Claude 3.5 Haiku

Generates text responses with claimed sub-second latency across 200K token context window using optimized transformer inference on Anthropic's managed infrastructure. Implements streaming response capability to deliver tokens incrementally, enabling real-time user feedback. Supports configurable max_tokens parameter (e.g., 1024) to control output length and latency trade-offs for production workloads.

Generates, refactors, and analyzes code across multiple programming languages using transformer-based code understanding. Achieves 73.3% on SWE-bench Verified (Claude Haiku 4.5), matching Claude 3 Sonnet 4 on coding benchmarks despite smaller model size. Supports tool use for multi-step refactoring workflows, code migrations, and feature implementations. Processes entire codebases via 200K context window, enabling codebase-aware suggestions without external indexing.

Enables models to interact with computer interfaces (screenshots, mouse clicks, keyboard input) to autonomously execute tasks. Model receives screenshots of the desktop or application, reasons about the current state, and generates actions (click, type, scroll) to progress toward a goal. Matches Claude 3 Sonnet 4 on computer use benchmarks (Augment's agentic coding evaluation: 90% of Sonnet 4). Supports multi-step task execution without human intervention.

Generates and analyzes text in multiple languages using transformer-based language understanding. Supports code-switching (mixing languages in a single request) and maintains context across language boundaries. No explicit language specification required; model infers language from input. Supports all major languages (English, Spanish, French, German, Chinese, Japanese, etc.) with comparable quality across languages.

Accessible through multiple cloud provider APIs (Amazon Bedrock, Google Cloud Vertex AI, Microsoft Azure Foundry) in addition to Anthropic's native API. Each cloud provider integration uses the provider's native authentication and billing, enabling organizations to consolidate AI spending within existing cloud contracts. API surface is consistent across providers, allowing code portability.

Native integrations with Slack and Google Workspace enable Claude to be accessed directly from chat and productivity tools. Slack integration allows @Claude mentions in channels or DMs to invoke the model. Google Workspace integration (Gmail, Docs, Sheets) enables Claude to analyze emails, draft documents, or process spreadsheet data. Integrations use OAuth for authentication and maintain conversation context within the platform.

Processes images and visual documents (including PDFs) through transformer-based vision encoding, extracting text, analyzing layouts, and answering questions about visual content. Integrates with Files API for multi-page document handling. Vision input is embedded in the same request/response flow as text, enabling mixed-modality reasoning (e.g., analyzing code screenshots alongside written explanations).

Enables models to invoke external functions or APIs through structured tool definitions (JSON schema format). Implements agentic loops where the model generates tool calls, receives results, and reasons over outputs to decide next steps. Supports multi-agent systems with sub-agents for specialized tasks (e.g., one agent for code refactoring, another for testing). Tool calls are returned as structured JSON, enabling deterministic downstream processing.

Performs text classification and named entity extraction using transformer-based sequence labeling, with support for structured output formats (JSON schema). Model returns predictions in a defined schema (e.g., sentiment classification with confidence scores, entity lists with types and positions). Structured outputs are validated against the schema before being returned, reducing parsing errors and hallucinations.

Processes continuous streams of financial data (market prices, trading signals, news feeds) with sub-second latency, enabling real-time analysis and decision-making. Leverages 200K context window to maintain historical context (price trends, news sentiment) within a single request, avoiding context loss across streaming updates. Supports tool use for triggering trades, alerts, or notifications based on analysis results.

Synthesizes research papers, articles, and documents into cohesive summaries and insights using 200K context window to process entire papers or multiple documents in a single request. Supports vision input for analyzing figures, tables, and diagrams embedded in PDFs. Generates structured outputs (JSON) for organizing findings by theme, methodology, or conclusion, enabling downstream analysis and report generation.

Powers conversational customer service agents that maintain context across multiple turns using 200K context window. Supports tool use for looking up account information, processing refunds, or escalating to human agents. Streaming responses enable real-time chat UX. Structured outputs can format responses for specific UI templates (e.g., FAQ answers, troubleshooting steps).

Implements prompt caching at the API level, storing frequently-used system prompts, documents, or context in Anthropic's cache. Subsequent requests with the same cached content incur only 10% of the normal token cost, enabling cost-effective batch processing or repeated analysis of the same documents. Cache keys are automatically generated based on content hash; no explicit cache management required.

Processes requests asynchronously through a batch API, deferring execution to off-peak hours in exchange for 50% cost reduction. Requests are queued and processed in batches, with results delivered via callback or polling. Ideal for non-time-sensitive workloads like document analysis, code review, or research synthesis that can tolerate hours of latency.