the 4.5 series.

If you’re not familiar with Anthropic, they’re the AI safety-focused company founded by former OpenAI researchers, including Dario and Daniela Amodei. Their approach to AI development emphasizes what they call “Constitutional AI”—training models to be helpful, harmless, and honest. This philosophy carries through to Claude 4, which tends to be more cautious and nuanced in its responses compared to some competitors.

What makes Claude 4 different from Claude 3? Three things stand out immediately:

1. Extended thinking — The model can now show its reasoning process and use tools while thinking through complex problems. This isn’t just a cosmetic feature—it fundamentally changes how you can debug AI behavior and understand its decision-making.

2. Agentic capabilities — Claude 4 is built for long-running, multi-step tasks that require memory and tool orchestration. It can maintain context, remember decisions, and execute complex workflows without constant human intervention.

3. Massive context windows — Up to 1 million tokens on Opus 4.1+, with 200K as the standard. That’s roughly 750,000 words—enough to fit entire codebases, comprehensive documentation sets, or lengthy legal documents in a single prompt.

For developers specifically, Claude 4 is interesting because Anthropic has clearly prioritized the coding use case. Their Claude Code tool, which went generally available alongside Opus 4, is essentially an AI pair programmer that can operate autonomously on your codebase. It’s not just suggesting completions—it’s making architectural decisions, running tests, and committing changes.

In the landscape of AI assistants—GPT-5, Claude 4, Gemini 2—Claude has carved out a reputation for being the “developer’s AI.” It’s particularly strong at coding, following complex instructions, and maintaining coherence over very long interactions. Whether that reputation is fully deserved is something we’ll dig into shortly.

Claude 4 Model Lineup: Opus, Sonnet, and Haiku Explained

Anthropic continues their three-tier model strategy with Claude 4. Understanding which model to use for which task is crucial—you can easily overspend by defaulting to Opus for everything, or get frustrated by underpowered responses if you always reach for Haiku. Here’s how each model fits into the picture as of January 2026:

Model	Release	Best For	Context Window	Speed	Cost Tier
Claude Opus 4.5	Nov 2025	Complex reasoning, coding, agents	200K (1M extended)	Slower	$$$
Claude Sonnet 4.5	Sep 2025	Balanced performance	200K (1M extended)	Medium	$$
Claude Haiku 4.5	Oct 2025	Real-time, high-volume	200K	Fast	$

Claude Opus 4.5 — The Flagship

Opus 4.5 is Anthropic’s most intelligent model. Period. It’s designed for tasks where you need maximum reasoning capability—complex coding problems, multi-step research, and agentic workflows that run for hours. When Anthropic talks about Claude achieving #1 on coding benchmarks like SWE-bench, they’re talking about Opus.

I’ve found Opus particularly impressive for architectural decisions and code reviews. It catches things that Sonnet misses, especially around edge cases and security implications. Last week, I fed it a authentication module I’d written and asked for a security review. It identified a timing attack vulnerability I’d completely overlooked—and explained the attack vector in detail.

The tradeoff? It’s slower and more expensive. For quick tasks, you’ll feel the latency. A complex query that Sonnet handles in 2-3 seconds might take Opus 8-10 seconds as it reasons more deeply. For interactive coding assistance, this can feel sluggish. For batch processing or async workflows, it’s a non-issue.

When to use Opus: Architecture reviews, complex debugging, security audits, tasks requiring synthesis of many documents, and agentic workflows where accuracy matters more than speed.

Claude Sonnet 4.5 — The Balanced Choice

Most developers should probably default to Sonnet 4.5 for everyday work. It hits a sweet spot between Opus-level intelligence and Haiku-level speed. I use it for most of my daily coding assistance—generating functions, explaining code, writing tests, drafting documentation.

The 4.5 update in September 2025 brought significant improvements to agentic tasks. Sonnet can now handle multi-step operations much more reliably than the original Sonnet 4. It’s better at maintaining state, following complex instructions, and recovering from errors mid-task.

One thing I’ve noticed: Sonnet occasionally needs more explicit instructions than Opus. Where Opus might infer what you mean from context, Sonnet sometimes takes a too-literal interpretation. It’s not a dealbreaker—just be more specific in your prompts when using Sonnet.

When to use Sonnet: Daily coding assistance, content generation, code explanation, test writing, moderate-complexity tasks where you need both speed and quality.

Claude Haiku 4.5 — Speed and Cost Optimized

Haiku is your high-volume, low-latency workhorse. Customer support bots, real-time assistants, and any use case where response time matters more than peak intelligence. At $1/$5 per million tokens, it’s also significantly cheaper than the alternatives.

Honestly, I’m continuously surprised by how capable Haiku 4.5 is. For straightforward coding tasks—formatting, simple refactors, generating boilerplate—it’s more than sufficient and much cheaper. I’ve started routing my simpler requests to Haiku automatically and saving Sonnet for when I actually need deeper reasoning.

The October 2025 Haiku 4.5 update improved its coding abilities notably. It’s no longer the “dumb” option in the lineup—it’s legitimately useful for a wide range of development tasks.

When to use Haiku: High-volume chat applications, real-time features, simple code generation, classification tasks, cost-sensitive applications.

What’s New in Claude 4: Key Features for Developers

Let’s break down the features that actually matter for building things. I’ll skip the marketing fluff and focus on capabilities you can use today.

Extended Thinking with Tool Use

This is the headline feature that made me pause when I first used it. Extended thinking allows Claude to “show its work”—allocating extra compute to reason through complex problems before responding. You can literally watch the model think through different approaches, consider edge cases, and arrive at conclusions.

But here’s what makes Claude 4’s implementation special: it can use tools while thinking. Previous versions had to complete their thinking, then use tools, then respond. Claude 4 can interleave thinking and tool use, which means it can search the web, run code, check documents, and incorporate those results into its ongoing reasoning process.

This matters for agentic applications. Imagine asking Claude to debug a production issue. With extended thinking + tool use, it can:

1. Think about possible causes
2. Search documentation to verify its hypotheses
3. Run diagnostic code
4. Revise its thinking based on results
5. Propose and test fixes
6. Iterate until the problem is solved

All without breaking the reasoning chain. Here’s how to enable it in the API:

import anthropic

client = anthropic.Anthropic()

# Enabling extended thinking in the API
response = client.messages.create(
    model="claude-sonnet-4-5-20250915",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000  # Allocate thinking budget
    },
    messages=[{
        "role": "user", 
        "content": "Analyze this codebase and suggest architecture improvements. Consider scalability, maintainability, and security."
    }]
)

# Access both thinking and response
for block in response.content:
    if block.type == "thinking":
        print("Claude's reasoning:", block.thinking)
    elif block.type == "text":
        print("Final response:", block.text)

The beta also supports parallel tool execution—Claude can query multiple data sources simultaneously rather than sequentially. For agentic applications, this dramatically reduces latency. Instead of checking five different APIs one by one, Claude can fan out all five requests and wait for them to complete.

I’m not 100% sure this is the final API shape. Anthropic has it in beta, and I’ve seen some changes between versions. But the core capability is solid and worth building around.

Claude Code: Your AI Coding Partner

Claude Code is Anthropic’s answer to GitHub Copilot, but it’s philosophically different. Rather than inline completions (suggesting the next few characters as you type), Claude Code operates as an autonomous agent that can:

• Make multi-file code changes across your entire project
• Run terminal commands and interpret their output
• Create and manage sub-agents for complex tasks
• Integrate with MCP (Model Context Protocol) servers for extended capabilities
• Maintain memory across sessions so it “knows” your project

The IDE extensions for VS Code and JetBrains are genuinely useful. I’ve been using the VS Code extension for a few weeks now, and the in-editor code reviews have become part of my workflow. When I finish a feature, I’ll ask Claude Code to review it before I commit. It catches bugs, suggests improvements, and even identifies when I’ve forgotten to update related tests.

What I appreciate about Claude Code is that it’s built on Opus 4.5, so you get the full power of the flagship model. The “Plan Mode” feature is particularly nice—before making changes, Claude outlines what it’s planning to do and asks for confirmation. This prevents the AI from going off in unexpected directions.

The downside is that it burns through tokens quickly, so costs can add up on large projects. I’ve seen single review sessions cost $2-3 in API usage. For enterprise teams with volume discounts, this is fine. For indie developers, you’ll want to be strategic about when you invoke it.

Enhanced Memory and Context

Claude 4 models support a standard context window of 200K tokens—roughly 150,000 words or about 500 pages. That’s enough to fit most codebases, comprehensive documentation sets, or even entire books. Opus 4.1 and later can handle up to 1 million tokens, though you’ll pay premium pricing for requests exceeding 200K.

But the more interesting feature is Claude’s new memory capability. When granted access to local files, Claude can extract and retain important information across sessions. It’s not persistent memory in the way humans remember things—it’s more like Claude building a persistent knowledge base about your project that it can reference.

In practice, this means:

• Claude remembers your coding conventions without re-explaining them
• It tracks project state (what’s been implemented, what’s in progress)
• It maintains context about previous decisions and their rationale
• It can reference past conversations when making new suggestions

For long-running projects, this is significant. Before, every new session meant re-establishing context. Now, Claude can pick up where it left off.

New API Features

Beyond the headline capabilities, Claude 4 introduced several API improvements worth knowing about:

Files API: Upload and reference documents directly through the API without base64 encoding. Supports PDFs, images, and various document formats.

MCP Connector: Native integration with Model Context Protocol servers, allowing Claude to access external tools, databases, and services.

Prompt Caching: Cache frequently used prompts for up to an hour at 90% discount. Great for applications with consistent system prompts.

Effort Parameter (Opus 4.5): Control how much reasoning Claude applies. Set to “low” for quick tasks, “high” for complex problems.

Claude 4 API Pricing: What It Costs in 2026

Let’s talk money. This is one of the most common questions I get, and the answer matters for architectural decisions. API pricing is per million tokens (MTok), which roughly translates to 750,000 words.

Model	Input (per MTok)	Output (per MTok)	Batch Discount
Claude Opus 4.5	$5.00	$25.00	50% off
Claude Sonnet 4.5	$3.00	$15.00	50% off
Claude Haiku 4.5	$1.00	$5.00	50% off
Legacy Opus 4/4.1	$15.00	$75.00	50% off

A few important notes that aren’t immediately obvious:

Long-context pricing: If your requests exceed 200K input tokens, you’ll pay double the standard rates. So Sonnet 4.5 jumps to $6.00 input / $22.50 output for very long documents. Plan your context budgets accordingly.

Prompt caching: Anthropic offers up to 90% discount on cached content (prompts you reuse). For applications with consistent system prompts—which is most production applications—this adds up fast. A system prompt that costs $0.50 per request drops to $0.05 with caching.

Extended thinking tokens: The tokens Claude uses for extended thinking count toward your usage. A 10,000 token thinking budget adds 10,000 tokens to your request. Factor this into cost calculations for complex tasks.

Legacy premium: If you’re still using Claude Opus 4 or 4.1 (the pre-4.5 versions), you’re paying 3x the price of Opus 4.5. Anthropic is clearly incentivizing migration—they want everyone on 4.5.

Batch processing: If you can tolerate async processing, batch jobs run at 50% discount. Great for overnight analysis, bulk content processing, or any non-real-time workload.

Here’s my honest take on pricing: Claude 4.5 is genuinely cost-competitive while offering larger context windows. For most developer use cases, Sonnet 4.5 at $3/$15 per million tokens is excellent value—cheaper than GPT-5 for comparable capability. Just watch your extended thinking token budgets—those can sneak up on you if you’re not careful.

Cost Optimization Tips

After running Claude 4 in production for a few months, here are strategies that actually work:

1. Route by complexity: Use Haiku for simple tasks, Sonnet for moderate work, Opus only when needed. A simple routing layer can cut costs 40-60%.

2. Cache aggressively: Enable prompt caching for system prompts. Most applications reuse the same instructions thousands of times.

3. Batch non-urgent work: Any processing that doesn’t need real-time results should go through batch API.

4. Truncate context: Don’t send your entire codebase when Claude only needs one file. Be surgical about context.

5. Monitor thinking budgets: Start with lower thinking budgets and increase only when needed.

Getting Started with Claude 4 API (Python Tutorial)

Enough theory. Let’s write some code and get you up and running.

Step 1: Get Your API Key

First, you’ll need an Anthropic account and API access:

1. Go to console.anthropic.com
2. Create an account or sign in with existing credentials
3. Navigate to API Keys in the dashboard
4. Click “Create Key” and give it a descriptive name
5. Copy the key immediately (you won’t see it again)

Security note: Never hardcode API keys in your source code. Use environment variables or a secrets manager. If your key is committed to a public repository, consider it compromised.

Step 2: Install the Python SDK

The official Anthropic Python library is the recommended way to interact with Claude. It handles authentication, retries, error formatting, and keeps up with API changes.

pip install anthropic

Then set your API key as an environment variable:

# Linux/Mac
export ANTHROPIC_API_KEY='your-api-key-here'

# Windows PowerShell
$env:ANTHROPIC_API_KEY='your-api-key-here'

For production, consider using a .env file with python-dotenv or your deployment platform’s secrets management.

Step 3: Your First API Call

Here’s a complete working example that you can run immediately:

import anthropic

# Initialize the client (automatically uses ANTHROPIC_API_KEY env var)
client = anthropic.Anthropic()

# Make a simple request
message = client.messages.create(
    model="claude-sonnet-4-5-20250915",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Write a Python function that validates email addresses using regex. Include docstring and type hints."
        }
    ]
)

# Print the response
print(message.content[0].text)

That’s it. Run this script and you’ll get a well-documented email validation function back.

The SDK handles authentication, retries, and error formatting automatically. For production use, you’ll want to add explicit error handling:

from anthropic import APIError, RateLimitError, APIConnectionError

try:
    message = client.messages.create(
        model="claude-sonnet-4-5-20250915",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Your prompt here"}]
    )
    print(message.content[0].text)
except RateLimitError:
    print("Rate limited - implement exponential backoff")
except APIConnectionError:
    print("Network error - check connectivity")
except APIError as e:
    print(f"API error: {e.status_code} - {e.message}")

Step 4: Using Extended Thinking

For complex reasoning tasks, enable extended thinking to get better results:

response = client.messages.create(
    model="claude-opus-4-5-20251114",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 8000  # Allow up to 8000 tokens for reasoning
    },
    messages=[
        {
            "role": "user",
            "content": """Review this code and identify potential security vulnerabilities:
            
            def login(username, password):
                query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
                return db.execute(query)
            """
        }
    ]
)

# Access both the thinking process and final response
for block in response.content:
    if block.type == "thinking":
        print("=== Claude's Reasoning ===")
        print(block.thinking)
        print()
    elif block.type == "text":
        print("=== Final Response ===")
        print(block.text)

This example will identify the obvious SQL injection vulnerability, but more importantly, you can see how Claude reasons about security—what attack vectors it considers, what mitigation strategies it suggests.

Step 5: Adding System Prompts

For consistent behavior, use system prompts to establish context:

response = client.messages.create(
    model="claude-sonnet-4-5-20250915",
    max_tokens=2048,
    system="You are a senior Python developer with expertise in security best practices. Provide code review feedback that's specific, actionable, and includes examples. Always explain the 'why' behind your suggestions.",
    messages=[
        {
            "role": "user",
            "content": "Review this authentication handler for security issues."
        }
    ]
)

For more detailed guidance on building with Claude, check out our complete Claude API tutorial.

Claude 4 vs GPT-5: How Do They Compare?

This is the question I get asked most often. Here’s my honest assessment based on using both extensively over the past several months. I’m not going to pretend there’s a clear winner—the answer genuinely depends on your use case.

Aspect	Claude 4.5	GPT-5	Verdict
Coding	SWE-bench #1	Strong	Claude edge
Context window	200K-1M	128K	Claude wins
Extended thinking	Yes (beta)	Yes (o3)	Comparable
API pricing	Lower	Higher	Claude wins
Ecosystem	Growing	Massive	GPT-5 wins
Multimodal	Vision, docs	Vision, audio, video	GPT-5 wins
Fine-tuning	Limited	Full support	GPT-5 wins

Where Claude 4 Genuinely Excels

Long-form coding tasks: Claude maintains coherence better over multi-file refactors. When I’m making sweeping changes across a codebase—updating an API version, refactoring a module—Claude keeps track of what it’s done and what still needs changing. GPT-5 sometimes loses the thread on very long operations.

Instruction following: According to Anthropic’s benchmarks, Claude 4 is 65% less likely to take shortcuts in agentic tasks. In practice, this means Claude actually does what you ask, even when the task is tedious or multi-step. It’s less likely to give you a “here’s the general approach” when you asked for working code.

Technical writing: Claude produces cleaner, more readable code and documentation. This is subjective, but multiple developers I’ve talked to share this observation. Claude’s output tends to need less editing.

Cost efficiency: You get more capability per dollar, especially on longer contexts. For budget-conscious projects, the math works out in Claude’s favor.

Safety and ethics: Claude is more cautious about potentially harmful requests. This can be a pro or con depending on your use case, but for enterprise applications, it’s typically a plus.

Where GPT-5 Has the Edge

Multimodal breadth: GPT-5’s audio and video understanding is ahead. If you’re building something that processes audio, video, or needs to work across multiple modalities, OpenAI is the clear choice.

Ecosystem and integrations: More third-party tools support OpenAI natively. The plugin ecosystem, fine-tuning infrastructure, and developer tools are more mature.

Function calling: While Claude has tool use, OpenAI’s function calling is more mature and better documented. Complex tool-use scenarios are still easier to implement with GPT-5.

Fine-tuning: OpenAI offers full fine-tuning support. If you need a model adapted to your specific domain or style, that’s currently easier with GPT-5.

Brand recognition: Non-technical stakeholders often default to “ChatGPT.” If you need to explain your tech choices to executives, OpenAI is an easier sell.

For a deeper dive, see our full comparison of top AI models.

My personal take? I reach for Claude Sonnet 4.5 for most coding tasks and GPT-5 when I need multimodal capabilities or specific integrations. Having accounts with both is cheap insurance—the APIs are similar enough that switching isn’t painful.

Best Use Cases for Claude 4

Based on my experience and community feedback, here’s where Claude 4 shines:

Complex coding tasks: Multi-file refactors, architecture reviews, debugging gnarly issues. Claude’s extended thinking really helps with problems that require holding multiple considerations in mind simultaneously. When I’m stuck on a problem that’s been bugging me for hours, Opus + extended thinking usually unsticks me.

Long document analysis: With 200K-1M token context windows, you can feed Claude entire codebases, legal documents, or research papers. Try asking it to find inconsistencies, summarize key points, or compare multiple documents. I regularly use it to review contracts—it catches things my tired eyes miss.

Multi-step agentic workflows: Claude 4’s memory and tool use make it well-suited for autonomous tasks—code generation, testing, deployment pipelines that run without constant human intervention. Claude Code is the productized version of this, but you can build custom workflows too.

Content generation: Technical documentation, API references, README files. Claude produces clean, well-structured prose that typically needs less editing than alternatives. It’s become my first draft for most documentation.

Data analysis with code execution: Claude can write Python to analyze datasets, generate charts, identify patterns, and iterate on findings. The code execution tool (currently free for initial usage) handles this natively—you don’t need to run the code yourself.

For practical examples of building with Claude’s capabilities, check out our guide to building RAG applications.

Migrating from Claude 3 to Claude 4

If you’re still on Claude 3, the clock is ticking. Anthropic retired Claude Opus 3 on January 5, 2026, and the other Claude 3 models will follow. Here’s what you need to know about migrating smoothly.

API compatibility: The good news is that the Claude API is largely backward compatible. Most code changes involve updating the model name:

# Old - Claude 3
model="claude-3-opus-20240229"
model="claude-3-sonnet-20240229"
model="claude-3-haiku-20240307"

# New - Claude 4.5
model="claude-opus-4-5-20251114"
model="claude-sonnet-4-5-20250915"
model="claude-haiku-4-5-20251022"

The message format, authentication, and most parameters work identically.

What might break:

1. Behavior changes — Claude 4 follows instructions more strictly, which occasionally means different outputs for ambiguous prompts. If your prompt relied on Claude 3’s interpretation of vague instructions, you may need to be more explicit.

2. Token usage — Extended thinking uses tokens from your budget. If you enable extended thinking, costs will increase. Budget accordingly.

3. System prompts — Claude 4 is less susceptible to prompt injection, which is good for security but might affect some creative workarounds. If you were using clever prompt techniques to get around limitations, test carefully.

4. Output formatting — Claude 4 sometimes formats responses differently. If you’re parsing structured output, test your parsers.

Performance improvements to expect:

• Faster response times on Sonnet and Haiku (Opus is about the same)
• Significantly better coding accuracy across all models
• More reliable multi-step reasoning
• Better handling of very long contexts
• Fewer hallucinations on factual queries

Migration strategies:

Even experts disagree on the best approach. Options include:

1. Gradual rollout: Route 10% of traffic to Claude 4, monitor, increase to 50%, then 100%
2. Shadow testing: Run both models in parallel, compare outputs, switch when confident
3. Feature flag: Let power users opt into Claude 4 early
4. Big bang: Just switch, fix issues as they arise (simplest but riskiest)

I’d suggest setting up parallel testing first—run your key use cases against both Claude 3 and Claude 4, compare outputs, and verify nothing regresses before fully migrating. The cost of running both briefly is trivial compared to shipping broken functionality.

Frequently Asked Questions

What is Claude 4 and when was it released?

Claude 4 is Anthropic’s fourth-generation large language model family. Initial release (Claude Opus 4 and Sonnet 4) was May 22, 2025. The Claude 4.5 series (Opus 4.5, Sonnet 4.5, Haiku 4.5) followed between September and November 2025. As of January 2026, Claude 4.5 is the current recommended version.

How much does Claude 4 API cost?

Claude Opus 4.5 costs $5 per million input tokens and $25 per million output tokens. Claude Sonnet 4.5 is $3/$15, and Claude Haiku 4.5 is $1/$5. Batch processing offers 50% discount. Requests over 200K tokens pay premium rates (approximately double).

Is Claude 4 better than GPT-5?

For coding and long-context tasks, Claude 4 has advantages—it tops the SWE-bench leaderboard and offers larger context windows at lower prices. GPT-5 excels at multimodal tasks (audio, video) and has a larger ecosystem. Most developers benefit from having access to both.

What’s the difference between Opus, Sonnet, and Haiku?

Opus is the most capable (and expensive/slowest)—use it for complex reasoning and important tasks. Sonnet balances capability and speed for everyday work. Haiku prioritizes speed and cost for high-volume, latency-sensitive applications like chatbots.

How do I use Claude 4’s extended thinking?

Enable it via the API by adding a thinking parameter with type: "enabled" and a budget_tokens allocation. Extended thinking is in beta and works best on complex reasoning tasks. Simpler queries may not benefit and will just cost more.

Should I upgrade from Claude 3 to Claude 4?

Yes. Claude Opus 3 was retired January 5, 2026. Claude 4.5 models are more capable, often cheaper (legacy Claude 4 pricing is higher than 4.5), and include new features like extended thinking and memory. There’s no technical reason to stay on Claude 3.

What is Claude Code?

Claude Code is Anthropic’s agentic coding tool built on Claude Opus 4.5. It can make multi-file code changes, run terminal commands, manage sub-agents, and integrate with IDEs (VS Code, JetBrains). It’s designed for autonomous coding assistance on real projects.

Wrapping Up

Claude 4 represents a meaningful leap for developers working with AI. The extended thinking capability changes how you can approach complex problems—instead of hoping the model “gets it,” you can watch it reason and intervene when needed. The pricing is competitive. And the agentic features—Claude Code, memory, parallel tool use—point toward a future where AI isn’t just answering questions but actively participating in development workflows.

My honest assessment: if you’re building with LLMs in 2026, Claude 4 deserves a serious look. It won’t replace your judgment, and it still makes mistakes (all LLMs do). But for coding, technical writing, and long-context analysis, it’s become my default choice.

Ready to get started? Here’s your next step: set up your API key, copy the code examples from this post, and try Claude on a real problem you’re working on. Nothing beats hands-on experience. For a deeper walkthrough, our complete Claude API tutorial covers everything from authentication to advanced techniques.

The AI landscape moves fast. Claude 4 is what’s working right now—and based on Anthropic’s pace of updates, it’ll keep getting better. Time to start building.