- Text Generation
o3 Deep Research is an OpenAI model variant optimized for autonomous, long-horizon research tasks that combine web browsing, data analysis, and report generation. It focuses on…
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Claude Opus 4.5
- Text Generation
Claude Opus 4.5 is Anthropic’s frontier large language model optimized for advanced reasoning, coding, and long-context, agentic workflows. It is positioned as a flagship, high-intelligence model for demanding enterprise and developer use cases.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Claude Opus 4.5?
Claude Opus 4.5 is a large language model from Anthropic designed as a frontier reasoning system for complex tasks and long-horizon interactions. It is mainly used for sophisticated software engineering and code generation, autonomous or semi-autonomous agent workflows, and handling large-context enterprise workloads such as document analysis and planning. It also serves in research and high-stakes professional settings that require strong reasoning, safety-focused behavior, and long, tool-using sessions. Claude Opus 4.5 is part of the Claude Opus family, succeeding earlier Claude 4.x Opus models and sitting alongside related Claude 4.5 Sonnet and Haiku variants.
Model capabilities
5 Core Capabilities
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Advanced Dialogue
Engages in extended, context-rich conversations, following complex instructions and maintaining coherence across long multi-turn interactions.
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Code and Reasoning
Performs complex reasoning, writing and debugging code, analyzing algorithms, and solving multi-step technical problems across many languages.
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Multilingual Translation
Translates between major languages with strong fluency, preserving meaning, tone, and technical nuance in long or specialized texts.
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Visual Image Analysis
Understands uploaded images, describing content, layout, and relationships between objects, and answering detailed visual questions.
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Text Extraction OCR
Reads and extracts text from images, including screenshots and documents, handling varied fonts, layouts, and moderately challenging image quality.
Use cases
6 Most Valuable Use Cases
- Advanced Code Generation
- Complex Legal Research
- Contract Drafting Assistance
- Business Strategy Analysis
- Technical Tagging Automation
- Invoice Data Extraction
Transparent pricing
Cost Comparison
Save up to ~70% vs. direct Claude Opus 4.5 API pricing with LLM API.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~80 tps | 99.99% | $15.00 | $60.00 | 200K |
| Anthropic | US East | ~220ms | ~40 tps | 99.9% | ~$25.00 | ~$100.00 | 200K |
| OpenAI (closest: GPT-4.1-tier) | Global | ~210ms | ~50 tps | 99.9% | ~$30.00 | ~$60.00 | 128K |
| Google (closest: Gemini 1.5 Pro) | Global | ~230ms | ~35 tps | 99.9% | ~$20.00 | ~$80.00 | 1M |
| Azure (Anthropic via Azure AI) | US East | ~240ms | ~30 tps | 99.9% | ~$27.00 | ~$110.00 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.5 (Anthropic) | GPT-4.1 (OpenAI) | Gemini 1.5 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~260ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M tokens) | $15.00 | $10.00 | $7.50 |
| Output Price ($/1M tokens) | $75.00 | $30.00 | $30.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 40 tps | 50 tps | 45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (30 days)
- 21B
- Completion tokens generated (30 days)
- 8.4M
- API requests served (30 days)
- 99.96%
- Average API uptime (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
Define policies once, then let LLM.API dynamically route each request across providers and models based on cost, latency, or quality—without changing your application code.
One policy, many models. -
Cost-Aware Controls
Set hard budgets, price caps, and model preferences so LLM.API automatically chooses the most cost-efficient option while preserving target quality and performance.
Control spend per token. -
Automatic Fallback Chains
Configure fallback trees once and LLM.API seamlessly retries on alternate models or providers when timeouts, rate limits, or errors occur—no custom retry logic required.
Resilience by default. -
End-to-End Observability
Get structured logs, traces, and metrics across all providers in one place, making it easy to debug prompts, tune routing rules, and track model performance.
See every token hop. -
Task-Level Abstractions
Describe tasks—chat, generation, tools, RAG—at a high level and let LLM.API map them to the right models and parameters across vendors automatically.
Program to tasks, not models. -
High-Throughput Batch APIs
Ship massive workloads via optimized batch endpoints that handle chunking, parallelization, and retries, dramatically cutting latency and cost for large-scale inference jobs.
Scale workloads, not scripts.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a very strong general-purpose model for complex reasoning and coding tasks.
- You need high-quality natural language understanding and generation for nuanced, long-form content.
- Your use case involves multi-step analytical reasoning, such as debugging or data interpretation.
- Your use case involves assisting experts in law, finance, or scientific research workflows.
- You need reliable tool-use and orchestration capabilities in a larger agentic application stack.
- You need a frontier model for high-stakes question answering with strong instruction following.
Avoid if...
- You need the absolute lowest possible cost per token for large-scale batch workloads.
- You need ultra-low latency responses for real-time interactive systems or on-device experiences.
- Your workload requires strict on-premise deployment with no external cloud dependencies.
- You need highly specialized vision, audio, or multimodal capabilities beyond primarily text-focused models.
- You need a lightweight model for simple classification or routing where frontier quality is unnecessary.
- Your workload requires tight integration with an ecosystem that standardizes on a different provider.
FAQ
Frequently Asked Questions
-
What is Claude Opus 4.5?
Claude Opus 4.5 is Anthropic’s flagship large language model focused on high reasoning quality, complex problem solving, and reliable enterprise-grade outputs.
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What is Claude Opus 4.5 best suited for?
Claude Opus 4.5 is best for complex analytical tasks, long-form content generation, multi-step coding, and scenarios requiring strong reasoning and instruction following.
-
What context window does Claude Opus 4.5 support on LLM.API?
Claude Opus 4.5 supports up to a 200K token context window via LLM.API, suitable for large documents and multi-step interactions.
-
Which modalities does Claude Opus 4.5 support?
Claude Opus 4.5 supports text input and output; multimodal image or audio support is not available for this model on LLM.API.
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How is Claude Opus 4.5 priced on LLM.API?
Claude Opus 4.5 pricing on LLM.API is usage-based per input and output token, with specific rates defined in the LLM.API pricing documentation.
-
How fast is Claude Opus 4.5 in terms of latency?
Claude Opus 4.5 typically has higher latency than lighter models due to its size, making it slower but more capable for complex workloads.
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How do I call Claude Opus 4.5 through LLM.API?
You select the Claude Opus 4.5 model name in the LLM.API request payload, using the standard chat or completion endpoint for your language client.
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How does Claude Opus 4.5 compare to smaller Claude models?
Claude Opus 4.5 offers better reasoning and accuracy than smaller Claude tiers but with higher cost and latency per token.
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What are the main limitations of Claude Opus 4.5?
Claude Opus 4.5 can hallucinate, may reflect training data biases, lacks real-time browsing, and cannot access or remember data outside each request context.
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Can I use Claude Opus 4.5 for streaming responses on LLM.API?
Yes, Claude Opus 4.5 supports server-side streaming responses on LLM.API when you enable the streaming flag in your API request.
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