What is Claude Haiku 4.5?
Claude Haiku 4.5 is Anthropic's fastest and most cost-efficient model in the Claude 4 family, delivered through the Zenmux provider on Aiduct. Released in October 2025 with a knowledge cutoff of January 2025, Haiku 4.5 is designed for production workloads where latency and cost matter as much as quality. It performs within approximately five percentage points of Claude Sonnet 4.5 on many benchmarks while costing roughly one-third as much, making it the go-to choice for high-throughput applications that need strong reasoning without the overhead of larger models.
Haiku 4.5 is a multimodal model that accepts text and image inputs and produces text output. It supports a 200,000-token context window and can generate up to 64,000 tokens in a single response, giving you room for long-form generation, large document analysis, and multi-turn conversations. The model natively supports function calling, structured output via JSON schemas, and vision tasks such as OCR, chart interpretation, and image description.
Who Should Use Claude Haiku 4.5?
Claude Haiku 4.5 is built for engineers who need to balance performance, speed, and cost at scale. If you're running a customer-facing chatbot, processing thousands of support tickets, or building an agentic workflow that makes dozens of tool calls per session, Haiku 4.5 delivers strong results without the latency or expense of flagship models.
Use Haiku 4.5 when you need:
- High-throughput text generation: customer support automation, content moderation, summarization pipelines, or batch classification tasks.
- Fast vision inference: receipt parsing, document layout analysis, screenshot understanding, or real-time image captioning.
- Agentic workflows with tool use: multi-step reasoning loops where the model calls functions, interprets results, and decides next actions.
- Structured output: extracting entities, generating JSON payloads, or populating database records from unstructured text or images.
- Cost-sensitive production deployments: scenarios where you need Claude-quality reasoning but cannot justify the cost of Sonnet or Opus at scale.
Haiku 4.5 is not a toy model. It handles complex reasoning, follows detailed instructions, and maintains coherent context across long conversations. For many real-world tasks, the performance gap between Haiku and Sonnet is small enough that the cost and speed advantages make Haiku the better choice.
How to Call Claude Haiku 4.5 via Aiduct
Aiduct exposes Claude Haiku 4.5 through an OpenAI-compatible endpoint at https://api.aiduct.ai/v1. You can use the OpenAI Python SDK, make raw HTTP requests, or integrate with any tool that supports the OpenAI ChatCompletion format. The model identifier is claude-haiku-4-5.
Here's a minimal Python example using the OpenAI SDK:
from openai import OpenAI
client = OpenAI(
base_url="https://api.aiduct.ai/v1",
api_key="your_aiduct_api_key"
)
response = client.chat.completions.create(
model="claude-haiku-4-5",
messages=[
{"role": "user", "content": "Explain the CAP theorem in two sentences."}
],
max_tokens=150
)
print(response.choices[0].message.content)
For vision tasks, pass image data as base64-encoded content or public URLs in the message array:
response = client.chat.completions.create(
model="claude-haiku-4-5",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What is in this image?"},
{
"type": "image_url",
"image_url": {"url": "https://example.com/photo.jpg"}
}
]
}
]
)
If you prefer curl:
curl https://api.aiduct.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_AIDUCT_API_KEY" \
-d '{
"model": "claude-haiku-4-5",
"messages": [{"role": "user", "content": "List three use cases for edge computing."}],
"max_tokens": 200
}'
Aiduct also supports the Anthropic Messages protocol natively, so if you're migrating from Anthropic's API, you can keep your existing request shapes and simply point your client at Aiduct's endpoint.
Function Calling and Structured Output
Claude Haiku 4.5 supports function calling (tool use) and structured output, both critical for building agents and data extraction pipelines. When you define tools in your request, the model can decide when to invoke them, generate the correct arguments, and interpret the results you return.
Here's a function-calling example:
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Fetch current weather for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string", "description": "City name"}
},
"required": ["city"]
}
}
}
]
response = client.chat.completions.create(
model="claude-haiku-4-5",
messages=[{"role": "user", "content": "What's the weather in Berlin?"}],
tools=tools
)
# The model will return a tool_call; you execute it and send results back
For structured output, use the response_format parameter to enforce a JSON schema. This is especially useful for extracting entities, generating form data, or populating API payloads:
response = client.chat.completions.create(
model="claude-haiku-4-5",
messages=[{"role": "user", "content": "Extract name and email from: John Doe, john@example.com"}],
response_format={
"type": "json_schema",
"json_schema": {
"name": "contact",
"schema": {
"type": "object",
"properties": {
"name": {"type": "string"},
"email": {"type": "string"}
},
"required": ["name", "email"]
}
}
}
)
Pricing and Cost Optimization
Claude Haiku 4.5 is one of the most cost-effective frontier models available. Aiduct's unified billing means you pay only for what you use, with no minimums or monthly fees. Prompt caching is supported, which can dramatically reduce costs for repeated context—ideal for multi-turn conversations, RAG pipelines, or workflows that reuse the same system prompt or document preamble.
For current per-token rates and caching discounts, see the Aiduct pricing page. Because Haiku 4.5 is roughly one-third the cost of Sonnet 4.5, you can often handle three times the traffic for the same budget, or run experiments and A/B tests without worrying about runaway inference costs.
Aiduct vs. Direct Provider Access
When you call Claude Haiku 4.5 through Aiduct instead of directly through Anthropic or Zenmux, you gain several operational advantages:
- Unified API: Use the same endpoint, authentication, and request format for models from Anthropic, OpenAI, Google, and other providers. Switch models by changing a single string.
- Single bill: One invoice, one set of usage logs, one integration to maintain.
- Prompt caching across providers: Aiduct normalizes caching semantics, so you can use the same caching strategy whether you're calling Claude, GPT, or Gemini.
- Observability: Built-in request logging, latency tracking, and cost attribution without additional SDKs or third-party tools.
- Fallback and routing: Configure automatic failover to alternative models if Haiku 4.5 is unavailable or rate-limited.
You trade off direct access to provider-specific features (like Anthropic's workbench UI or beta endpoints) for the simplicity of a single, stable integration. For production systems, this trade-off usually favors the unified API.
Common Gotchas and Best Practices
Context window management: Haiku 4.5 supports 200,000 tokens of context, but that doesn't mean you should always use it. Long contexts increase latency and cost. Trim irrelevant history, summarize past turns, or use prompt caching to reuse static preambles.
Max output tokens: The model can generate up to 64,000 tokens, but you must set max_tokens explicitly in your request. If you don't, the API will use a conservative default, and long-form generation may be truncated.
Vision input size: Large images consume many tokens. Resize or compress images before encoding them as base64, especially in high-throughput pipelines.
Rate limits: Aiduct enforces per-model rate limits. If you're scaling quickly, contact support to request a limit increase rather than implementing aggressive retry logic that can cascade failures.
Streaming: Haiku 4.5 supports streaming responses via stream=True. Use streaming for user-facing applications to reduce perceived latency, but be aware that token-by-token delivery complicates error handling and retries.
Prompt caching: To enable caching, structure your messages so that the static portion (system prompt, reference documents) appears first. Aiduct will automatically cache repeated prefixes and bill you at the lower cache-read rate for subsequent requests.
When to Choose Haiku 4.5 Over Sonnet or Opus
Claude Sonnet 4.5 and Opus 4 offer stronger reasoning, better performance on hard benchmarks, and more nuanced instruction following. But for many tasks, Haiku 4.5 is close enough. If your application involves summarization, classification, simple Q&A, or tool-calling workflows with clear success criteria, Haiku 4.5 will often match Sonnet's output quality at a fraction of the cost and latency.
Choose Sonnet or Opus when you need:
- Maximum reasoning depth for complex math, code generation, or multi-step logic.
- Best-in-class creative writing or nuanced tone control.
- Tasks where the cost of a wrong answer is high (legal analysis, medical triage).
Choose Haiku 4.5 when you need:
- High throughput and low latency.
- Cost efficiency at scale.
- Good-enough quality for well-scoped tasks.
Run your own evals. Many teams find that Haiku 4.5 meets their quality bar for 70–80% of requests, and they route only the hardest cases to Sonnet or Opus.
Getting Started
Sign up for an Aiduct API key at aiduct.ai, add credits to your account, and start calling claude-haiku-4-5 through the OpenAI-compatible endpoint. No special SDK is required—use the OpenAI Python library, the Anthropic SDK pointed at Aiduct's base URL, or raw HTTP requests.
For code examples, API reference, and integration guides, see the Aiduct documentation. If you're migrating from Anthropic's API, the transition is usually a one-line change to your base URL and API key.