Overview
Anthropic's API exposes the Claude 3.5 and Claude 4 (Opus/Sonnet/Haiku) families with the industry's leading long-context accuracy, exceptional instruction-following, and clean tool-use semantics. It's the API teams reach for when writing quality, prompt fidelity, and multi-file reasoning matter more than the widest ecosystem.
Capabilities
- 200K-token context window with strong needle-in-haystack accuracy.
- Tool use with parallel calls and streaming.
- Prompt caching for large system prompts and RAG contexts (up to 90% discount).
- Message Batches API for async jobs at 50% discount.
- Extended thinking mode on Opus 4 / Sonnet 4.
Where it shines
- Best-in-class prompt following — Claude does what you asked, not what it assumed.
- Long-context recall is the closest to lossless of any current model family.
- Prompt caching makes 100K-token system prompts economically viable.
- Refusal patterns are more predictable and appealable than OpenAI's.
Where it struggles
- Ecosystem smaller than OpenAI's — some third-party integrations lag.
- No native voice / TTS product; use ElevenLabs or OpenAI Realtime alongside.
- Structured outputs exist but aren't as strict as OpenAI's schema-enforced mode.
Best for
- Long-document analysis, contract review, codebase Q&A.
- Agentic workflows where instruction following and tool discipline matter.
- Writing-heavy applications where prose quality is the product.
Pricing notes
Sonnet 4 ~$3/M input / $15/M output. Haiku ~$0.25/$1.25. Prompt caching drops cached input to ~$0.30/M. Batches API halves any of these. Big system prompts + caching = the cheapest way to run long-context RAG.
Tutorials & patterns that work
Enable prompt caching on every long system prompt
Anything >2K tokens that repeats across requests should be cached. Add cache_control:{type:'ephemeral'} on the message block. First call sets the cache; subsequent hits within 5 minutes bill at 10% of normal input rate. Instant savings.
Use tools with parallel execution
Claude will emit multiple tool_use blocks in a single response when they're independent. Execute in parallel on your side and return all tool_result blocks in the next turn. This cuts latency 2-4x on multi-tool agent turns.
Extended thinking for hard problems only
Extended thinking (Opus 4 / Sonnet 4) adds a large reasoning budget before the response. It's expensive — reserve it for architecture, math, and multi-step planning. Cheap first-pass + escalate is still the right pattern.