Powered by Anthropic
Claude Sonnet 4.5
- Text Generation
Claude Sonnet 4.5 is an Anthropic large language model optimized for software development, computer use, and agentic workflows, offering strong performance on coding and reasoning tasks at mid-tier pricing. It is part of the Claude 4.5 generation and is available through multiple cloud providers and enterprise platforms.
About the model
What is Claude Sonnet 4.5?
Claude Sonnet 4.5 is a mid-sized Claude 4.5 family model from Anthropic tuned for high-quality coding assistance, computer-use agents, and general-purpose language tasks. Its main use cases include software development support such as code generation, debugging, and refactoring, and serving as an AI agent for tools like IDE copilots, workflow automation, and enterprise integrations. It is also used for tasks like analysis, drafting, and problem solving where a balance of capability and cost is desired, and it belongs to the Claude Sonnet line that follows earlier Claude Sonnet 4 models within the broader Claude family.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn English conversations, follows complex instructions, maintains context, and produces helpful, safe, and coherent responses.
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Image Understanding
Interprets images to identify objects, text, layouts, and visual relationships, enabling grounded reasoning and explanation about visual content.
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Text Translation
Translates between major natural languages with attention to meaning and tone, supporting cross-lingual comprehension and communication tasks.
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Document OCR
Extracts and structures text from images or document snapshots, including screenshots and scanned pages, for downstream analysis or editing.
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Code and Tools
Analyzes and writes code, reasons stepwise, and coordinates tool usage or external systems for complex workflows and automation.
Use cases
6 Most Valuable Use Cases
- Software Code Generation
- Customer Support Chatbots
- Enterprise Document Analysis
- Legal Research Assistance
- Regulatory Change Monitoring
- Text Classification Tagging
Transparent pricing
Cost Comparison
Save up to ~70% vs direct Anthropic Sonnet 4.5 pricing with lower latency and higher throughput.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.80 | $4.00 | 200K |
| Anthropic | US East | ~220ms | ~40 tps | 99.9% | ~$2.50 | ~$10.00 | 200K |
| Anthropic | EU West | ~260ms | ~32 tps | 99.9% | ~$2.70 | ~$10.80 | 200K |
| AWS Bedrock (Anthropic Claude Sonnet 4.5 equivalent) | US West | ~250ms | ~35 tps | 99.9% | ~$2.60 | ~$10.40 | 200K |
| Google Cloud Vertex AI (Anthropic Claude Sonnet 4.5 equivalent) | Global | ~240ms | ~38 tps | 99.9% | ~$2.55 | ~$10.20 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Sonnet 4.5 | GPT-4.1 Mini | Gemini 1.5 Flash |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $0.30 | $0.15 | $0.20 |
| Output Price ($/1M) | $1.50 | $0.60 | $0.80 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ~120 tps | ~150 tps | ~140 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (30 days)
- 2.1B
- Completion tokens generated (30 days)
- 32M
- API requests served (30 days)
- 99.96%
- Average 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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Intelligent AI Routing
Define routing rules once and dynamically send traffic across models and providers based on latency, cost, or quality—without changing your app code.
One endpoint, every model. -
Cost-Aware Orchestration
Automatically choose cheaper models for non-critical paths, enforce spend caps, and compare provider pricing to keep AI costs predictable at scale.
Optimize every token. -
Resilient Fallback Flows
Configure smart fallbacks so if a provider fails, times out, or degrades, traffic seamlessly fails over to backups—no user-visible downtime.
Never ship a 500. -
Full-Stack Observability
Trace every request across providers with logs, metrics, and latency breakdowns so you can debug prompts, tune routing, and meet SLOs confidently.
See every token hop. -
Task-Level Abstractions
Declare tasks like chat, tools, RAG, or structured extraction once and run them on any underlying model without rewriting integration logic.
Think tasks, not models. -
High-Throughput Batching
Batch thousands of inferences into a single call with concurrency controls and retries, maximizing throughput while keeping provider limits and costs in check.
Scale to millions of calls.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose LLM for coding, writing, and analysis tasks.
- You need good reasoning and explanation quality for agents, copilots, or tutoring systems.
- You need safe, conservative behavior with robust alignment and content filtering defaults.
- Your use case involves multi-step problem solving without extreme token or latency constraints.
- Your use case involves natural-language data pipelines, classification, and extraction with reliable outputs.
- You need a capable assistant for code review, refactoring, and generating small to medium programs.
- Your use case involves brainstorming and editing high-quality long-form English text and documentation.
Avoid if...
- You need the absolute highest-end reasoning where only frontier models like Opus are acceptable.
- You need ultra-low-latency responses for interactive applications with strict real-time guarantees.
- You need guaranteed support for very long multi-hundred-page contexts without summarization strategies.
- Your workload requires heavy on-device or fully offline deployment without cloud connectivity.
- Your workload requires specialized multimodal capabilities beyond standard text and limited vision support.
- You need the cheapest possible token costs and will sacrifice quality for price.
- Your workload requires deterministic, reproducible outputs across time-sensitive regulatory or audit contexts.
FAQ
Frequently Asked Questions
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What is Claude Sonnet 4.5?
Claude Sonnet 4.5 is an Anthropic large language model focused on strong reasoning, coding, and general-purpose assistance with efficient performance.
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What is Claude Sonnet 4.5 best suited for?
Claude Sonnet 4.5 is best for production workloads requiring a balance of quality, cost, and speed across coding, agents, and complex reasoning tasks.
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How is Claude Sonnet 4.5 priced when used through LLM.API?
Claude Sonnet 4.5 pricing is defined by LLM.API’s unified billing layer, which may differ from Anthropic’s direct prices; check LLM.API pricing documentation.
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What context window does Claude Sonnet 4.5 support via LLM.API?
Claude Sonnet 4.5 supports a long context window suitable for large documents and multi-step workflows; exact limits depend on LLM.API’s configuration.
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How fast is Claude Sonnet 4.5 in terms of latency?
Claude Sonnet 4.5 targets mid-range latency, generally faster than larger flagship models while slower than smaller lightweight models under similar conditions.
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Which modalities does Claude Sonnet 4.5 support?
Claude Sonnet 4.5 supports text input and output, and image understanding when enabled by the underlying Anthropic and LLM.API deployment.
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How do I access Claude Sonnet 4.5 through the LLM.API gateway?
You call the unified LLM.API completions or chat endpoint and specify the Claude Sonnet 4.5 model identifier in the request payload.
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How does Claude Sonnet 4.5 compare to larger Anthropic models?
Claude Sonnet 4.5 typically offers lower cost and latency than Anthropic’s largest models while providing slightly lower peak capability on the hardest tasks.
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How does Claude Sonnet 4.5 compare to smaller Anthropic models?
Claude Sonnet 4.5 generally delivers higher reasoning quality and code performance than smaller Anthropic models at the expense of moderately higher cost and latency.
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What are the main limitations of Claude Sonnet 4.5?
Claude Sonnet 4.5 can still hallucinate, follow incorrect instructions, or misinterpret ambiguous inputs, so critical outputs require validation and alignment checks.
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Does Claude Sonnet 4.5 support function calling or tool use via LLM.API?
Yes, Claude Sonnet 4.5 can be used with LLM.API’s tool-calling or function-calling abstractions when you define tools in the request schema.