- Instruction Following
GPT-5.2 Chat is an OpenAI conversational language model designed for interactive dialogue and task assistance. It focuses on providing coherent, context-aware responses across a wide range…
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Claude Opus 4.8 (Fast)
- Instruction Following
Claude Opus 4.8 (Fast) is Anthropic’s flagship Claude Opus 4.8 model running in a special fast mode that delivers significantly higher output token throughput at premium pricing. It is designed for latency-sensitive workloads that need Opus‑level intelligence with substantially reduced response times.
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 (Fast)?
Claude Opus 4.8 (Fast) is a fast‑mode configuration of Anthropic’s Claude Opus 4.8 large language model, offering up to roughly 2.5× higher output speed than the standard mode at a higher per‑token price. It is mainly used for interactive applications, agentic workflows, and coding tools where reduced latency is critical but users still want Opus‑grade reasoning and reliability. It is also used for real‑time or near‑real‑time knowledge work, copilots, and developer tools that must respond quickly while handling large contexts. It belongs to the Claude Opus 4.x family of Anthropic’s top‑tier models and builds directly on Claude Opus 4.7.
Model capabilities
5 Core Capabilities
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Advanced Chat
Handles complex, multi-turn conversations, following nuanced instructions and maintaining context for long, detailed dialogues and workflows.
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Code Assistance
Helps read, write, and debug code in multiple languages, explaining logic, suggesting fixes, and improving code clarity and structure.
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Vision Understanding
Interprets images, recognizing objects, text, and layouts, and answers questions about visual content in detail and context.
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Text Translation
Translates text between many languages while preserving meaning, tone, and style, suitable for technical, conversational, or formal content.
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Document OCR
Extracts and structures text from images or scanned documents, enabling search, summarization, and analysis of visual text content.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Review
- Regulatory Change Monitoring
- E-commerce Product Assistants
- Code Generation and Debugging
Transparent pricing
Cost Comparison
Save up to ~50% vs major Claude Opus APIs with LLM API
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~150ms | ~80 tps | 99.99% | ~$12.00 | ~$60.00 | 200K |
| Anthropic | US East | ~350ms | ~30 tps | 99.9% | ~$24.00 | ~$120.00 | 200K |
| Amazon Bedrock | US West | ~420ms | ~25 tps | 99.9% | ~$26.00 | ~$130.00 | 200K |
| Google Cloud Vertex AI | Global | ~380ms | ~28 tps | 99.9% | ~$25.00 | ~$125.00 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.8 (Fast) | Claude 3.5 Sonnet (Latest API) | GPT-4.1 Mini (OpenAI) | GPT-4.1 (OpenAI) |
|---|---|---|---|---|
| Context Window | — | 200K tokens | 128K tokens | 128K tokens |
| Max Output Tokens | — | — | — | — |
| Input Price ($/1M tokens) | — | $3.00 | $0.15 | $5.00 |
| Output Price ($/1M tokens) | — | $15.00 | $0.60 | $15.00 |
| Avg Latency | Lower than standard Opus 4.8 | — | Low (optimized for speed) | — |
| Throughput | — | — | High (mini-tier) | — |
| Uptime | — | — | — | — |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 19B
- Completion tokens generated (last 30 days)
- 27M
- API requests served (last 30 days)
- 99.9%
- Avg 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
Intelligently route each request to the best model across providers based on latency, cost, or quality. One endpoint, dynamic routing, no client changes.
One endpoint, every model. -
Cost-Aware Orchestration
Automatically optimize spend with per-request cost controls, smart downgrades, and provider mixing. Hit your budget targets without manually tuning every call.
More value per token. -
Resilient Fallback Flows
Define provider and model fallbacks that trigger on errors, timeouts, or quality checks. Keep critical paths up even when individual APIs fail.
Never ship a dead-end. -
End-to-End Observability
Trace every call across providers with logs, metrics, and latency breakdowns. Debug fast, tune routing strategies, and prove reliability to stakeholders.
See every token’s journey. -
Task-Level Abstractions
Describe what you want—chat, classify, extract, search—while LLM.API picks the right models and prompts. Ship complex AI features without wiring every detail.
Think in tasks, not models. -
High-Throughput Batch Jobs
Run large batch workloads across providers with automatic throttling, retries, and progress tracking. Process millions of items without building batch infrastructure.
Batch at platform scale.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose model for coding help, debugging, and refactoring.
- You need solid reasoning on typical tasks without paying for Anthropic’s top models.
- Your use case involves chatbot-style assistants that must respond helpfully and coherently.
- Your use case involves generating or editing technical documentation, reports, and knowledge articles.
- You need a capable model for multi-language text understanding but will communicate results in English.
- Your use case involves moderate-length tool-calling or API orchestration with reliable structure adherence.
Avoid if...
- You need the absolute highest reasoning quality Anthropic offers, regardless of cost or speed.
- You need ultra-low-latency real-time interactions, such as high-frequency trading or live control.
- Your workload requires processing extremely long documents near the provider’s maximum context limits.
- You need heavy vision, audio, or multimodal support beyond primarily text-centric capabilities.
- Your workload requires the very cheapest possible inference cost across billions of daily tokens.
- You need full offline or on-prem deployment instead of managed, cloud-hosted Anthropic services.
FAQ
Frequently Asked Questions
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What is Claude Opus 4.8 (Fast)?
Claude Opus 4.8 (Fast) is an Anthropic large language model variant optimized for lower latency while preserving strong reasoning and coding capabilities.
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What is Claude Opus 4.8 (Fast) best suited for?
It is best for complex reasoning, code generation, multi-step agents, and production applications needing strong intelligence with faster responses than the standard Opus tier.
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How is Claude Opus 4.8 (Fast) priced when accessed through LLM.API?
LLM.API applies its own per-token pricing for Claude Opus 4.8 (Fast); check your LLM.API dashboard or pricing docs for exact current rates.
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What context window does Claude Opus 4.8 (Fast) support on LLM.API?
Claude Opus 4.8 (Fast) supports long-context prompts via LLM.API; refer to the model card for the exact maximum token limit.
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How fast is Claude Opus 4.8 (Fast) compared to the regular Opus model?
Claude Opus 4.8 (Fast) is tuned for noticeably lower latency and higher throughput than standard Opus, making it better for interactive or high-traffic workloads.
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Which modalities does Claude Opus 4.8 (Fast) support?
Claude Opus 4.8 (Fast) supports text input and output, and may support images depending on Anthropic and LLM.API configuration at request time.
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How do I call Claude Opus 4.8 (Fast) via the LLM.API gateway?
Specify the model name "claude-opus-4.8-fast" (or the exact identifier from LLM.API docs) in your LLM.API completion or chat request payload.
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How does Claude Opus 4.8 (Fast) compare to other Anthropic models on LLM.API?
Compared to smaller Claude models, Opus 4.8 (Fast) generally offers stronger reasoning and coding quality at higher cost but still responsive speeds.
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What are the main limitations of Claude Opus 4.8 (Fast)?
It can still hallucinate, lacks real-time browsing or tools by default, and should not be relied on alone for critical legal, medical, or financial decisions.
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Can I use Claude Opus 4.8 (Fast) for streaming responses on LLM.API?
Yes, you can enable streaming in LLM.API requests to get token-by-token responses from Claude Opus 4.8 (Fast) for lower perceived latency.
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