Anthropic's fastest, lowest-cost Claude, delivering Sonnet 4-class coding and agentic performance for high-volume, latency-sensitive production.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Haiku 4.5 Earns Its Place: Speed With Real Coding Ability
Benchmarks
Independent Benchmarks: Fast Throughput, Above-Average Intelligence for Its Tier
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
Claude Haiku 4.5 vs Claude Sonnet 4.5: Speed and Cost or Reasoning Depth?
These two are compared because Haiku 4.5 launched two weeks after Sonnet 4.5 and shares the same 200,000-token context window, tool calling, extended thinking, and vision input. Anthropic explicitly positions them to work together: Sonnet 4.5 as the frontier orchestrator and Haiku 4.5 as the fast, cheap sub-agent. The practical decision is not capability parity—Sonnet 4.5 remains the stronger reasoning and coding model—but whether a given task needs that extra depth or benefits more from Haiku's lower latency and substantially lower per-token cost at high volume.
Choose Claude Haiku 4.5 when latency is user-visible or volume is high: real-time chat, code autocomplete, classification, and fleets of parallel sub-agents where speed and cost dominate. It runs several times faster than Sonnet 4.5 and occupies the lowest-cost tier of the family. Choose Claude Sonnet 4.5 when you need the strongest agentic coding, deeper multi-step reasoning, or long-horizon autonomy on complex tasks, and can absorb higher latency and cost. Many production systems use both—Sonnet to plan, Haiku to execute.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Real-time chat and customer service agents
Anthropic positions Haiku 4.5 for low-latency assistants, and independent TTFT measurements near 0.7 seconds with ~95 tokens/second output make responses feel near-instant. This suits customer service bots and interactive assistants where every hundred milliseconds is visible to the user. The 200K context comfortably holds conversation history and retrieved documents. The main caveat: for the most complex escalations that need deep reasoning, route to a larger Claude model.
Agentic coding and multi-agent sub-agents
With 73.3% on SWE-bench Verified (Anthropic-reported) and coding roughly comparable to Sonnet 4, Haiku 4.5 handles repository-level bug fixing, rapid prototyping, and tool-driven code tasks. Its speed makes tight edit-run-fix loops responsive. Anthropic explicitly recommends it as the sub-agent layer under a Sonnet or Opus orchestrator, so large jobs can be fanned out to many cheap, fast Haiku instances running in parallel.
High-volume classification and extraction
The combination of fast throughput, low per-token cost, structured JSON outputs, and function calling makes Haiku 4.5 well suited to mass classification, tagging, moderation, and structured extraction pipelines. Prompt caching further reduces cost when a large shared instruction set or schema is reused across many calls. For these throughput-bound jobs, its speed-per-dollar is the decisive advantage over larger models.
Long-output generation and rewrites
The 64,000-token output ceiling—about eight times the previous Haiku generation—lets Haiku 4.5 produce long-form code, detailed reports, and large document rewrites in a single response without stitching multiple calls. Because it proactively shortens output as it approaches the context limit, plan input size so the model has room to complete long generations. For inputs beyond 200K tokens, a 1M-window model remains the better fit.