I was halfway through migrating an extraction pipeline to a new prompt format when Anthropic dropped Claude 3: three models – Opus, Sonnet, and Haiku – with different capability tiers, price points, and latency profiles.
My first reaction: finally, someone is admitting that one model doesn’t fit every job.
My second reaction: now I have to rerun all my evals.
The lineup
Anthropic did something smart here. Instead of releasing one model and calling it “the best,” they gave you a menu with clear trade-offs.
Opus is the heavyweight. Complex reasoning, deep analysis, demanding coding tasks. It’s slower and more expensive than the others, but the quality ceiling is noticeably higher. I ran it against some gnarly extraction cases I’ve been working on – multi-page contracts with nested clauses and ambiguous references. It handled nuance that the previous generation fumbled.
Sonnet is the workhorse. Good enough for most production workloads, fast enough for interactive use, and priced so it is still viable at volume. This is where I expect most teams to land as a default.
Haiku is the speed demon. Lightweight tasks, high-volume classification, anything where latency matters more than depth. I tested it on a categorization pipeline – hundreds of short inputs, simple labels – and it ripped through them. The quality was adequate for the task, and the speed was impressive.
The real value isn’t any single model. It’s the fact that you can route between them based on what the task actually needs.
What I noticed in practice
A few things stood out during my first week of testing.
Instruction following is substantially better. Prompts that previously needed careful phrasing to avoid drift now work with more natural language. This is the kind of improvement that doesn’t show up in benchmarks but saves real time in production prompt maintenance.
Vision capabilities are real. I fed Opus some architectural diagrams from a past project and asked it to describe the data flow. The descriptions were useful – not perfect, but useful enough to save someone from manually transcribing a whiteboard photo.
The context window is large, but I’ve learned not to treat large context as a substitute for good retrieval. Stuffing 200k tokens of raw documents into context and hoping for the best is still a bad strategy. I got better results with targeted retrieval feeding a smaller context window.
One thing that frustrated me: the API rate limits during launch week were tight. I burned through my allocation faster than expected while running evals. Plan for this if you’re testing around a major release.
How I’m thinking about adoption
The question isn’t “should I use Claude 3?” It’s “which tier maps to which workflow?”
Before switching any production traffic, I work through these questions:
- Latency budget. Interactive features need sub-3-second responses. That might mean Haiku for the fast path and Sonnet for a follow-up detail request.
- Quality threshold. Classification and routing tasks don’t need Opus. Contract analysis probably does.
- Cost sensitivity. High-volume features should default to the cheapest model that meets the quality bar. Upgrade selectively.
- Rollback plan. What happens if quality regresses after the switch? If you don’t have an answer, you aren’t ready.
I route by task type, not by model hype. Haiku handles the lightweight stuff. Sonnet is the default for anything interactive. Opus gets called when the task genuinely needs deeper reasoning. This isn’t a Claude-specific strategy – it’s how I think about any multi-model setup.
The honest assessment
Claude 3 is a meaningful step forward. The quality improvements are real, especially in instruction following and structured output. The tiered model approach is the right direction for the industry – it forces you to think about routing, evaluation, and cost management instead of treating the model as a magic box.
But it’s still a model. It still hallucinates. It still needs evaluation. It still needs guardrails and fallback paths. The teams that will get the most out of Claude 3 are the ones that already have those systems in place.
For everyone else, the release is a good excuse to finally build them.