Claude 4: Anthropic's New AI Models Raise the Bar

The AI world is buzzing again, and this time, it's Anthropic's new Claude 4 models—Opus 4 and Sonnet 4—taking center stage. Announced on May 22, 2025, these next-generation models are making some serious waves, especially in the coding arena. If you're curious about what these new AI powerhouses can do and how they stack up, you're in the right place. Let's break it down in plain English.

What's New with Claude 4?

Anthropic has rolled out two new models:

• Claude Opus 4: This is the big gun. Anthropic claims it's the world's best coding model. It's designed for complex, long-running tasks and can work for hours on end. Think of it as the AI brainiac for heavy-duty coding and problem-solving.
• Claude Sonnet 4: A significant upgrade to its predecessor (Sonnet 3.7), Sonnet 4 offers improved coding and reasoning, and it's better at following instructions precisely. It aims to balance high performance with efficiency for everyday tasks.

Both models are hybrid, meaning they can give you near-instant responses for quick queries or engage in "extended thinking" for deeper, more complex reasoning. They're available through various plans (Pro, Max, Team, and Enterprise on Claude.ai), the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI. Good news for free users: Sonnet 4 is also available to you! Pricing remains the same as the previous Opus and Sonnet models.

Anthropic also announced Claude Code is now generally available, with new beta extensions for VS Code and JetBrains, plus an extensible Claude Code SDK.

How Does Claude 4 Perform?

This is where things get interesting. Anthropic has put Claude 4 through its paces on several benchmarks, and the results are impressive, particularly in coding.

Here's a peek at how Claude Opus 4 and Sonnet 4 compare to other leading models like OpenAI's GPT-4.1 and Google's Gemini 2.5 Pro based on the provided image:

• Agentic Coding (SWE-bench Verified):
  • Claude Opus 4: 72.5% / 79.4%
  • Claude Sonnet 4: 72.7% / 80.2%
  • OpenAI GPT-4.1: 54.6%
  • Gemini 2.5 Pro (Preview): 63.2%
  • Claude 4 models are leading the pack here, showcasing strong capabilities in software engineering tasks.
• Agentic Terminal Coding (Terminal-bench):
  • Claude Opus 4: 43.2% / 50.0%
  • Claude Sonnet 4: 35.5% / 41.3%
  • OpenAI GPT-4.1: 30.3%
  • Gemini 2.5 Pro (Preview): 25.3%
  • Opus 4, in particular, shows a significant lead in terminal coding tasks.
• Graduate-Level Reasoning (GPQA Diamond):
  • Claude Opus 4: 79.6% / 83.3%
  • Claude Sonnet 4: 75.4% / 83.8%
  • OpenAI o3: 83.3%
  • OpenAI GPT-4.1: 66.3%
  • Gemini 2.5 Pro (Preview): 83.0%
  • While OpenAI's o3 and Gemini 2.5 Pro are strong contenders, Claude 4 models hold their own in graduate-level reasoning.
• Agentic Tool Use (TAU-bench):
  • Claude Opus 4: Retail 81.4%, Airline 59.6%
  • Claude Sonnet 4: Retail 80.5%, Airline 60.0%
  • OpenAI GPT-4.1: Retail 68.0%, Airline 49.4%
  • Claude 4 models demonstrate superior performance in using tools for agentic tasks.
• Multilingual Q&A (MMMLU):
  • Claude Opus 4: 88.8%
  • Claude Sonnet 4: 86.5%
  • OpenAI GPT-4.1: 83.7%
  • Opus 4 leads in multilingual question answering.
• Visual Reasoning (MMMU validation):
  • Claude Opus 4: 76.5%
  • Claude Sonnet 4: 74.4%
  • OpenAI GPT-4.1: 74.8%
  • Gemini 2.5 Pro (Preview): 79.6%
  • Gemini 2.5 Pro has an edge in visual reasoning, but Claude 4 models are competitive.
• High School Math Competition (AIME 2025):
  • Claude Opus 4: 75.5% / 90.0%
  • Claude Sonnet 4: 70.5% / 85.0%
  • OpenAI o3: 88.9%
  • Gemini 2.5 Pro (Preview): 83.0%
  • OpenAI o3 shows strong performance in math competitions, with Claude 4 models also performing very well, especially Opus 4.

It's worth noting that some of these scores, particularly the higher ones for Claude 4 in coding, were achieved using "high compute" methods, which involve sampling multiple attempts and using an internal scoring model to pick the best one. Some benchmarks also utilized "extended thinking."

Key Improvements and Features

Beyond the impressive benchmark scores, Anthropic highlights several improvements:

• Reduced Loopholes: Both models are 65% less likely to use shortcuts or loopholes to complete tasks compared to Sonnet 3.7, especially in agentic tasks.
• Enhanced Memory: Claude Opus 4, in particular, has significantly better memory capabilities. When given access to local files, it can create and maintain "memory files" to store key information, improving long-term task awareness. They even showcased Opus 4 creating a "Navigation Guide" while playing Pokémon!
• Thinking Summaries: For lengthy thought processes, a smaller model condenses them into summaries. This is only needed about 5% of the time. For those who need the nitty-gritty, raw chains of thought are available by contacting sales.
• Latest Knowledge: An interesting tidbit from the online discussions is that Claude 4's training cutoff date is March 2025, which is very recent compared to other models. This means it's more up-to-date with newer software packages and APIs, which can be a big deal for developers.

What Does This Mean for Everyday Users?

So, what's the takeaway for someone who isn't a hardcore AI developer?

1. Better Coding Assistance: If you dabble in coding or use AI for coding tasks, Claude 4, especially Opus 4, promises a much-improved experience. It's better at understanding complex code and can work on tasks for longer.
2. More Reliable AI Agents: The improvements in avoiding loopholes and better memory mean that AI agents built with Claude 4 could be more reliable and coherent over longer interactions.
3. Smarter Problem Solving: With enhanced reasoning capabilities, these models can be more helpful for a wider range of complex problems, not just coding.
4. Up-to-Date Information: The recent training cutoff means Claude 4 is less likely to give you outdated information, especially about rapidly evolving technologies.

The Community Buzz: What People Are Saying About Claude 4

The announcement of Claude 4 didn't just make a splash; it created a tidal wave of discussion across tech communities, especially on platforms like Hacker News. Let's dive deeper into what developers, AI enthusiasts, and everyday users are chattering about:

1. The All-Important Training Cutoff Date (March 2025): A Game Changer?

• Praise for Freshness: A significant point of excitement is Claude 4's March 2025 training data cutoff. It was highlighted that this is the latest of any recent model, with competitors like Gemini 2.5 Pro having an earlier cutoff (January 2025). This is a big deal because AI models can quickly become outdated, especially when it comes to rapidly evolving software libraries and APIs.
• Relevance for Developers: Many developers emphasized that this matters immensely for software packages, particularly Python packages related to AI programming, which evolve quickly with deprecations and updated documentation. Having to constantly correct for outdated knowledge in system prompts is a pain point Claude 4 aims to alleviate.
• Skepticism and Nuance: While some felt the exact cutoff month is becoming less relevant with the increasing availability of web search in LLMs, others pointed out that web search isn't always an option or desirable, and for specific API knowledge, a recent cutoff is still crucial. There were also discussions about how models "know" their cutoff date, with some suggesting it's often part of the system prompt rather than an inherent knowledge. Users shared experiences where Claude models gave different cutoff dates depending on the question, highlighting the complexities. For instance, one user asked Sonnet 4 about Tailwind CSS and was told its knowledge was up to v3.4 (cutoff Jan 2025), while another got an "April 2024" cutoff when asking who the president was.

2. Coding Prowess and Agentic Capabilities: Hype vs. Reality

• High Hopes for Coding Agents: The impressive SWE-bench and Terminal-bench scores, especially for Opus 4, have fueled optimism. There's hope that this could push features like GitHub's "Assign to CoPilot" closer to automating tasks like package upgrades, potentially reviving older projects by reducing the maintenance burden.
• Real-World Testing Begins: Users are already putting Claude 4 to the test. Some have shared results from SQL Generation Benchmarks where Opus 4 reportedly beat other models. However, it was also noted that Sonnet 4 surprisingly ranked below older Sonnet versions (3.7 and 3.5) on one specific benchmark, highlighting that performance can be nuanced and task-dependent.
• Early Experiences with Claude Code: Some shared initial struggles with Claude Code using Opus 4, encountering failing tool calls. However, this was quickly identified as a potential bug related to max output tokens and was reportedly being addressed urgently by Anthropic. This highlights the iterative nature of new releases and the active engagement between Anthropic and the user community.
• Memory and Long-Term Tasks: The "memory files" feature, allowing Opus 4 to store key information locally for long-term tasks (like the Pokémon playing example), caught attention. This is seen as a step towards more coherent and context-aware AI agents.

3. Comparisons with the Titans: Claude vs. GPT vs. Gemini

• Context Window Wars: A recurring theme is the comparison of Claude 4's context window (still 200k for Opus and Sonnet) with Gemini 2.5 Pro's massive 1 million token window. While some initially suggested the 1M context might have diminishing returns, other users strongly disagreed, stating Gemini 2.5 Pro shows impressive performance with large contexts in their coding agents. This remains a key differentiator for users dealing with very large codebases or documents.
• Feature Parity and Leapfrogging: The AI space is moving at breakneck speed. It's frequently noted how different models excel in different areas (e.g., Gemini for visual reasoning, Claude for coding). The sentiment is that these models are constantly leapfrogging each other, and what's state-of-the-art today might be overtaken tomorrow.
• "Thinking Summaries" - A Double-Edged Sword? Anthropic's decision to summarize lengthy chains of thought (CoT), with raw CoT available by contacting sales, sparked debate. Some lamented that all major LLM providers seem to be hiding or summarizing CoT, which was useful for debugging and refining prompts. Others speculated this might be to prevent distillation or because CoT itself can sometimes be a confabulation.

4. The "Vibe" and User Experience

• Brand Loyalty and Trust: Some users expressed a "brand loyalty" to Claude, finding it more trustworthy for coding tasks compared to other models. A common refrain was a preference for Claude when it comes to letting an AI handle actual work.
• Concerns about "Personality" Changes: At least one user expressed strong dislike for what they perceived as a new, overly enthusiastic "personality" in Claude Opus 4, comparing it to "ChatGPT at its worst" with over-the-top exclamations. This highlights how the perceived persona of an AI can significantly impact user experience.
• Rate Limits and Accessibility: Practical concerns like rate limits were also raised. One user mentioned getting rate-limited quickly when trying the new model in GitHub Copilot.

5. The Broader AI Landscape: Plateaus and Future Directions

• Incremental vs. Revolutionary: Some voiced opinions that recent LLM releases feel more like incremental improvements or "gimmicks" rather than massive leaps, suggesting a potential plateau in the current LLM architecture. Others countered that benchmarks like SWE-bench have shown significant jumps (e.g., from ~30-40% to ~70-80% pass rates this year), indicating continued progress.
• Focus on Tooling and Application: There's a growing understanding that even if core model intelligence isn't making order-of-magnitude jumps with every release, the improvements in tooling, agentic capabilities, and specific task performance (like coding) are still adding significant value.

The community's reaction to Claude 4 is a mix of excitement, cautious optimism, and critical evaluation. While the benchmark numbers are impressive, developers are now in the process of discovering how these new capabilities translate into real-world productivity and problem-solving. The discussions underscore a highly engaged user base eager to push the boundaries of what AI can do, while also keeping a keen eye on practical usability and the ever-shifting competitive landscape.

Conclusion: A Big Step Forward

Anthropic's Claude 4 models, Opus 4 and Sonnet 4, represent a significant advancement in AI, particularly in the realm of coding and complex reasoning. With leading performance on several benchmarks, enhanced memory, and a very recent knowledge cutoff, these models are poised to be powerful tools for developers and everyday users alike.

While the AI landscape is constantly evolving, Claude 4 has certainly made a strong statement. It will be exciting to see how these models are adopted and what new applications they enable.

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