- Text Generation
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Claude Opus 4.6 (Fast)
- Text Generation
Claude Opus 4.6 (Fast) is an Anthropic large language model deployment variant that emphasizes reduced latency while retaining strong general-purpose reasoning and generation capabilities. It is designed to provide high-quality answers more quickly than standard Opus configurations.
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 4.6 (Fast)?
Claude Opus 4.6 (Fast) is a performance-optimized configuration of Anthropic’s Claude Opus large language model aimed at delivering fast, capable natural language understanding and generation. It is used for tasks such as interactive chat, drafting and editing text, and answering complex questions with lower response times. It also supports use cases like code assistance, data analysis workflows, and integration into products that require responsive AI features. It belongs to the Claude Opus family of Anthropic frontier models, which are successors to earlier Claude 2.x and Claude 3-series models.
Model capabilities
5 Core Capabilities
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Advanced Chat
Engages in complex, context-aware conversations, following nuanced instructions and maintaining coherence over long, multi-turn interactions.
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Image Reasoning
Interprets uploaded images, identifying key elements and relationships to support description, analysis, and problem-solving tasks.
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Code and Debugging
Reads, writes, and improves code in multiple languages, explaining logic, suggesting fixes, and helping debug software issues.
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Multilingual Translation
Translates between major languages with attention to tone and context, enabling cross-lingual understanding of documents and messages.
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Text Extraction
Extracts structured information from documents, screenshots, and other visual text inputs for summarization, analysis, or reformatting.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice And Receipt Parsing
- Legal Case Research Assistance
- Regulation And Policy Monitoring
- E-commerce Product Recommendations
- Code Generation And Review
Transparent pricing
Cost Comparison
Save up to ~70% vs direct Anthropic Claude Opus access
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 140ms | 120 tps | 99.99% | $6.00 | $18.00 | 200K |
| Anthropic | Global | ~220ms | ~60 tps | 99.9% | ~$18.00 | ~$54.00 | ~200K |
| AWS Bedrock | US East | ~260ms | ~80 tps | 99.9% | ~$19.00 | ~$57.00 | ~200K |
| Google Cloud (Vertex AI) | US Central | ~250ms | ~70 tps | 99.9% | ~$20.00 | ~$60.00 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.6 (Fast) | OpenAI o4-mini | Google Gemini 1.5 Pro |
|---|---|---|---|
| Avg Latency | ~250ms | ~220ms | ~350ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | ~$3.00 | $1.00 | ~$3.50 |
| Output Price ($/1M) | ~$15.00 | $5.00 | ~$10.00 |
| Max Output Tokens | 8K | 16K | 8K |
| Throughput | ~80 tps | ~100 tps | ~70 tps |
| Uptime | ~99.9% | ~99.9% | ~99.9% |
30-day usage via LLM API
- 46.8B
- Prompt tokens processed (last 30 days)
- 11.2M
- API requests served (last 30 days)
- 39.5B
- Completion tokens generated (last 30 days)
- 99.8%
- 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
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Execution
Optimize spend with per-request cost controls, smart model selection, and transparent usage metrics so you can scale AI features without surprise bills.
Lower spend, same output -
Resilient Fallback Logic
Define automatic cross-provider fallbacks to keep your app running through outages, rate limits, and model errors—no custom retry spaghetti required.
Stay online, automatically -
Deep Observability
Trace every request across models and providers with logs, timings, and costs in one place, making debugging and performance tuning actually actionable.
See every token hop -
Task-Level Orchestration
Describe high-level tasks instead of wiring raw prompts; LLM.API handles tool calls, model chaining, and state so you ship complex agents with fewer lines.
Ship workflows, not glue -
High-Throughput Batch
Run massive batches of prompts or tasks asynchronously with built-in queuing, retries, and cost tracking—perfect for backfills, evaluations, and data labeling.
Millions of calls, one API
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a fast Claude variant for interactive coding assistance and debugging sessions.
- You need strong general-purpose reasoning without paying for the very top tier.
- Your use case involves rapid iteration on product copy, emails, and short-form content.
- Your use case involves lightweight data extraction or transformation from short to medium texts.
- You need a responsive assistant for prototyping agents, tools, and workflow orchestration.
- Your use case involves chat-style UX where users expect strong quality and low latency.
Avoid if...
- You need the absolutely highest-quality reasoning and writing Anthropic offers regardless of speed.
- You need consistently optimal performance on extremely long, complex research or legal documents.
- Your workload requires ultra-cheap token pricing for massive batch or background jobs.
- You need specialized vision, audio, or multimodal capabilities beyond text understanding and generation.
- Your workload requires strict, proven performance on safety-critical medical, legal, or financial advice.
- You need deterministic, highly repeatable outputs where small model updates are unacceptable risks.
FAQ
Frequently Asked Questions
-
What is Claude Opus 4.6 (Fast)?
Claude Opus 4.6 (Fast) is an Anthropic large language model variant tuned for lower latency while preserving strong reasoning and coding capabilities.
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What is Claude Opus 4.6 (Fast) best suited for?
It is best for complex reasoning, multi-step code generation, and production chat agents that need faster responses than standard Claude Opus tiers.
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What is the context window of Claude Opus 4.6 (Fast)?
Claude Opus 4.6 (Fast) supports a large-context window suitable for long conversations and multi-file code, as configured by LLM.API.
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How fast is Claude Opus 4.6 (Fast) compared to other Claude models?
Claude Opus 4.6 (Fast) is optimized for reduced latency and higher throughput compared to the non-fast Opus variant on LLM.API.
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What modalities does Claude Opus 4.6 (Fast) support on LLM.API?
Claude Opus 4.6 (Fast) supports text input and output, and can be used in tool-calling and structured-output workflows via LLM.API.
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How do I access Claude Opus 4.6 (Fast) through the LLM.API gateway?
Specify the model name "claude-opus-4.6-fast" (or equivalent configured identifier) in your LLM.API completion or chat endpoint calls.
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How does Claude Opus 4.6 (Fast) compare to other Claude 4.x family models?
It generally offers faster and cheaper responses than the flagship Opus variant while being more capable than smaller Claude models on complex reasoning and coding tasks.
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What are the main limitations of Claude Opus 4.6 (Fast)?
It can still hallucinate, be sensitive to ambiguous prompts, and may be slightly less accurate than the highest-quality Claude Opus 4.6 configuration.
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Does Claude Opus 4.6 (Fast) support image or audio input on LLM.API?
On LLM.API, Claude Opus 4.6 (Fast) is currently available as a text-only model without native image or audio understanding.
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How is pricing for Claude Opus 4.6 (Fast) handled on LLM.API?
Your cost is determined by LLM.API’s per-token pricing for this model, billed separately for input and output tokens according to their posted rates.
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