Claude Models Overview — Opus, Sonnet, Haiku

How the three tiers are organised

Anthropic structures its Claude model line around three named tiers within each generation: Opus at the top, Sonnet in the middle, and Haiku at the lightweight end. The names carry forward across generations with numeric suffixes when a new release updates a tier — so Claude 3 Sonnet, Claude 3.5 Sonnet, and a future Claude 4 Sonnet would all sit in the middle tier, with each succeeding version outperforming its predecessor on standard benchmarks.

That naming structure means the tier label is a reliable signal for capability level regardless of which specific version you are on. When you see a model string like claude-3-5-sonnet-20241022, the "sonnet" segment tells you the tier and the date suffix tells you the checkpoint. For most integrations, you want to pin to a specific version in production and update deliberately rather than floating to the latest, because model behaviour can shift between checkpoints in ways that matter for fine-tuned prompts.

Claude Opus

Opus is built for tasks where reasoning depth and sustained coherence across a large, complex context are the deciding factors. Multi-file code reviews, lengthy document analysis, architecture planning that requires holding many constraints simultaneously — these are the workloads that justify Opus's higher per-token cost. In practice, most teams use it on a minority of tasks: the ones where Sonnet's output clearly missed something that Opus catches.

The extended thinking capability available on larger models further differentiates Opus for tasks that benefit from an explicit reasoning trace before the answer. That trace is visible in the response when enabled, which is useful when you need the model to show its work for a technical audience. See the dedicated claude opus page for a full trade-off breakdown versus Sonnet.

Claude Sonnet

Sonnet is the recommended default for most engineering teams. It handles interactive pair programming, code generation, documentation drafting, and routine code review well, at a per-token cost that is substantially lower than Opus. Latency is notably better than Opus, which matters in interactive sessions where the model is in a tight loop with the developer.

Claude 3.5 Sonnet in particular improved significantly on instruction following and coding accuracy compared to its predecessor, and many teams that previously defaulted to Opus for coding work found Sonnet sufficient after benchmarking against their actual task distribution. The upgrade from Sonnet to Opus should be driven by measured outcomes on your workload, not by a default assumption that bigger is better.

Claude Haiku

Haiku is the speed and cost optimisation tier. It runs at the lowest latency and the lowest per-token price in the Claude family, making it the natural choice for bulk batch processing, lightweight classification, quick retrieval augmentation, and any workload where the prompt is short and the required output is simple. A team running several thousand classification calls per day will find Haiku's economics compelling compared to Sonnet.

Haiku does not match Sonnet or Opus on complex reasoning or long-document coherence, but for tasks that do not require those capabilities, the gap in output quality is smaller than the gap in cost. A reasonable architecture for cost management: use Haiku for all narrow, repetitive tasks; route complex or high-stakes tasks to Sonnet; reserve Opus for the small subset that genuinely requires maximum reasoning depth. That routing can be implemented as a simple classifier or as a manual flag in your application logic.

Side-by-side comparison

Model	Context	Output limit	Typical price signal	Best fit
Claude Opus	200k tokens	Up to 8192 tokens	Highest (~5× Sonnet)	Deep reasoning, long documents
Claude Sonnet	200k tokens	Up to 8192 tokens	Mid-tier (baseline)	Interactive coding, daily dev work
Claude Haiku	200k tokens	Up to 4096 tokens	Lowest (~0.2× Sonnet)	Bulk tasks, classification, speed

Choosing a model for your workload

The fastest path to the right model is to run a short benchmark on your actual prompts rather than relying on general guidance. Take a representative sample of your most common requests, run each model against them, and grade the outputs on the dimensions that matter to your application — accuracy, format adherence, latency, cost. That data will anchor the decision far more reliably than any static comparison page, including this one.

If you cannot run a benchmark before committing, the safe default is Sonnet for interactive work and Haiku for batch automation. Opus is the upgrade path when Sonnet falls short on a specific task type. For research context on model selection heuristics, the NSF CISE directorate funds publicly accessible research on AI system evaluation that may be useful for teams designing formal evaluation frameworks.