Claude Sonnet 4 Review - Is It Actually Worth Switching?

Look, I'll cut through the marketing bullshit. Sonnet 4 is noticeably better at coding tasks. My usual debugging tests went from failing about 40% of the time to maybe 25-30%. Noticeable difference when you're trying to actually ship code.

Yeah, the benchmarks actually match what I'm seeing - Sonnet 4 scores 72.7% compared to 3.7's 62.3% on real software engineering tasks. GPT-4.1 only hits 54.6% on the same stuff.

The Coding Got Way Better

Here's what changed: Sonnet 4 can actually follow complex debugging sessions without losing the thread. I had this React component that was throwing weird state update errors - kept getting the dreaded "Cannot update a component while rendering a different component" error. Fed the full component to 3.7 and it gave me generic advice about useEffect dependencies. Sonnet 4 immediately spotted the race condition in my state setter that was causing the cascade.

The big win is that it doesn't lose track of what it's doing in large codebases. I dumped a 2000-line file into it, asked for refactoring, and it actually remembered the architecture. 3.7 would forget the beginning by the time it reached the end.

GitHub started using it for Copilot, so it's definitely not just hype.

Extended Thinking Mode is Actually Useful

The "thinking" feature sounds gimmicky but it's actually useful for gnarly problems. When I give it a tricky algorithm problem or ask it to debug something with multiple interacting systems, the extra thinking time produces way better results than instant responses.

Downside: it's slow as hell. If you're trying to iterate quickly on simple problems, the 5-10 second thinking delay gets annoying fast. I use quick mode for basic stuff and extended thinking for the complex debugging.

Same Price, Better Output Limits

They didn't raise the API pricing ($3 input/$15 output per million tokens), which is nice because API bills are brutal enough already. The big improvement is the output limit - went from 8k tokens to 64k tokens. This means it can actually complete large code generation tasks without getting cut off mid-function.

I was working on a data migration script that needed to handle 15 different edge cases. With 3.7, I'd get halfway through and hit the token limit. Sonnet 4 spit out the complete 800-line script in one go. 64k tokens is roughly 50,000 words of output - enough for entire application modules.

Where It Still Sucks

It's not perfect. Sometimes it gets overly verbose when you give it a lazy prompt. Ask it to "make this code better" and you'll get 500 lines of overengineered garbage with dependency injection patterns you didn't ask for. You need to be specific about what you actually want.

It also makes up documentation sometimes. Spent 2 hours debugging one of its "solutions" that didn't work. It suggested using a useState pattern with async/await that causes infinite re-renders. Spent hours debugging why my component kept crashing before realizing async state updates don't work that way in React. Now I always double-check API references. This thing will confidently suggest patterns that look right but break in practice.

I've been running Sonnet 4 in production for our team's internal tools for about 2 months now. Here's what actually happens when you deploy this thing at scale.

API Integration Was Dead Simple

API upgrade was actually painless - just changed the model parameter from claude-3-7-sonnet to claude-4-sonnet and it worked. Same auth, same endpoint, same billing. If you're already using the Anthropic API, upgrading is stupidly simple.

Same request format - just swap the model parameter. No breaking changes, no new auth flows, no migration scripts needed.

The response times are pretty consistent - usually 2-4 seconds for normal requests. Extended thinking mode can take 10-15 seconds for really complex problems, which is too slow for user-facing features but fine for background processing.

Context Window Actually Works

The 200k token context window isn't marketing fluff. I regularly dump entire codebases into it - like our 15,000 line Express.js API - and ask it to explain specific architectural decisions. It maintains context about the full codebase structure while answering questions about individual functions.

Had a nasty bug in our authentication middleware that was causing random 401 errors. Pasted the entire auth flow (about 800 lines across 4 files) and it immediately identified a race condition between our JWT refresh logic and session management. Would have taken me hours to find that manually.

GPT-4's context window caps out at 128k tokens, and Gemini 2.5 Pro starts losing coherence around 100k tokens in my experience. Claude maintains context quality better at scale.

Output Limits Actually Matter

The 64k token output limit is a game changer. I was building a data migration tool and asked it to generate the full SQL schema migration with error handling. With 3.7, I'd get cut off halfway through and have to ask for continuation. Sonnet 4 generated 1,200 lines of complete, working SQL with proper rollback procedures.

When It Breaks Down

Here's where it shit the bed: complex multi-step deployments with lots of external dependencies. Asked it to write a complete Docker deployment for our microservices architecture with proper networking, secrets management, and health checks. The output looked perfect but assumed we were using Docker Swarm instead of Kubernetes. Spent 3 hours debugging why the networking config was completely fucked.

It also struggles with newer frameworks or recently updated APIs. Asked it about Next.js 15's new caching behavior and got outdated information from the training data. It suggested using unstable_cache() which broke in production because that API changed between React 19 canary builds. Always double-check the APIs it references - the training data has gaps and it doesn't know it.

Docker best practices change frequently, and Claude often suggests outdated approaches. I always cross-reference with official documentation.

Cost Reality

Been running this for 2 months across our 8-person engineering team. Monthly API costs are around $180, which breaks down to about $22 per developer per month. That's less than we spend on coffee, and it's saved us way more than $22 worth of debugging time per person.

The pricing is actually predictable because you can estimate token usage pretty well. Code generation tasks cost more (longer outputs), but debugging and analysis tasks are usually cheap.

For comparison, Cursor Pro is $20/month and actually integrates with your IDE. This is cheaper but requires copy-pasting everything. GitHub Copilot is $10/month but honestly feels pretty basic compared to this.

Team Adoption Patterns

Half our team still forgets this exists and debugs shit manually for hours. The devs who actually use it get stuff done way faster. The ones who ignore it are still printf debugging like it's 2010.

Biggest productivity gain: code reviews. Instead of spending 2 hours understanding a complex PR, I paste the diff into Sonnet 4 and get a solid architectural analysis in 30 seconds. Then I focus my human review time on business logic and edge cases.

The context window is great until you hit some weird edge case and it gives you 50 lines of useless suggestions. But when it works, it works really well.

For team adoption, I recommend starting with optional usage and letting early adopters demonstrate value before mandating it. Half our team jumped on it immediately, the other half needed to see the results first.