- Instruction Following
Nova Premier 1.0 is Amazon’s most capable multimodal Nova-family model, optimized for complex reasoning with a very large 1M-token context window.
Powered by Anthropic
Claude Opus 4.8
- Instruction Following
Claude Opus 4.8 is a large language model from Anthropic’s Claude family, designed for high-level reasoning, detailed writing assistance, and complex problem solving. It emphasizes helpfulness, safety, and reliability across a wide range of professional and creative tasks.
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 4.8?
Claude Opus 4.8 is an Anthropic large language model focused on advanced natural language understanding and generation. It is used for tasks such as drafting and editing complex documents, answering technical or domain-specific questions, and supporting research and analysis workflows. It also assists with creative writing, brainstorming, and conversational applications where nuanced, context-aware responses are important. Claude Opus 4.8 belongs to Anthropic’s Claude model family, succeeding earlier Claude generations and related Opus variants.
Model capabilities
5 Core Capabilities
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Advanced Chat
Engages in complex, multi-turn conversations, following nuanced instructions, maintaining context, and adapting tone to different user needs.
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Code Reasoning
Understands, writes, and explains code across multiple languages, assisting with debugging, refactoring, and algorithmic problem-solving tasks.
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Image Understanding
Interprets images to identify objects, text, and visual relationships, supporting tasks like description, analysis, and high-level reasoning.
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Document OCR
Extracts and structures text from images or scanned documents, supporting downstream search, analysis, and content transformation workflows.
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Multilingual Translation
Translates between many languages, preserving meaning and tone while handling idioms, technical terminology, and longer documents.
Use cases
6 Most Valuable Use Cases
- Complex Code Generation
- Enterprise Knowledge Search
- Legal Document Review
- Financial Report Analysis
- Customer Support Automation
- Business Strategy Assistance
Transparent pricing
Cost Comparison
Save up to ~80% vs standard Claude Opus APIs with LLM API.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $4.00 | $16.00 | 200K |
| Anthropic | US East | ~220ms | ~40 tps | 99.9% | $15.00 | $75.00 | 200K |
| AWS Bedrock | US West | ~260ms | ~35 tps | 99.9% | ~$16.00 | ~$80.00 | ~200K |
| Google Cloud Vertex AI | Global | ~250ms | ~30 tps | 99.9% | ~$16.00 | ~$80.00 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.8 | Claude 3 Opus | GPT-4.1 | Gemini 1.5 Pro |
|---|---|---|---|---|
| Model Type | LLM | LLM | LLM | LLM |
| Context Window | — | 200K tokens | 128K tokens | 2M tokens |
| Max Output Tokens | — | 4K–8K tokens | 4K tokens | 8K tokens |
| Input Price ($/1M tokens) | — | $15.00 | $5.00 | $7.00 |
| Output Price ($/1M tokens) | — | $75.00 | $15.00 | $21.00 |
| Avg Latency | — | — | — | — |
| Throughput | — | — | — | — |
| Uptime | — | — | — | — |
30-day usage via LLM API
- 28.4B
- Prompt tokens processed (30 days)
- 7.9B
- Completion tokens generated (30 days)
- 12.3M
- API requests served (30 days)
- 99.95%
- 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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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, capability, and constraints—no client changes or new SDKs required.
One endpoint, any model -
Cost-Optimized Scaling
Balance price and performance with dynamic model selection, per-call controls, and usage limits so you can keep bills predictable even as traffic spikes.
Lower spend, same quality -
Resilient Fallback Logic
Define automatic failover rules so if a model or provider degrades, requests are retried on healthy alternatives without errors leaking into your app.
No more AI 500s -
End-to-End Observability
Get unified logs, traces, and metrics across all providers so you can debug prompts, compare models, and monitor latency from a single dashboard.
See every token -
Task-Aware Orchestration
Express higher-level tasks—chat, generation, tools, RAG—once and let LLM.API pick the right models, parameters, and flows for each request.
Describe tasks, not models -
High-Throughput Batch
Send large batches of prompts in a single call with automatic parallelization, rate-limit smoothing, and retries to maximize throughput across providers.
Millions of calls, one API
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a frontier-level general-purpose model for complex reasoning and problem solving.
- You need high-quality long-form writing, such as reports, documentation, or technical briefs.
- Your use case involves nuanced analysis of long documents, contracts, or research papers.
- Your use case involves multi-step coding tasks, refactoring, and explaining non-trivial codebases.
- You need strong instruction-following and safe, aligned behavior for consumer-facing assistants.
- Your use case involves detailed brainstorming, ideation, and refining product or design concepts.
Avoid if...
- You need the absolute lowest-cost model for simple classification or routing tasks.
- You need ultra-low-latency responses for real-time interaction on constrained devices.
- Your workload requires extremely high request throughput where per-token cost dominates value.
- You need heavy vision, audio, or multimodal processing that this text-focused model lacks.
- You need an on-premise or fully self-hosted solution rather than a cloud API.
- Your workload requires strict model determinism and reproducibility across many rapid deployments.
FAQ
Frequently Asked Questions
-
What is Claude Opus 4.8?
Claude Opus 4.8 is a flagship Anthropic large language model focused on high reasoning ability, complex analysis, and high-quality natural language generation.
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What is Claude Opus 4.8 best suited for?
Claude Opus 4.8 is best for complex multi-step reasoning, advanced coding, data analysis, long-form writing, and high-stakes assistant-style interactions.
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How is Claude Opus 4.8 priced when accessed via LLM.API?
Claude Opus 4.8 pricing on LLM.API follows LLM.API’s own per-token rates, which may differ from Anthropic’s direct pricing; check LLM.API’s pricing page.
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What context window does Claude Opus 4.8 support on LLM.API?
Claude Opus 4.8 supports long-context interactions on LLM.API; refer to the model’s context window specification in the LLM.API documentation for exact token limits.
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How fast is Claude Opus 4.8 in terms of latency?
Claude Opus 4.8 generally has higher latency than smaller Anthropic models due to its size, especially for very long prompts or outputs.
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Which modalities does Claude Opus 4.8 support through LLM.API?
Through LLM.API, Claude Opus 4.8 supports text input and output; multimodal capabilities depend on LLM.API’s enabled features for this model.
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How do I call Claude Opus 4.8 via the LLM.API endpoint?
Specify the model name "Claude Opus 4.8" in your LLM.API request payload and authenticate with your LLM.API key to start generating responses.
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How does Claude Opus 4.8 compare to smaller Anthropic models on LLM.API?
Claude Opus 4.8 provides stronger reasoning, coding, and analysis capabilities but is slower and more expensive than smaller Anthropic models exposed on LLM.API.
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Can Claude Opus 4.8 browse the web or access external tools?
Claude Opus 4.8 itself cannot browse; any tool use or web access must be implemented via LLM.API or your own middleware.
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What are the main limitations of Claude Opus 4.8?
Claude Opus 4.8 can still hallucinate, produce incorrect code or facts, and may struggle with very domain-specific or unseen proprietary data.
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Is Claude Opus 4.8 suitable for real-time applications?
Claude Opus 4.8 can power interactive apps, but its higher latency makes it less ideal for strict real-time or ultra-low-latency constraints.
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Does Claude Opus 4.8 preserve conversational state across requests on LLM.API?
Claude Opus 4.8 is stateless; you must send prior messages in each request to maintain conversation context via LLM.API.
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