AI

Claude vs GPT — What We Actually Use Each For

Teams keep asking: which model should we use for X? The short answer: use the right tool for the job. Here’s how we allocate work across three model classes in production: Opus-class for coding-heavy tasks, Sonnet-class for general reasoning and contextual dialogue, and lightweight minis for repetitive micro-tasks.

Opus: coding, synthesis, and heavy reasoning

We use Opus-family models when we need precise code generation, complex refactors, or multi-step planning that requires a lot of context. Opus handles code synthesis well because it preserves structure, produces runnable snippets, and tolerates longer prompts. For pull-request generation, automated code review, and CI-time fixes, Opus is our go-to.

Sonnet: conversations, strategy, and nuanced judgement

Sonnet sits in the middle. It’s our generalist: product sketches, PR summaries for humans, strategic recommendations, and customer-facing copy that needs a sense of tone. Sonnet is cheaper than Opus but retains reasonable depth. We prefer it for tasks where human readability and judgment matter more than exact syntactic correctness.

Mini models: cheap, fast, repeatable

For high-volume, low-complexity work (labeling, simple classification, token transformations), tiny models run locally or at minimal cost. These minis are perfect for preprocessing text, extracting structured fields, or enforcing formatting rules. They are fast and cheap enough to run thousands of times per day.

How we route tasks

We implemented a simple router: if the task is code-heavy or needs high-fidelity reasoning, send to Opus. If it’s dialogue, tone, or product thinking, send to Sonnet. If it’s repetitive, deterministic work, use a mini. A confidence layer re-routes low-confidence outputs to humans or a higher-tier model.

Cost vs quality tradeoffs

Opus is expensive but reduces developer time. Sonnet is cost-effective for customer-facing outputs. Minis are trivial in cost but require robust validation to avoid garbage-in-garbage-out. The economics are straightforward: spend on the model that saves equivalent human time.

Practical tips

- Cache results from expensive models when possible. - Use minis for pre-filtering and Sonnet/Opus only after filtering to reduce calls. - Add verification steps: run a cheap checker on Opus output before applying it.

Final note

Model choice should be explicit and revisited regularly. As models improve, the balance will shift. For now, treat Opus as your skilled engineer, Sonnet as your generalist storyteller, and minis as the scale engine. Use them together and build the plumbing to route, validate, and escalate.