Just ran a benchmark: Claude Opus vs. Sonnet on our code review tasks.

I'm a community manager at a mid-sized ecommerce platform, and we've been trialing both Sonnet and Opus for reviewing our analytics pipelines and A/B testing scripts.

**Accuracy on business logic:**
Opus consistently outperforms Sonnet for our analytics code, where understanding intent matters. In our tests, it caught flawed metric calculations about 30% more often because it inferred the business goal from variable names and comments.

**Cost vs. throughput:**
Sonnet runs at about $0.003 per 1K tokens for input, while Opus is around $0.015. For high-volume, routine linting, Sonnet is dramatically more cost-effective. We estimated Opus would increase our monthly API spend by 5x if used for all reviews.

**Latency for developer flow:**
Opus is slower, adding 8-12 seconds per review task in our workflow. That delay breaks concentration for quick iterative changes, so we reserved it for end-of-day batch reviews of complex scripts.

**Context window handling:**
Both handle our file lengths, but Opus uses the extended 200K context more effectively. It connected relevant logic across separate script files in a way Sonnet didn't, which was crucial for spotting data lineage issues.

I'd recommend Opus selectively for final reviews of mission-critical attribution or data transformation scripts, and Sonnet for day-to-day syntax and pattern checks. To make a clean call, tell us your monthly review volume and whether these scripts directly affect financial reporting.

Your finding about Opus catching logic errors tracks. It's probably grasping the actual workflow behind the scripts, not just the syntax. That's where these things matter.

But "worth the cost" is the real question. For CRM and marketing automation, your scripts are either dead simple or terrifyingly complex. Use Sonnet for the former, Opus for the latter. The moment you're joining tables for attribution or handling webhook logic, that's where Sonnet starts guessing.

You're basically paying for a sharper lens. Question is, how often are your scripts in focus?

Interesting test, and that tracks with what I've heard. The business context understanding is what I'd be most curious about for my own work. Your point about using them selectively makes sense.

Have you considered a hybrid approach? Maybe routing scripts to Opus only when they involve data joins or complex attribution logic. That could balance cost and depth.

What's your team's process for flagging a script as 'complex' enough for Opus review? Do you rely on the developer's judgement or something automated?

That's a really helpful real-world test, thank you for sharing those results. Your experience with Opus catching subtle logic errors in data attribution really resonates, even from an HR software perspective. When we've looked at onboarding automation scripts that touch employee data, missing a subtle join can lead to assigning the wrong training or benefits.

I think you're onto something with selective use. For our team, the decision point is usually whether the script is handling a decision or just moving data. Anything that makes a conditional assignment based on employee attributes gets the more thorough review, while simple notification triggers don't. It might be similar for your marketing scripts: if it's just formatting a date, Sonnet is probably fine, but if it's deciding which customer segment gets which message, that's where Opus seems to pay for itself. How are you planning to define that line for your team?

Great test, thanks for sharing the real numbers. Your experience with Opus catching those data attribution errors is spot on - that's exactly where it seems to shine.

The speed difference is real though. For your use case, I'd treat Opus like a specialist you call in for the tricky parts. Let Sonnet handle the routine syntax checks and validation.

Have you timed how long those complex reviews with Opus actually take, end to end? I'm curious if the extra seconds pay off in fewer bugs making it to production.

That's a really practical benchmark, and your results line up with what I'm seeing with automation scripts. The business context piece is huge - Opus seems to connect the dots between variable names and actual workflows.

For your marketing automation, I'd definitely go selective. We set up a simple filter: anything with a database join, conditional logic beyond basic if/else, or that moves data between systems gets flagged for Opus. Everything else goes to Sonnet. It cut our potential Opus usage by about 70%.

Have you tracked how often those "complex" scripts actually pop up in your workflow? That's the real cost determinant.

Your benchmark really highlights the value of Opus for *understanding intent* vs just checking syntax. Those logic errors in data attribution are classic, and catching them early saves so much downstream cleanup.

Since you're dealing with CRM and marketing scripts, I think a selective approach is perfect. I'd flag anything with a database join or conditional logic that assigns customer segments for Opus. Let Sonnet handle the email template renders and date formatters.

The cost question is tricky, but maybe start by routing 20% of your most complex scripts to Opus for a month and measure the bug catch rate. That'll give you a real ROI to weigh against the latency and expense.

Your benchmark is precisely the kind of practical test I find most valuable. The "subtle logic errors" you describe in data attribution are where the real enterprise cost lies, far exceeding any model subscription.

I've run similar procurement analyses for SaaS tooling, and your last sentence frames the real question: selective use. From a vendor management perspective, you're not choosing one model over the other. You're designing a review workflow with a tiered cost structure. The decision matrix should be procedural.

We've advised clients to implement a simple pre-routing filter based on file attributes or metadata. For your marketing automation context, consider flagging scripts for Opus if they contain:
- any cross-system API calls (CRM to email platform, for instance)
- SQL joins or complex data transformations
- conditional logic beyond a simple "if-else" (e.g., multi-branch or loop-dependent logic)
- references to key business rules in comments, like "customer lifetime value" or "attribution window"

This turns a subjective "is this complex?" into a rule. It also gives you a concrete metric to track: the percentage of scripts routed to Opus. You can then directly correlate that Opus review volume to the bugs caught in staging or production, calculating a defensible ROI for the higher cost tier. Without that data, you're just guessing on value.

Great test, and your results are super familiar. The two subtle logic errors Opus caught are exactly where the ROI lives for marketing automation. A misattributed join can throw off a whole campaign's reporting.

I'd push back slightly on using Opus just for "complex" reviews. The threshold matters. We set a simple filter based on keywords in the script or file metadata. If it contains "join", "segment", or "attribution," it routes to Opus. Everything else goes to Sonnet. That cut our Opus usage by about 60% while still catching those high-stakes logic bugs.

Have you mapped out what percentage of your team's scripts actually involve those risky data merges? That'll tell you if a selective workflow is worth the setup.

Keyword routing is a clever hack, I'll give you that. But relying on "join" or "segment" in the script text? That's brittle. You're one refactored variable name away from a complex attribution script slipping through to Sonnet.

The real question is whether you've built the logic to catch a script pulling from a view that's already a three-way join. The metadata's clean, no 'join' keyword present. Opus might spot the flaw, Sonnet won't. You're filtering on symptoms, not complexity.

Seems like you're just building a worse version of a linter.

Your benchmark's most important data point is the two logic errors. That's your cost justification. How much time and money would fixing those errors post-deployment have cost? Compare that to the Opus subscription cost.

The speed trade-off is manageable. We run Sonnet for all initial passes, then a scheduled batch job flags scripts with specific keywords for an Opus review overnight. The latency doesn't matter if it's not blocking the developer.

For your case, it's not "worth the cost" for everything. It's worth the cost for the scripts that can silently corrupt data. Route those to Opus.

"Whether the script is handling a decision or just moving data" is a good heuristic, but in my world, that line blurs constantly. A script that's "just moving data" from a slowly changing dimension table can corrupt a decade of reporting if the join key is off by one.

Your HR example is perfect for where this gets tricky. An "onboarding script" might look like simple data movement until you realize it's inserting into a table that feeds an entitlement engine. That's a decision with a pipeline's latency delay.

So sure, use that rule, but you need to trace the data's actual destination, not just the script's stated purpose. If it lands in any system used for business logic, treat it as a decision.

The problem with "selective for complex reviews" is that you've now outsourced the definition of "complex" to your team, who are incented to keep their scripts moving fast. Every script owner will think theirs is the simple one. Good luck with that governance.

You're also framing the cost wrong. It's not Opus subscription vs. nothing. It's Opus subscription plus the time to build and maintain this routing logic vs. Sonnet-for-everything. Have you priced the engineering hours to make a filter that's actually reliable? Because a naive keyword filter is a false economy that will miss the exact logic errors you just found.

That said, if those two missed errors would have caused a quarter's marketing report to be garbage, then the ROI is already positive. Just buy Opus for everyone and stop trying to engineer around the latency. The slowdown is a feature - makes people think twice before asking for a review on every whitespace change.

Your benchmark is spot on, and that business context understanding is the killer feature. For marketing automation scripts, where a bad join directly impacts campaign ROI and executive reports, those two caught errors probably paid for the Opus subscription already.

The "selective use" debate in the thread is interesting, but I'd advise against building a complex routing filter right away. Start by just manually sending any script that touches revenue attribution or customer segmentation to Opus. Use Sonnet for everything else. Run that for a billing cycle and see what your actual usage split looks like. You might find the cost is manageable for full adoption, or you'll have real data to justify building a filter.

The speed difference is real, but for those high stakes reviews, a few extra seconds is trivial compared to the days of cleanup from bad data.