Anthropic recently unveiled Claude 4 (Opus and Sonnet), achieving record-breaking 72.7% performance on SWE-bench Verified and surpassing OpenAI’s latest models. Benchmarks aside, I wanted to see how Claude 4 holds up under real-world software engineering tasks. I spent the last 24 hours putting it through intensive testing with challenging refactoring scenarios.

I tested Claude 4 using a Rust codebase featuring complex, interconnected issues following a significant architectural refactor. These problems included asynchronous workflows, edge-case handling in parsers, and multi-module dependencies. Previous versions, such as Claude Sonnet 3.7, struggled here—often resorting to modifying test code rather than addressing the root architectural issues.

Claude 4 impressed me by resolving these problems correctly in just one attempt, never modifying tests or taking shortcuts. Both Opus and Sonnet variants demonstrated genuine comprehension of architectural logic, providing solutions that improved long-term code maintainability.

Key observations from practical testing:

• Claude 4 consistently focused on the deeper architectural causes, not superficial fixes.

• Both variants successfully fixed the problems on their first attempt, editing around 15 lines across multiple files, all relevant and correct.

• Solutions were clear, maintainable, and reflected real software engineering discipline.

I was initially skeptical about Anthropic’s claims regarding their models' improved discipline and reduced tendency toward superficial fixes. However, based on this hands-on experience, Claude 4 genuinely delivers noticeable improvement over earlier models.

For developers seriously evaluating AI coding assistants—particularly for integration in more sophisticated workflows—Claude 4 seems to genuinely warrant attention.

A detailed write-up and deeper analysis are available here: Claude 4 First Impressions: Anthropic’s AI Coding Breakthrough

Interested to hear others' experiences with Claude 4, especially in similarly challenging development scenarios.

Been using ChatGPT Plus with o3 and Gemini 2.5 Pro for coding the past months. Both are decent but always felt like something was missing, you know? Like they'd get me 80% there but then I'd waste time fixing their weird quirks or explaining context over and over or running in a endless error loop.

Just tried Claude 4 Opus and... damn. This is what I expected AI coding to be like.

The difference is night and day:

• Actually understands my existing codebase instead of giving generic solutions that don't fit

• Debugging is scary good - it literally found a memory leak in my React app that I'd been hunting for days

• Code quality is just... clean. Like actually readable, properly structured code

• Explains trade-offs instead of just spitting out the first solution

Real example: Had this mess of nested async calls in my Express API. ChatGPT kept suggesting Promise.all which wasn't what I needed. Gemini gave me some overcomplicated rxjs nonsense. Claude 4 looked at it for 2 seconds and suggested a clean async/await pattern with proper error boundaries. Worked perfectly.

The context window is massive too - I can literally paste my entire project and it gets it. No more "remember we discussed X in our previous conversation" BS.

I'm not trying to shill here but if you're doing serious development work, this thing is worth every penny. Been more productive this week than the entire last month.

Got an invite link if anyone wants to try it: https://claude.ai/referral/6UGWfPA1pQ

Anyone else tried it yet? Curious how it compares for different languages/frameworks.

EDIT: Just to be clear - I've tested basically every major AI coding tool out there. This is the first one that actually feels like it gets programming, not just text completion that happens to be code. This also takes Cursor to a whole new level!

I don't know what magic you guys did, but holy crap, Claude 4 opus is freaking amazing, beyond amazing! Anthropic team is legendary in my books for this. I was able to solve a very specific graph database chatbot issue that was plaguing me in production.

Rock on Claude team!

Been using these tools for the last few years. Can already tell opus and sonnet 4 have set a completely new benchmark, especially using Claude Code.

They just work, less hallucination, less infinite loops of confusion. You can set it off and come back with a 80-90% confidence it’s done what you asked. Maximum 3-4 iterations to get website/app component styling perfect (vs 5-10 before).

I’ve already seen too many of the classic ‘omg this doesn’t work for me they suck, overhyped’ posts. Fair enough if that’s your experience, but I completely disagree and can’t help but think your prompting is the problem.

Without using too much stereotypical AI hyperbole, I think these are the biggest step change since GPT 3.

The 200k context window is deflating especially when gpt and gemini are eating them for lunch. Even if they went to 500k would be better.

Benchmarks at this point in the A.I game are negligible at best and you sure don't "Feel" a 1% difference between the 3. It feels like we are getting to the point of diminishing returns.

Us as programmers should be able to see the forest from the trees here. We think differently than the normal person. We think outside of the box. We don't get caught in hype as we exist in the realm of research, facts and practicality.

This Claude release is more hype than practical.

I have spent at least 1000+ hours with LLMs last year. At least 100+ creating programs with GPT4, creating agents, doing all kinds of little research projects.

Claude is blowing me away, especially for coding tasks. There is no way I can use GPT4 now. Here is why, Claude has a much bigger context window.

In Claude I can paste in 5 , 200 line .python files (this is enough for millions of useful small projects if you are creative). GPT 4 as soon as you try this will summarize one into a few lines, ignore the rest. The comparison is night and day.

Then for code generation, it is getting almost impossible even using tricks to get GPT4 to generate a lot of code without spending a lot of time coaxing it. Claude will gladly write code until it can't... every single time if I ask it for full code it will spit out 200 lines of code.

Now for the understanding, it's just mind blowing. I can give it a fairly complex adjustment to the code and it will one-shot it, almost every time. GPT4 will take away hours of time coaxing it in the right direction. The amount of complexity Claude is able to one-shit given this much context length is otherworldly. Really I can't underestimate it.

The full potential is orders of magnitude above current public facing GPT4. It's probably mostly about context length and the amount of compute a company is willing to give someone for $20 / month. I worry that Anthropic scales it may have similar tradeoffs but for now earlier users can gain alpha and really use it.

This is really a dream. I truly feel it encroaching on human-level intelligence. I would personally pay $1000 / month if it gave me even more context, and more agentive and proactive behavior. I predict these companies will eventually release these advanced plans that rival a human salary but grant you much more compute, because it will be worth it.

I have been using Gemini 2.5 pro preview 05-06 and using the free credits because imma brokie and I have been having problems at coding that now matter what I do I can't solve and gets stuck so I ask Gemini to give me the problem of the summary paste it to Claude sonnet 4 chat and BOOM! it solves it in 1 go! And this happened already 3 times with no fail it's just makes me wish I can afford Claude but will just have to make do what I can afford for now. :)

Source: Code with Claude Opening Keynote

I’m in a unique situation where I’m a non-technical founder trying to become technical.

I had a CTO who was building our v1 but we split and now I’m trying to finish the build. I can’t do it with just AI - one of my friends is a senior dev with our exact tech stack: NX typescript react native monorepo.

The status of the app was: backend about 90% -100% done (varies by feature), frontend 50%-70% plus nothing yet hooked up to backend (all placeholder and mock data).

Over the last 3 weeks, most of the progress was by by friend: resolving various build and native dependency issues, CI/CD, setting up NX, etc…

I was able to complete onboarding screens + hook them up to Zustand (plus learn what state management and React Query is). Everything else was just trying, failing, and learning.

Here comes Claude 4. In just 1 days (and 146 credits):

Just off of memory, here’s everything it was able to do yesterday

1. Fully document the entire real-time chat structure, create a to-do list of what is left to build, and hook up the backend. And then it rewrote all the frontend hooks to match our database schema. Database seeding. Now messages are sent and updated in real time and saved to the backend database. All varied with e2e tests.

2. Various small bugs that I accumulated or inherited.

3. Fully documented the entire authentication stack, outlined weaknesses, and strength, and fixed the bug that was preventing the third-party service (S3 + Sendgrid) from sending the magic link email.

We have 100% custom authentication in our app and it assessed it as very good logic but and it was missing some security features. Adding some of those security features require required installing Redix. I told Claude that I don’t want to add those packages yet. So that it fully coded everything up, but left it unconnected to the rest of the app. Then it created a readme file for my friend/temp CTO to read and approve. Five minutes worth of work remaining for CTO to have production ready security.

4. Significant and comprehensive error handling for every single feature listed above.

5. Then I told her to just fully document where we are in the booking feature build, which is by far the most complicated thing across the entire app. I think it wrote like 1500 to 2000 lines of documentation.

6. Finally, it partially created the entire calendar UI. Initially the AI recommended to use react-native-calendar but it later realized that RNC doesn’t support various features that our backed requires. I asked it to build a custom calendar based on our existing api and backend logic- 3 prompts layers it all works! With Zustand state management and hooks. Still needs e2e testing and polish but this is incredible output for 30 mins of work (type-safe, error handling, performance optimizations).

Along side EVERYTHING above, I told it to treat me like a junior engineer and teach me what it’s doing.I finally feel useful.

Everything sent as a PR to GitHub for my friend to review and merge.

I am a senior dev of 10 years, and have been using claude code since it's beta release (started in December IIRC).

I have seen countless posts on here of people saying that the code they are getting is absolute garbage, having to rewrite everything, 20+ corrections, etc.

I have not had this happen once. And I am curious what the difference is between what I am doing and what they are doing. To give an example, I just recently finished 2 massive projects with claude code in days that would have previously taken months to do.

1. A C# Microservice api using minimal apis to handle a core document system at my company. CRUD as well as many workflow oriented APIs with full security and ACL implications, worked like a charm.

2. Refactoring an existing C# API (controller MVC based) to get rid of the mediatr package from within it and use direct dependency injection while maintaining interfaces between everythign for ease of testing. Again, flawless performance.

These are just 2 examples of the countless other projects im working on at the moment where they are also performing exceptionally.

I genuinely wonder what others are doing that I am not seeing, cause I want to be able to help, but I dont know what the problem is.

Thanks in advance for helping me understand!

Edit: Gonna summarize some of the things I'm reading here (on my own! Not with AI):

- Context is king!

- Garbage in, Garbage out

- If you don't know how to communicate, you aren't going to get good results.

- Statistical Bias, people who complain are louder than those who are having a good time.

- Less examples online == more often receiving bad code.

I'm a new user, not sure why it took me so long to give AI a real try. In the past couple of weeks Sonnet 3.5 has helped me with a whole bunch of work. I'm a freelance software engineer/data scientist. Instead of googling or reading docs or stackoverflow, I have just been asking Claude and I'd estimate it's increased my output per unit time by several hundred percent. Yeah I have to debug and sometimes it gets things a little wrong but having Claude at my fingertips feels like a superpower. How are you guys using Claude for your computer work?

I used to think cursor was pretty average and not super helpful, but Claude code with opus 4 takes longer and seems to be a lot better at generating quality code without needing to spec every single requirement.

I still do review the code but I feel like I’m trusting it more because the quality is better.

Interested to hear your thoughts

I've been using Claude Code extensively since its release, and despite not being a coding expert, the results have been incredible. It's so effective that I've been able to handle bug fixes and development tasks that I previously outsourced to freelancers.

To put this in perspective: I recently posted a job on Upwork to rebuild my app (a straightforward CRUD application). The quotes I received started at $1,000 with a timeline of 1-2 weeks minimum. Instead, I decided to try Claude Code.

I provided it with my old codebase and backend API documentation. Within 2 hours of iterating and refining, I had a fully functional app with an excellent design. There were a few minor bugs, but they were quickly resolved. The final product matched or exceeded what I would have received from a freelancer. And the thing here is, I didn't even see the codebase. Just chatting.

It's not just this case, it's with many other things.

The economics are mind-blowing. For $200/month on the max plan, I have access to this capability. Previously, feature releases and fixes took weeks due to freelancer availability and turnaround times. Now I can implement new features in days, sometimes hours. When I have an idea, I can ship it within days (following proper release practices, of course).

This experience has me wondering about the future of programming and AI. The productivity gains are transformative, and I can't help but think about what the landscape will look like in the coming months as these tools continue to evolve. I imagine others have had similar experiences - if this technology disappeared overnight, the productivity loss would be staggering.

Since ChatGPT came out about a year ago the way I code, but also my productivity and code output has changed drastically. I write a lot more prompts than lines of code themselves and the amount of progress I’m able to make by the end of the end of the day is magnitudes higher. I truly believe that anyone not using these tools to code is a lot less efficient and will fall behind.

A little bit o context: I’m a full stack developer. Code mostly in React and flaks in the backend.

My AI tools stack:

Claude Opus (Claude Chat interface/ sometimes use it through the api when I hit the daily limit)

In my experience and for the type of coding I do, Claude Opus has always performed better than ChatGPT for me. The difference is significant (not drastic, but definitely significant if you’re coding a lot).

GitHub Copilot

For 98% of my code generation and debugging I’m using Claude, but I still find it worth it to have Copilot for the autocompletions when making small changes inside a file for example where a writing a Claude prompt just for that would be overkilled.

I don’t use any of the hyped up vsCode extensions or special ai code editors that generate code inside the code editor’s files. The reason is simple. The majority of times I prompt an LLM for a code snippet, I won’t get the exact output I want on the first try. It of takes more than one prompt to get what I’m looking for. For the follow up piece of code that I need to get, having the context of the previous conversation is key. So a complete chat interface with message history is so much more useful than being able to generate code inside of the file. I’ve tried many of these ai coding extensions for vsCode and the Cursor code editor and none of them have been very useful. I always go back to the separate chat interface ChatGPT/Claude have.

Prompt engineering

Vague instructions will product vague output from the llm. The simplest and most efficient way to get the piece of code you’re looking for is to provide a similar example (for example, a react component that’s already in the style/format you want).

There will be prompts that you’ll use repeatedly. For example, the one I use the most:

Respond with code only in CODE SNIPPET format, no explanations

Most of the times when generating code on the fly you don’t need all those lengthy explanations the llm provides before/after the code snippets. Without extra text explanation the response is generated faster and you save time.

Other ones I use:

Just provide the parts that need to be modified

Provide entire updated component

I’ve the prompts/mini instructions I use saved the most in a custom chrome extension so I can insert them with keyboard shortcuts ( / + a letter). I also added custom keyboard shortcuts to the Claude user interface for creating new chat, new chat in new window, etc etc.

Some of the changes might sound small but when you’re coding every they, they stack up and save you so much time. Would love to hear what everyone else has been implementing to take llm coding efficiency to another level.

As a subscriber to both Claude and ChatGPT, I've been comparing their performance to decide which one to keep. Here's my experience:

Coding: As a programmer, I've found Claude to be exceptionally impressive. In my experience, it consistently produces nearly bug-free code on the first try, outperforming GPT-4 in this area.

Text Summarization: I recently tested both models on summarizing a PDF of my monthly spending transactions. Claude's summary was not only more accurate but also delivered in a smart, human-like style. In contrast, GPT-4's summary contained errors and felt robotic and unengaging.

Overall Experience: While I was initially excited about GPT-4's release (ChatGPT was my first-ever online subscription), using Claude has changed my perspective. Returning to GPT-4 after using Claude feels like a step backward, reminiscent of using GPT-3.5.

In conclusion, Claude 3.5 Sonnet has impressed me with its coding prowess, accurate summarization, and natural communication style. It's challenging my assumption that GPT-4 is the current "state of the art" in AI language models.

I'm curious to hear about others' experiences. Have you used both models? How do they compare in your use cases?