Claude Haiku 4.5: Pricing, Context, and Batch Agent Configuration

One-sentence takeaway

Haiku 4.5 is well suited to high-concurrency, low-latency subtasks and real-time assistants: the official model table lists $1/$5 per million input/output tokens, a 200k context window, and a 64k maximum output, but complex tasks should be planned and reviewed by a larger model.

Use cases

• Suitable tasks: Classification/extraction, short summaries, customer service, code completion, parallel subtasks, and real-time multi-turn interaction.

• Unsuitable tasks: Using it as the sole planner for high-risk, long-chain refactoring or deep security reasoning.

• Applicable model version: Claude Haiku 4.5; API name claude-haiku-4-5.

• Applicable clients, agents, or APIs: Claude API, Bedrock, Vertex AI, and Claude Code; platform alias and regional support require verification.

• Recommended reasoning tier and parameters: There is no unified adaptive thinking; if a task genuinely requires thinking, test an extended thinking budget and set a hard max_tokens; for routine high-throughput work, disable extra thinking by default.

Ready-to-use content

{
  "model": "claude-haiku-4-5",
  "max_tokens": 2048,
  "system": "You are a batch-task subagent. Complete only the assigned subtask and output strict JSON. Return null when evidence is missing; do not guess.",
  "messages": [
    {
      "role": "user",
      "content": "<task>\nExtract the order status and amount.\n</task>\n<document>\n...\n</document>\n<output_schema>{status:string|null, amount:number|null, evidence:string[]}</output_schema>"
    }
  ]
}

Recommended routing workflow: A larger model generates the task plan and acceptance criteria → parallel Haiku subagents perform extraction/retrieval/drafting → server-side schema and permission checks → a larger model or rules engine merges and reviews the results.

Test/workflow steps

1. Using real batch data, test 1, 4, and 8 parallel Haiku workers separately; fix the inputs, timeout, output schema, and retry rules.

2. Record each task's success rate, p95 latency, tokens, cost, retry count, and manual correction rate.

3. Add difficult samples to an escalation queue for Sonnet/Opus review instead of retrying Haiku indefinitely.

4. Evaluate whether prompt caching, the Batch API, or provider-specific batching changes the cost; use the current official page for pricing.

Raw evidence and data

• Anthropic release page: Haiku 4.5 pricing is $1 per million input tokens / $5 per million output tokens; it describes the model as having Sonnet 4-level coding performance, one-third the cost, and more than twice the speed, but these are comparisons made by the publisher.

• Official model table: Haiku 4.5 has a 200k context window, a 64k maximum output, the comparison latency designation “fastest,” extended thinking support, and no adaptive thinking support.

• The official model table lists text/image input, text output, multilingual support, and vision for all current models; Haiku's specific capabilities still need to be checked against the endpoint specifications.

• The official release page recommends that Sonnet 4.5 plan complex problems and then orchestrate multiple Haiku 4.5 subtasks in parallel.

Applicability boundaries

• “One-third the cost / twice the speed” is the official release wording relative to Sonnet 4; the full workload, throughput, and confidence intervals have not been disclosed.

• The $1/$5 pricing does not include differences for caching, batching, regions/cloud platforms, or additional long-context charges; check the current pricing table before deployment.

• Parallel workers amplify the risks of tool use, rate limiting, duplicate writes, and data leakage; idempotency IDs, permissions, and auditing are required.

• A 64k maximum output does not mean a task will reliably generate 64k; max_tokens should still be set per task to control costs.

Source excerpts or observations (compliance short quote only)

The official product positioning is “fastest model with near-frontier intelligence” (compliance short quote); the trade-off here prioritizes throughput rather than making it unconditionally optimal for complex reasoning.