I needed to pick a Claude model for production.
Three options. Three price points. Three levels of capability.
So I tested them against real code. Not benchmarks. Real problems.
The Quick Numbers
Opus 5.5:
- 12 seconds response time
- $0.042 per request
- 95% accuracy
- Exceptional depth
Sonnet 5.5:
- 9 seconds response time
- $0.011 per request
- 90% accuracy
- Practical recommendations
Haiku 5.5:
- 4 seconds response time
- $0.001 per request
- 80% accuracy
- Surface-level but useful
The Uncomfortable Truth
At scale (10,000 requests/day):
- Opus: $420/day ($153K/year)
- Sonnet: $110/day ($40K/year)
- Haiku: $10/day ($3.6K/year)
Most developers default to Opus because "it's the best."
But they're throwing away money.
What I Actually Found
Opus wins on ~10% of tasks: multi-step reasoning, complex refactoring, novel problems.
For everything else? Sonnet crushes it. Cheaper, faster, good enough.
And Haiku? The benchmarks undersell it. For real-time systems, it's incredible.
The real question isn't "which model is best?"
It's "which model is best for MY actual use case?"
The Full Analysis
I tested on real code. Measured speed, cost, quality. Analyzed the cost-benefit at scale. Broke down when each model actually matters.
The full breakdown — with cost calculations, decision framework, and escalation strategy — is on my blog:
👉 Read the complete analysis on my blog
This is part of "Claude Foundations & Model Family" — a 30-post deep-dive into mastering Claude in production. Daily posts.
Next: Why Claude Opus Is Overkill (And When You Actually Need It)
Top comments (2)
The "which model is best for MY use case" reframing is the right one, and the at-scale numbers make it concrete — the $153K-vs-$3.6K/year spread is what actually changes behavior, not accuracy deltas. The hard part your breakdown hints at but doesn't fully solve: you know Opus wins ~10% of tasks (multi-step reasoning, novel problems), but routing requires classifying which 10% before you run them, and that classifier is itself a cost/accuracy tradeoff. What's worked for us is cheap-first with an escalation trigger rather than upfront routing — run Sonnet, and promote to Opus only on a measurable signal (low self-consistency across a couple of samples, a failed cheap verifier, or the task touching known-hard categories). It captures most of the Opus-only wins without paying Opus prices on the 90% where Sonnet is genuinely good enough. One caution on the per-request accuracy numbers: on multi-step agent work the gap compounds — an 80% single-step model drifts fast over a ten-step trajectory — so Haiku's "80%, incredible for real-time" holds for single-shot calls but not for long agent loops. Did you measure accuracy per-step or end-to-end on the multi-step tasks?
Hey Max,
You nailed it. Two things I missed:
1. Routing cost: Classifying upfront to decide "does this need Opus?" adds cost. Your cheap-first escalation (run Sonnet → escalate on signals) is smarter. I should have framed it that way.
2. Multi-step accuracy: I tested single-shot tasks only. I didn't measure agent loops or reasoning chains where errors compound. You're right: 80% per-step over 10 steps is ~11% end-to-end. That's unusable.
My claim that "Haiku handles 80% of production" only holds for single-shot work. Should have said so.
For Day 4 (Adaptive Thinking), I want to focus on the signals you mentioned: self-consistency, cheap verifiers, known-hard categories.
What signal do you see as most reliable in practice for "Sonnet isn't enough, escalate to Opus"?
Thanks for this. It's the feedback that turns "sounds right" into "actually works."