Claude Opus
Highest-complexity reasoning and engineering
Evaluate Opus for difficult codebases, long planning tasks, deep analysis, and workflows where stronger reasoning can justify higher cost.
Browse matching modelsFull-capability model access · Anthropic Claude models
Claude full-capability API across Opus, Sonnet, and Haiku model families
Compare Claude model tiers for coding, long context, reasoning, tool use, and production assistants, then confirm the exact model ID in the current catalog.
Provider family
Anthropic Claude
Common interfaces
Messages and OpenAI-compatible
Selection rule
Quality, speed, and context
Current Claude model families
Claude tiers serve different workload profiles. Open the live catalog for the exact versioned model IDs currently available.
Claude Sonnet
Balanced coding and production work
Sonnet is a practical starting point for coding assistants, agents, document workflows, and applications balancing quality, latency, and price.
Browse matching modelsClaude Haiku
Fast routine requests
Use Haiku for classification, extraction, short responses, and latency-sensitive tasks after confirming it meets the required quality level.
Browse matching modelsUse-case fit
Where Claude model families fit
Claude Code and agents01
Repository and coding work
Claude is commonly tested for repository understanding, code changes, tool calls, reviews, debugging, and multi-file engineering tasks.
Context-heavy work02
Long documents and instructions
Use representative documents and system instructions to test whether the selected tier preserves important details across a long context.
Quality and latency03
Production assistants
Compare Opus, Sonnet, and Haiku with the same prompts to choose the tier that meets quality targets without unnecessary cost.
User decision signals
What to verify before choosing Claude
Run the same tools, system instructions, document sizes, and output format expected in production before choosing a Claude tier.
Provider family
Anthropic Claude
Common interfaces
Messages and OpenAI-compatible
Selection rule
Quality, speed, and context
Selection logic
What to verify before choosing Claude
01
Native Messages behavior matters
If your application or Claude Code uses the Anthropic Messages format, validate that path directly instead of testing only an OpenAI-compatible request.
02
Tool use and vision depend on the exact model
Confirm supported inputs, tools, thinking behavior, and endpoint types in the current catalog before relying on them in production.
03
Tier selection should follow real tasks
A short chat is not enough. Test your actual repository, documents, tools, and output constraints across the candidate Claude tiers.
Real user questions
Claude full-capability API FAQ
Q01
What does Claude full-capability API mean here?
It means using the intended Claude tier through a supported interface while preserving the capabilities listed for that exact model ID.
Q02
Which Claude tier should I test first?
Sonnet is a common balanced starting point. Compare Opus for harder reasoning and Haiku for faster routine work using the same evaluation set.
Q03
Can I use these models with Claude Code?
Use a model and access path that supports the Anthropic Messages workflow, then verify the current Claude Code configuration instructions before rollout.
Anthropic Claude models
Test Claude through the endpoint your application will use
Run the same tools, system instructions, document sizes, and output format expected in production before choosing a Claude tier.