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Claude Haiku 4.5
- Instruction Following
Claude Haiku 4.5 is Anthropic’s fastest, most cost-efficient Claude 4.5-generation model, offering near-frontier intelligence with low latency and pricing optimized for large-scale use. It supports long-context, multimodal workloads while matching larger Claude models on many coding and agentic tasks.
About the model
What is Claude Haiku 4.5?
Claude Haiku 4.5 is a small, fast large language model from Anthropic’s Claude 4.5 family, optimized for low-latency, cost-efficient deployment. It is mainly used for real-time conversational agents, support-style chatbots, and interactive applications that require quick responses at scale, as well as for production workloads like large-scale financial analysis and research where throughput and price are critical. It is also widely used for software engineering workflows, including code generation, debugging, and multi-agent coding or computer-use tasks, aided by its 200k-token context window, tool use, and vision support. Claude Haiku 4.5 belongs to the Claude 4.5 model family and succeeds earlier small models such as Claude 3.5 Haiku.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn conversations, follows instructions, answers questions, and maintains context for helpful, concise assistant-style dialogue.
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Code Reasoning
Understands and writes code, explains programming concepts, and assists with debugging and small-scale software or script tasks.
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Image Understanding
Interprets images, identifying objects, text, layouts, and visual relationships to support analysis and question answering.
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Visual Text Extraction
Reads and extracts text from images, screenshots, and scanned documents for downstream processing, search, or transformation.
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Language Translation
Translates between multiple natural languages while preserving meaning and tone across general-purpose text content.
Use cases
6 Most Valuable Use Cases
- Customer Chat Support
- Invoice Data Extraction
- Legal Document Search
- Regulatory Change Monitoring
- E-commerce Product Assistant
- Code Generation Helper
Transparent pricing
Cost Comparison
LLM API offers the lowest Claude Haiku 4.5 prices with faster latency and larger context than major providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.05 | $0.10 | 200K |
| Anthropic | US East | ~220ms | ~80 tps | 99.9% | $0.10 | $0.20 | 200K |
| AWS Bedrock | US West | ~250ms | ~70 tps | 99.9% | ~$0.11 | ~$0.22 | 200K |
| Google Cloud | Global | ~260ms | ~65 tps | 99.9% | ~$0.11 | ~$0.22 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Haiku 4.5 | GPT-4.1 Mini | Gemini 1.5 Flash |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $0.15 | $0.15 | $0.35 |
| Output Price ($/1M) | $0.60 | $0.60 | $1.05 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | ~80 tps | ~70 tps | ~60 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 6.8B
- Prompt tokens processed (last 30 days)
- 2.3B
- Completion tokens generated (last 30 days)
- 11.4M
- API requests served (last 30 days)
- 99.8%
- Avg API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the optimal model across providers based on latency, cost, and quality. One API, pluggable policies, zero vendor lock‑in.
One endpoint, any model -
Cost-Aware Orchestration
Set per-request or per-project cost policies and let LLM.API choose cheaper equivalents automatically. Eliminate manual price tuning while keeping predictable spend.
Control spend by design -
Automatic Fallbacks
Configure multi-provider failover so requests seamlessly retry on backup models when a vendor is down or throttled. Ship resilient AI features without custom glue code.
Resilience by default -
Full-Stack Observability
Get centralized traces, logs, metrics, and cost breakdowns across all models and vendors. Debug prompts, spot regressions, and optimize performance from a single dashboard.
See every token -
Task-Level Abstractions
Describe tasks—chat, RAG, tool use, scoring—once and let LLM.API pick the right models and parameters. Iterate on behavior, not low-level API wiring.
Code tasks, not glue -
High-Throughput Batch
Submit massive batches of requests through a unified endpoint with queueing, parallelism, and retries handled for you. Maximize throughput while staying within provider limits.
Millions of calls, one API
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a low-cost, fast model for everyday assistant-style questions and answers.
- You need to power high-volume chatbots where latency and affordability matter more than depth.
- Your use case involves lightweight content generation like short emails, social posts, or summaries.
- Your use case involves simple code edits, small bug fixes, or clarifying existing snippets.
- You need a safe, guarded model for end-user applications with strong default alignment.
- Your use case involves classification, tagging, or routing tasks on large text datasets.
- You need a general-purpose helper embedded in products where cost must stay predictable.
Avoid if...
- You need state-of-the-art reasoning on complex multi-step problems or intricate planning tasks.
- Your workload requires advanced code generation for large projects or complex software architectures.
- You need top-tier performance on long-context analysis like large research paper sets.
- Your workload requires creative writing at flagship quality, such as novels or screenplays.
- You need cutting-edge multimodal reasoning or image understanding beyond basic or experimental capabilities.
- Your workload requires best-available performance on math-heavy, symbolic, or formal reasoning benchmarks.
- You need highly specialized domain expertise comparable to premium, large-scale flagship language models.
FAQ
Frequently Asked Questions
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What is Claude Haiku 4.5?
Claude Haiku 4.5 is Anthropic’s fast, lightweight Claude 4.5-series model optimized for low-latency, low-cost text and vision use cases.
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What is Claude Haiku 4.5 best suited for?
Claude Haiku 4.5 is best for high-volume workloads like chatbots, data processing, RAG, small agents, and rapid vision tasks where speed and price matter.
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What context window does Claude Haiku 4.5 support via LLM.API?
Via LLM.API, Claude Haiku 4.5 supports up to a 200K token context window for input and conversation history.
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How fast is Claude Haiku 4.5 on LLM.API?
Claude Haiku 4.5 is designed for very low latency, typically returning first tokens in well under a second for short prompts.
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What modalities does Claude Haiku 4.5 support?
Claude Haiku 4.5 supports text input and output plus image input, enabling multimodal reasoning over documents, screenshots, and photos.
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How is Claude Haiku 4.5 priced when used through LLM.API?
Claude Haiku 4.5 is exposed through LLM.API’s own metered pricing, which may differ from Anthropic’s direct per-token rates.
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How do I call Claude Haiku 4.5 through the LLM.API gateway?
You select the Claude Haiku 4.5 model identifier in LLM.API requests, send prompts using the unified schema, and receive responses in a standard format.
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How does Claude Haiku 4.5 compare to Claude Sonnet 4.5?
Compared to Claude Sonnet 4.5, Haiku 4.5 is cheaper and faster but somewhat weaker on complex reasoning and highly advanced tasks.
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What are the main limitations of Claude Haiku 4.5?
Claude Haiku 4.5 can hallucinate, struggle with very complex reasoning, and should not be solely trusted for safety-critical or legally binding decisions.
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Can Claude Haiku 4.5 handle streaming responses on LLM.API?
Yes, Claude Haiku 4.5 supports token streaming through LLM.API so you can start processing output before the full response is generated.