I've been using Claude Opus 4.5 daily since it launched in late 2025. Not for testing, not for benchmarking — for building things. Agent systems, long-form writing, architecture decisions. The kind of work where you find out quickly whether a model is actually better or just better at demos.
It's better. Not uniformly, not without caveats, but in ways that change how I work.
The Numbers That Matter
The benchmark story is strong:
- Graduate-Level Reasoning (GPQA): 73.3% (vs 61.9% for Opus 3)
- Coding (SWE-Bench): 52.6% (vs 33.4% for GPT-4)
- Mathematical Reasoning (MATH-500): 81.2% (vs 60.1% for previous best)
- Context Window: 200K tokens with real recall
- Processing Speed: 2.3x faster than Opus 3 despite larger size
But I've learned not to take benchmarks as gospel. A model can ace SWE-Bench and still generate code that doesn't fit your actual codebase. The numbers are a signal, not a verdict. What matters is whether the improvements translate into fewer iterations, less debugging, and better first-pass output when you're actually shipping something.
They do. Mostly.
What's Actually Different
Multi-step reasoning that doesn't lose the thread
This is the change I notice most. Previous models would hold a complex problem for maybe three or four steps before drifting — forgetting a constraint, contradicting an earlier assumption, or just producing output that sounded confident but didn't connect back to the actual question.
Opus 4.5 holds coherence significantly longer. I can describe a system with five or six interacting components, specify constraints across all of them, and get back a design that actually respects the full problem space. Not always correctly — but coherently. That distinction matters. A coherent wrong answer is something you can debug. An incoherent one is just noise.
The practical effect: I spend less time re-explaining context mid-conversation and more time actually evaluating the output. That's a meaningful workflow change.
Code that compiles on first try (usually)
The coding improvement is real but worth being honest about. Opus 4.5 generates code that compiles more often than any model I've used. For system design — not just individual functions, but how components fit together — the improvement is substantial.
But "compiles" isn't the same as "works in production." I still review everything. Edge cases, error handling, assumptions about the environment — these are where models still fail in ways that matter. The difference is that with Opus 4.5, I'm reviewing for subtle issues rather than fixing basic structural problems. That's a real upgrade in how I use it day to day.
Cross-domain synthesis
The model makes connections across domains that I wouldn't have reached on my own. Sometimes it's a stretch — pulling in concepts from unrelated fields that sound insightful but don't actually apply. But often enough, it surfaces an approach I hadn't considered. The signal-to-noise ratio on creative suggestions is noticeably better than previous versions.
This is hard to benchmark. It's a qualitative difference you notice over weeks of use, not something a test captures. But it's one of the reasons I default to Opus for architecture decisions and hard design problems.
The Competitive Landscape
Here's where I'll be honest: I use Claude as my primary tool. That creates bias. I've spent more hours in Opus than in any other frontier model, so I know its strengths better and I've adapted my workflows to it. With that caveat —
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Opus 4.5 vs GPT-4.5: Opus handles longer reasoning chains better and maintains coherence across complex tasks. GPT-4.5 is faster for short interactions and has stronger multimodal capabilities. If you're doing quick Q&A with images, GPT probably wins. If you're working through a multi-step design problem, Opus pulls ahead.
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Opus 4.5 vs Gemini 2.0: Gemini's Google ecosystem integration is tighter — search grounding, workspace tools, that whole stack. Opus produces better code and handles nuanced instructions more reliably. The trade-off is ecosystem vs. reasoning quality.
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Opus 4.5 vs Llama 4: Different category. Llama is open-source and self-hostable, which matters enormously for privacy, cost at scale, and the principle of the thing. Opus is significantly more capable on complex reasoning. But if you're building infrastructure you want to control end-to-end, Llama's openness is a feature that no amount of capability can substitute for.
These are practitioner impressions from daily use. Not formal evals. Your priorities will determine which trade-offs matter.
The Economics
Check Anthropic's pricing page for current rates — anything I write here goes stale within weeks.
The general shape: Opus is the most expensive Claude model, significantly more than Sonnet. Whether the premium justifies itself depends entirely on what you're doing with it.
I've settled into a clear pattern — Opus for architecture decisions, complex reasoning, long-context work, and anything where getting a bad first pass costs me more time than the API price. Sonnet for routine tasks, quick questions, and high-volume work where good-enough is good-enough.
The cost difference is real if you're making a lot of API calls. For the problems where I actually need Opus-level reasoning, the premium pays for itself in fewer iterations. For everything else, it's an expensive habit.
Limitations
The model still hallucinates. Less often, more subtly — which is almost worse, because the hallucinations are harder to catch. Confident, well-structured, totally wrong. You need to verify anything that matters.
Knowledge cutoff is still a hard constraint. The model doesn't know about things that happened after its training data closes. This sounds obvious until you forget and take its word on something recent.
The 200K context window is real, and recall across that window is genuinely good. But it's not infinite, and there's still degradation at the edges. For very long documents, the model attends to the beginning and end more reliably than the middle. You can work around this with structure, but it's a constraint worth knowing about.
And cost. Opus is expensive. For an individual developer, the API costs add up fast. For teams, you need to think about when Opus-level capability is actually required versus when you're paying a premium out of habit.
The Honest Take
Opus 4.5 is a real step forward. Not a paradigm shift — the model still does the same kind of thing as its predecessors, just better. But "better" compounds. Fewer iterations per task, less context re-explanation, more reliable first-pass output. Over weeks of daily use, that adds up to a meaningfully different workflow.
The question I keep coming back to isn't about the model's capabilities. It's about what happens when the gap between "good enough first pass" and "production ready" keeps shrinking. We're not there yet — not close — but the trajectory is pointing somewhere that changes what it means to build software. I don't know exactly where that lands. Nobody does.
The model is a better tool. It's not a replacement for judgment, taste, or knowing what to build in the first place. Those are still on you.