I've been using Claude Opus 4 as my primary coding model for three weeks. Not casual use. Heavy, all-day, production-code writing. Here's what I think.

Context Window: The Headline Feature

Opus 4 has a massive context window, and more importantly, it actually uses it well. Previous models would technically accept large contexts but lose track of details past a certain point. Opus 4 maintains coherence across very long conversations in a way that feels qualitatively different.

In practice, this means I can load an entire module (3,000+ lines) and ask questions that reference relationships between functions hundreds of lines apart. The model tracks these references accurately. With Opus 3.5, I'd often have to re-state important context. With Opus 4, it just remembers.

The practical impact: fewer correction loops. I spend less time saying "no, I meant the other function" or "remember the constraint I mentioned earlier." The conversation flows more naturally.

Reasoning Quality

This is where Opus 4 really separates from the competition. When I ask it to analyze a complex piece of code, it doesn't just describe what the code does. It reasons about edge cases, identifies potential race conditions, and connects pieces of logic that are far apart in the codebase.

A specific example: I asked it to review a caching implementation. It identified that the cache invalidation logic had a subtle timing window where stale data could be served, but only under high concurrent load. It explained the exact sequence of events that would trigger the bug and proposed a fix using a version counter. That's the kind of analysis I'd expect from a senior engineer who's been burned by caching bugs before.

GPT-5 would have caught the bug too, but its explanation was less precise about the timing. Gemini 2 Pro missed it entirely and said the implementation "looks correct."

Hallucination Rate

Noticeably lower than any previous Claude model. I track hallucinations informally (wrong API calls, nonexistent functions, incorrect library behavior), and Opus 4 hallucinates maybe once or twice per day of heavy use. Opus 3.5 was about 4-5 times per day. GPT-5 is comparable to Opus 4 on hallucination rate.

The bigger improvement: when Opus 4 is uncertain, it says so. "I'm not sure about the exact API for this library version, you should verify" is a response I get occasionally. That's infinitely better than a confident hallucination.

Code Generation Quality

The code Opus 4 generates is consistently production-ready. Not "demo quality that needs cleanup," but actual code I commit without modification about 70% of the time. The remaining 30% needs minor adjustments, usually project-specific conventions that aren't captured in my CLAUDE.md.

Key improvements over previous versions:

  • Error handling is comprehensive by default. It doesn't just catch errors, it handles them appropriately for the context. API endpoints get proper HTTP status codes. Service functions throw typed errors. Background jobs get retry logic.
  • Types are precise. No more any types unless genuinely necessary. Union types are narrow. Generic constraints are accurate.
  • Tests are meaningful. Test names describe behavior, not implementation. Edge cases are covered without being asked. The test structure matches the project's existing patterns.

Speed

Opus 4 is slower than Sonnet for simple tasks. For a quick function generation, Sonnet responds in 2-3 seconds. Opus 4 takes 5-8 seconds. For complex tasks involving reasoning, the gap narrows because Opus gets it right on the first attempt more often, saving retry time.

My approach: I use Sonnet for quick, simple tasks (generate a type, write a utility function) and Opus 4 for anything that requires understanding context or making decisions (reviewing code, refactoring, debugging, multi-file changes). This is the sweet spot for both cost and speed.

Where It Falls Short

Very new libraries. Despite improvements, Opus 4's training data has a cutoff. Libraries released in the last few months will get hallucinated APIs. I maintain a docs/ directory with API references for new dependencies and reference them in my CLAUDE.md.

Large-scale architectural reasoning. Opus 4 is great at understanding existing architecture and working within it. It's less reliable at proposing new architectures from scratch. I still do high-level design thinking myself and use Opus 4 for implementation.

Cost. Opus 4 is expensive through the API. For teams using it heavily, the bill adds up. The Claude Code subscription is more predictable, but you're limited by the Max plan's usage policies.

The Verdict

Claude Opus 4 is the best coding model available right now. The combination of large effective context, strong reasoning, low hallucination rate, and production-quality code generation makes it my default for any task that matters. GPT-5 is a strong second choice, especially for tasks where speed matters more than depth. Gemini 2 Pro is good for multimodal tasks but falls behind on pure coding.

If you're doing serious development work with AI, Opus 4 is worth the investment. The time savings from fewer corrections and higher first-attempt accuracy more than justify the cost.