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Anthropic's low-latency multimodal model featuring a 1,000,000-token context window and configurable reasoning effort.
Overview. Claude Haiku 5.5 is Anthropic's high-speed multimodal model designed for low-latency inference workloads. It processes text and visual inputs across a 1,000,000-token context window with support for up to 128,000 generated tokens. Developers select Haiku 5.5 when rapid response generation and large document ingestion take precedence over frontier-scale parameter counts.
Key strengths. The model pairs real-time latency with extensive ingestion capacity, accepting text, images, and document files up to 1,000,000 tokens. It supports adaptive extended thinking with five configurable reasoning effort levels ranging from low to maximum, allowing runtime calibration of deliberate problem solving. Evaluation profiles demonstrate particular strength in direct communication, writing synthesis, and high-volume document research.
When to use it. Deploy Claude Haiku 5.5 for customer-facing communication agents, real-time triage, and rapid document digestion over extended context inputs. It functions effectively within the Smart Router for medium-complexity tasks that demand low latency. Workloads requiring deep mathematical reasoning or complex agentic loops should route to higher-tier models.
Limitations. Claude Haiku 5.5 operates strictly as a closed-weight hosted API and cannot be self-hosted. The model rejects custom temperature settings at the API level, returning client errors if non-default sampling values are supplied, and does not support native audio processing.
Sovereignty note. Anthropic is based in the United States and subject to the US CLOUD Act. Inference processed via proprietary Anthropic endpoints does not meet European Sovereign deployment standards.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.09 / 0.45+1 rate | Online |
12 use cases classified by the curator. The note says why.
Fast inference speeds and low cost profile make this model highly effective for continuous, high-volume document and text classification.
Native tool-calling capability paired with low latency delivers rapid, reliable schema adherence for operational API integrations.
Sub-second turnaround and low token costs make it an ideal engine for real-time RAG query expansion and reformulation.
Combines a 1M token context window with native tool-calling to extract structured JSON from expansive documents at low unit cost.
High operational throughput and cost efficiency make it ideal for mass metadata tagging and batch taxonomy labeling.
Tool-calling support and minimal generation latency make it well-suited for fast multi-step agentic execution loops.
Low latency and tool-calling support handle frontline customer inquiries competently, though complex edge cases favor higher reasoning tiers.
A 1M token context window supports querying full document archives, though synthetic analysis across dense material is better handled by Sonnet.
1M context capacity allows single-pass summarisation of large text corpuses with fast turnaround and economical pricing.
Model specification does not include multimodal vision support.
Lacks native audio processing capabilities and cannot ingest speech input.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "anthropic/claude-haiku-5-5",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at