Claude Haiku 5.5 API

anthropic/claude-haiku-5.5
1M tokens · 16 input / 80 output credits / 1M tokens

Claude Haiku 5.5 turns tickets, documents, and screenshots into labels, structured fields, and short summaries, with a 1M-token context and 128K-token replies. Set output_config.effort per request to keep simple routing fast and give harder extractions more reasoning.

Claude Haiku 5.5

Anthropic · messages

Chat with Claude Haiku 5.5

Describe your coding or analysis task and provide relevant text. Choose a protocol and output limit, then start a conversation and review token usage.

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Claude Haiku 5.5

Claude Haiku 5.5 is Anthropic's fastest and lowest-cost Claude 5.5 model, released in October 2026 for high-volume, latency-sensitive work. It reads text and images and answers in text, using adaptive thinking whose depth follows the effort setting. A 1M-token context and up to 128K output tokens let one call classify a queue, extract fields from a long contract, or act as a quick subagent beside Sonnet or Opus.

Why Choose Claude Haiku 5.5?

  • Built for volumeRun classification, routing, and tagging over large queues at $0.08 input and $0.40 output per 1M tokens.

  • Structured extraction from long filesPull dates, amounts, parties, and clauses from documents up to the 1M-token context and return them in a JSON schema.

  • Effort you can dialChoose low, medium, high, xhigh, or max effort; medium is the default and low suits simple routing.

  • A fast subagentHand scoped lookups, file inspections, and summaries to Haiku 5.5 while Sonnet 5.5 or Opus 5.5 leads the main task.

  • Caching from 512 tokensCache shared instructions and reference text with a five-minute or one-hour lifetime; prompts from 512 tokens qualify.

Parameters

ParameterRequirementDescription
modelRequired

Use anthropic/claude-haiku-5.5 with either protocol.

messagesRequired

A non-empty ordered array of role and content messages that ends with a user turn. Claude Messages accepts text and image blocks; Chat Completions places system instructions in a system message.

max_tokensRequired for Claude Messages; optional for Chat Completions

Output limit from 1 to 128,000 tokens, including thinking. The playground defaults to 4,096.

systemOptional for Claude Messages

System instructions as a string or structured text blocks. The playground provides a text field.

output_config.effortOptional for Claude Messages

Sets how much the model reasons before answering.

Defaultmediumlowhighxhighmax
thinkingOptional for Claude Messages

Adaptive thinking is on by default; at high effort or below it can be set to disabled.

Defaultadaptivedisabled
output_config.formatOptional for Claude Messages

Use type json_schema and a schema object to define structured output, such as extracted fields.

cache_controlOptional for Claude Messages

Use an ephemeral cache marker with ttl 5m or 1h. Cacheable prompt content starts at 512 tokens.

toolsOptional for Claude Messages

Define callable tools with name, description, and input_schema; your application executes returned tool calls.

stop_sequencesOptional for Claude Messages

Strings that stop generation when encountered.

streamOptional

false returns complete JSON; true returns SSE events. The playground sends true.

Defaultfalsetrue

How to Use

  1. Define the labels or fieldsList the categories, routing targets, or field names you expect, with one short example of the desired output.

  2. Send the itemAdd the ticket text, document excerpt, or screenshot in a user message at the end of messages.

  3. Choose protocol and effortSelect Claude Messages or Chat Completions, set max_tokens, and start with low effort for simple routing.

  4. Validate and scaleCheck a sample of results against your labels, adjust instructions or effort, then run the full queue.

Pricing

Input, output, and cache usage are billed separately. All rates below apply per 1M tokens.

UsageRateDetails
Input$0.080 · 16 credits / 1M tokensFresh input tokens in the request, including images.
Output$0.400 · 80 credits / 1M tokensTokens generated in the response, including thinking.
Cache Read$0.008 · 1.6 credits / 1M tokensPreviously cached prompt tokens read by the request.
Cache Write (5m)$0.100 · 20 credits / 1M tokensPrompt tokens written with a five-minute cache lifetime.
Cache Write (1h)$0.160 · 32 credits / 1M tokensPrompt tokens written with a one-hour cache lifetime.

Best Use Cases

  • Support ticket triageAssign category, priority, and team to incoming tickets, with a one-line reason for each decision.

  • Invoice and contract field extractionReturn totals, due dates, parties, and renewal terms as JSON that downstream systems can store directly.

  • Call transcript summariesCondense long transcripts into outcomes, follow-ups, and customer sentiment for CRM notes.

  • Log and alert classificationGroup alerts by likely cause and flag the ones that need a person, so on-call teams see fewer duplicates.

Pro Tips

  • Shape the output with system instructions and output_config.format, and end messages with a user turn.
  • Start at low effort for routing; raise it only for items the model labels with low confidence.
  • Put shared instructions and label definitions first and cache them to cut repeated input cost.
  • Budget for the newer tokenizer: Anthropic notes the same text uses about 30% more tokens than on Claude Haiku 4.5.

Usage Notes

  • The context window is 1,000,000 tokens; the output limit is 128,000 tokens.
  • Requests carry system, messages, and max_tokens; reasoning depth follows output_config.effort.
  • The playground uses text conversations with either protocol and streams replies as SSE.
  • Claude Messages also accepts image blocks and API fields for tools, structured output, and caching.

Related Models

Claude Haiku 5.5 API FAQ

What is the Claude Haiku 5.5 API?

Claude Haiku 5.5 is an Anthropic model that turns text and images into labels, extracted fields, summaries, and short answers, and it is the fastest model in the Claude 5.5 lineup. It is built for high-volume work such as routing, classification, and subagent tasks. Adaptive thinking follows the effort setting, within a 1M-token context and up to 128K output tokens. Use the API tab for Claude Messages and Chat Completions examples, or try a conversation in the Playground.

What changed in Claude Haiku 5.5 from Haiku 4.5?

Context grows from 200K to 1M tokens and output from 64K to 128K tokens. Reasoning is set with output_config.effort instead of budget_tokens, and requests omit temperature, top_p, top_k, and assistant prefill. Anthropic reports 39.2% on Terminal-Bench 4.0, up from 0.0% for Haiku 4.5.

How do I set Claude Haiku 5.5 effort?

In Claude Messages, set output_config.effort to low, medium, high, xhigh, or max; medium is the default. Low effort keeps routing and tagging fast, while high or above helps with long or ambiguous documents. Thinking is on by default and can be disabled at high effort or below.

Can Claude Haiku 5.5 extract fields into JSON?

Yes. Define the fields in output_config.format with type json_schema, then send the document text or an image of it. The reply follows the schema, so invoices, contracts, and forms can feed databases without manual parsing.

What does Claude Haiku 5.5 cost on Vidgo?

Input costs $0.08 (16 credits) and output costs $0.40 (80 credits) per 1M tokens. Cache reads cost $0.008, five-minute cache writes $0.10, and one-hour cache writes $0.16 per 1M tokens. Anthropic lists $0.10 input and $0.50 output for prompts up to 100K tokens.

Is Claude Haiku 5.5 good for coding subagents?

It works well for scoped lookups, file inspections, and summaries that a lead agent hands off. Anthropic reports 39.2% on Terminal-Bench 4.0 and recommends Claude Sonnet 5.5 or Opus 5.5 for complex agentic coding. A common setup pairs a Sonnet or Opus lead with several Haiku 5.5 workers.

Can Claude Haiku 5.5 read screenshots and charts?

Yes. Claude Messages requests accept image blocks together with text, so you can ask about a chart, a form, or an interface screenshot. The model replies in text, for example with extracted values or a short explanation.