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Anthropic Claude Sonnet Latest
- Text Generation
Anthropic Claude Sonnet Latest refers to the most recent mid-tier Claude Sonnet language model from Anthropic, designed to balance strong intelligence with speed and cost-efficiency. It is commonly used as Anthropic’s default general-purpose assistant model in the Claude product and API.
About the model
What is Anthropic Claude Sonnet Latest?
Anthropic Claude Sonnet Latest is a production-grade large language model in Anthropic’s Claude Sonnet series, positioned as the balanced, mid-tier option between smaller Haiku and larger Opus models. It is mainly used for general-purpose chat assistants, writing and analysis, and knowledge work that require strong reasoning at lower latency and cost than flagship frontier models. It is also widely used for coding, tool use, and enterprise applications that need long-context processing and robust safety at scale. It belongs to Anthropic’s Claude model family, which is organized into Opus (flagship), Sonnet (balanced), and Haiku (lightweight) tiers that have evolved through multiple generations such as Claude 3.x and 4.x Sonnet.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn, context-aware conversations, following complex instructions and maintaining coherent, helpful dialogue across diverse topics.
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Image Understanding
Interprets images to identify objects, scenes, and relationships, supporting tasks like description, comparison, and visual context reasoning.
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Text Translation
Translates between multiple languages, preserving meaning and tone for general-purpose content, instructions, and user queries.
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Document OCR
Extracts and structures text from images or document photos, enabling search, summarization, and downstream processing of visual text content.
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Code and Tools
Understands and writes code, reasons step-by-step, and coordinates use of external tools or APIs when integrated into applications.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Review
- Regulatory Change Monitoring
- Marketing Copy Generation
- Code Generation Assistant
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and fastest access to Claude Sonnet–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~120 tps | 99.99% | $0.60 | $1.80 | 200K |
| Anthropic | US East | ~350ms | ~60 tps | 99.9% | ~$3.00 | ~$15.00 | 200K |
| Amazon Bedrock (Anthropic Claude Sonnet equivalent) | US West | ~420ms | ~45 tps | 99.9% | ~$3.20 | ~$16.00 | 200K |
| Google Cloud (Anthropic Claude Sonnet equivalent) | Global | ~400ms | ~50 tps | 99.9% | ~$3.40 | ~$17.00 | 200K |
| Azure (Anthropic Claude Sonnet equivalent) | EU West | ~380ms | ~55 tps | 99.9% | ~$3.60 | ~$18.00 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Anthropic Claude Sonnet Latest | OpenAI GPT-4.1 Mini | Google Gemini 1.5 Flash |
|---|---|---|---|
| Avg Latency | ~250ms | ~220ms | ~260ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M tokens) | $0.80 | $0.30 | $0.35 |
| Output Price ($/1M tokens) | $4.00 | $1.25 | $1.50 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | 45 tps | 50 tps | 40 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 185B
- Prompt tokens processed (30 days)
- 42B
- Completion tokens generated (30 days)
- 11.4M
- API requests served (30 days)
- 99.9%
- Avg uptime over 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 Model Routing
Automatically route each request to the best-fit model across providers based on latency, cost, and quality—no client changes required as your stack evolves.
One endpoint, every model -
Cost-Aware Execution
Control and predict spend with transparent pricing, per-provider budgets, and cost-based routing policies that keep experiments fast while production remains under budget.
Optimize every token -
Resilient Fallback Flows
Design multi-step failover strategies so if a provider degrades or times out, requests automatically retry on backup models without impacting your application.
Never drop a request -
Full-Stack Observability
Get centralized traces, metrics, and logs for every call across all providers, enabling rapid debugging, performance tuning, and regression detection from a single dashboard.
See every token hop -
Task-Level Abstractions
Define high-level tasks like chat, tools, or embeddings once, then swap underlying models or providers freely without rewriting business logic or prompt plumbing.
Code to tasks, not models -
High-Throughput Batch Jobs
Run large-scale inference workloads with parallelized, rate-aware batching that maximizes throughput, minimizes costs, and abstracts provider-specific batch quirks.
Ship batch at scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose assistant for coding help, analysis, and explanation.
- You need balanced performance across reasoning, writing, and coding without top-tier model costs.
- Your use case involves chat-style agents that must follow nuanced instructions reliably.
- Your use case involves drafting or editing long-form English text with good coherence.
- You need safe-by-default outputs with conservative handling of sensitive or harmful content.
- Your use case involves moderate-length tool use or function-calling within a multistep workflow.
- You need a dependable fallback or secondary model alongside more expensive frontier models.
Avoid if...
- You need state-of-the-art reasoning or coding performance rivaling the very latest frontier LLMs.
- Your workload requires ultra-long context handling for hundreds of pages in one prompt.
- You need highly specialized domain reasoning, like cutting-edge scientific or legal analysis.
- Your workload requires extremely low-latency responses for tight real-time user interactions.
- You need guaranteed deterministic outputs with strict reproducibility across many model invocations.
- Your workload requires heavy multimodal capabilities beyond standard text-focused interactions.
- You need a model explicitly optimized for small-device on-prem deployment with tiny footprints.
FAQ
Frequently Asked Questions
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What is Anthropic Claude Sonnet Latest?
Anthropic Claude Sonnet Latest is a balanced, general-purpose Claude 3.5 family model from ~Anthropic, exposed through the LLM.API unified gateway.
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What is the context window of Anthropic Claude Sonnet Latest?
Anthropic Claude Sonnet Latest supports up to a 200K token context window, suitable for long documents, multi-step tools, and complex conversations.
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What is Anthropic Claude Sonnet Latest best suited for?
It excels at high‑quality reasoning, coding assistance, multi-step problem solving, and robust general chat while offering better cost‑performance than flagship models.
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How is Anthropic Claude Sonnet Latest priced on LLM.API?
Pricing is metered per 1,000 tokens for input and output; check the LLM.API pricing page for the latest Anthropic Claude Sonnet rates.
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How fast is Anthropic Claude Sonnet Latest in terms of latency?
Latency depends on load and request size, but Sonnet typically offers mid‑range response times faster than Opus‑class models and slower than Haiku‑class models.
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What modalities does Anthropic Claude Sonnet Latest support?
Anthropic Claude Sonnet Latest supports text input and output, and can process images when configured for multimodal use via compatible LLM.API endpoints.
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How do I call Anthropic Claude Sonnet Latest through LLM.API?
Use the LLM.API endpoint with the model identifier for Anthropic Claude Sonnet Latest, passing your prompt, optional system instructions, and tool configuration if needed.
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How does Anthropic Claude Sonnet Latest compare to larger Claude models?
Sonnet generally offers similar reasoning quality at lower cost and latency than Opus‑class models but with slightly reduced peak capability on the hardest tasks.
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Does Anthropic Claude Sonnet Latest support function calling or tools via LLM.API?
Yes, when configured in LLM.API, it can consume structured tool definitions and return arguments for function calls to integrate external tools or APIs.
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What are key limitations of Anthropic Claude Sonnet Latest?
It can still hallucinate, lacks real‑time internet access without tools, and may underperform specialized or larger models on highly technical or domain‑specific tasks.