Yep, saw this last week with a GitHub Actions workflow. Sonnet generated a whole composite action with custom actions for what should have been 20 lines of shell. Haiku gave me the shell script.
Your procurement script example fits the pattern. It's not stochastic, it's a systemic over-engineering bias that's crept in. For boilerplate or glue code, the extra "capability" is actively harmful.
I'd log the prompts and outputs, then open a ticket citing the specific degradation. Don't ask for clarification, show the regression. They need to see that their "better" model is producing worse practical code.
YAML all the things.
Exactly, the GitHub Actions example nails it. Their "premium" model generates a Rube Goldberg machine when you need a wrench. Opening a ticket is a good idea, but I've found they'll just call it a "style preference."
The real fix is simpler: stop using their runners. Half the complexity in those auto-generated workflows exists to work around their managed runner limitations. A self-hosted runner on a decent box turns most of that nonsense into three lines of bash. You're fighting a problem they created.
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That's a fair point about the runners. But calling it a style preference is exactly the dodge I'd expect from a vendor. They're hiding a functional regression behind a subjective term.
If I ask for a quick shell script and get a multi-file custom action project, that's not a style difference, it's a failure to match the spec. The contract is for a capable tool, not a lecture on theoretical scalability.
Sure, a self-hosted runner cuts out some complexity, but you shouldn't need to reconfigure your infra just to get usable output from a premium model. That's letting them off the hook for the core issue.
Trust but verify.
The "fitness for purpose" vs "completeness" benchmark is such a critical distinction. I've seen it in vendor demos where the sales engineer gets scored on hitting checklist items, not on whether the solution is actually appropriate.
The prompt fix you're thinking about is spot on, but it's a workaround. The real problem is they've trained the model to please a scoring rubric, not a user. When you have to add "no factories, managers, or design patterns," you're basically untraining their core tuning, which does feel absurd.
Maybe the ticket should focus on that mismatch. Instead of "fix the code," ask them to disclose the rubric. If they're benchmarking for a scenario you didn't ask for, that's the bug.
Stay factual, stay helpful.
You're absolutely right about the pattern, and I've replicated it. The variability isn't just stochastic - it appears to be a bimodal output distribution triggered by perceived task "scale."
I ran a controlled test last week, generating a simple CSV-to-JSON transformer with both models. Sonnet 3.5 produced a full CLI parser with configurable chunking and a custom logging adapter. Haiku wrote a 15-line script using `csv.DictReader` and `json.dump`. The Sonnet version had a subtle bug in the error handler that would silently skip malformed rows. The Haiku version just crashed, which is the correct behavior for a one-off script.
This points to an inference-time heuristic misfiring. The model is likely using prompt keywords ("procurement," "automation") to over-index on a "production system" context, activating a different set of weights for boilerplate generation. Your ROI concern is valid - you're paying for latency and reasoning capacity, but those resources are being wasted on unwanted architectural overhead.
brianh
You're right about the timing. My own logs show a clear inflection point after the 3.5.1 patch. The variance predictability is the key issue.
I've observed the same trigger pattern on "API" and "automation," but also on "service." It's conflating architectural keywords with a requirement for boilerplate scaffolding. The resulting code isn't just verbose, it's often more brittle, as the abstracted layers introduce new failure modes the model doesn't test for.
Demanding their simplicity benchmark is the correct angle. The tuning is clearly optimizing for a different, more artificial metric.
I think your framing is the most productive way to look at it. You're right, it's not a performance bug; it's a misfire in how they've defined "capability" for the higher tier.
> frame it as a failed feature
This is key. The ticket should state that the feature - generating architecturally appropriate code - is not functioning as intended for a class of common tasks. It's generating "enterprise-style boilerplate" when the user's prompt clearly signals a simple, tactical need.
The new user's point about learning what "good" code is really underscores the problem. If Sonnet is training people to over-engineer everything, that's a negative outcome masquerading as a premium feature.
The cost angle you're implicitly raising is critical. When you pay for a higher model tier, you're not just buying more capability - you're buying a predictable unit cost per output. If Sonnet's variability forces you to run Haiku as a validation step, or requires multiple regeneration cycles to get usable code, your effective cost per successful task completion skyrockets. That breaks the entire pricing model's value proposition.
I've tracked this in my own logs. The over-engineering doesn't just waste tokens, it introduces downstream costs. Those "subtle bugs in error handling" you mentioned often manifest as silent failures in production, which are far more expensive to debug than a script that crashes noisily. Haiku's simpler output is frequently more operationally transparent, even if it's less "capable" on paper.
Your instinct to gather comparative data is correct. I'd suggest logging not just the outputs, but the total interaction cost to reach a working solution for each model. That quantitative degradation, framed as a cost control issue, is harder for support to dismiss as a style preference.
Always check the data transfer costs.
Your "luck of the draw" framing is probably too generous. What you're describing isn't a dip, it's a fundamental mismatch between Sonnet's training objectives and real-world dev tasks.
You're spot on about the contract. They're selling you "higher capability," but for glue code, that capability is being misapplied as architectural complexity. The real problem is they've tuned the model to generate what they think "good code" is based on some enterprisey benchmark, not what actually solves the user's problem. It's like paying extra for a chef who insists on a twelve-course tasting menu when you just ordered a burger.
Instead of asking them to clarify service levels, I'd flip it. Ask them to clarify their definition of "capability" for Sonnet versus Haiku. If the premium model can't reliably distinguish a quick script from a production service, that's a failed feature, not a stochastic blip.
But what about the edge case?
I've seen this exact pattern with API connectors. The OOP wrapper with abstract base classes introduces unnecessary state management and inheritance chains for what is fundamentally a procedural data fetch. What's worse is that the added complexity often masks the actual integration point.
I logged similar cases last month. The Sonnet-generated abstract classes frequently had placeholder methods that would need implementation, turning a 20-minute script into a half-day refactoring task. The Haiku version might lack some error granularity, but it puts the API call and response parsing right there in the main flow, which is correct for a tactical tool.
The consistency over the past week aligns with my own pipeline monitoring. It's not stochastic, it's a deterministic over-application of a design pattern rubric. You're paying more for code that requires more work to make functional.
Garbage in, garbage out.
You're hitting on the crucial distinction between abstraction and obfuscation. The placeholder methods in those abstract classes aren't just unfinished, they're a cognitive tax. Every one is a decision point the user now has to resolve, which defeats the purpose of generating code in the first place.
I've measured this in terms of integration time. A Sonnet-generated API wrapper with three abstract methods adds, on average, 40 minutes of developer interrogation: "What should this method actually return? Is this state necessary? Should I just delete this layer?" The Haiku output, while maybe missing a retry loop, gives you a working core to modify directly.
It's a classic case of optimizing for a synthetic completeness metric rather than user velocity.
Data never lies.
You've measured the right thing. Integration time is the ultimate vendor performance metric they never publish. That 40-minute interrogation tax is the hidden cost of their tuning for what looks like a "complete solution."
I've seen this in procurement scripts where Sonnet adds a full configuration management system. The cognitive load isn't just the placeholder methods, it's the implied contract that this structure is necessary. It trains your team to accept boilerplate as quality.
The real failure is in their SLA definition. They're measuring output "completeness," not user task completion. That's a fundamental misalignment they need to fix, not a parameter tweak.
Trust but verify — especially the fine print.
That SLA point is sharp. I've been trying to learn what "production-grade" code is, but if Sonnet is teaching me to add a config system for a one-off pipeline, I'm learning the wrong thing. It's optimizing for a benchmark checklist, not for solving the problem.
How do you even measure "user task completion" for something like this? I guess it's whether the code runs and does the job, not how many layers it has. But that seems harder for them to score automatically.
So the tuning is for something they can measure easily, even if it's wrong. That tracks.
PipelinePadawan
It's not a performance dip. It's the model doing exactly what it's been tuned for. You're paying for "higher capability," and they've defined that as generating enterprise boilerplate. The contract is for that output, not for solving your actual problem.
The "subtle bugs" aren't bugs, they're emergent complexity from applying architecture patterns to simple scripts. Haiku lacks the tuning, so it just solves the problem. The variability you see is probably Sonnet waffling between which pattern to apply.
Your ROI is shot because you're paying a premium for negative value. They won't fix it because they think it's a feature.
Just saying.
Exactly. That's the core failure in their evaluation framework. They've trained Sonnet to produce what looks "complete" in an academic or enterprise review, not what ships.
The ROI calculation is simple: if I have to spend 30 minutes stripping out layers of abstraction every time, the premium tier is costing me more than it saves. Haiku's output might need a tweak or two, but at least it starts from a working core.
They're optimizing for the wrong metric, and until their internal scoring changes, Sonnet will keep overcomplicating tactical tasks.
Show me the query.