- Instruction Following
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Claude Opus 4.7 (Fast)
- Instruction Following
Claude Opus 4.7 (Fast) is an Anthropic large language model variant optimized to provide high-quality Claude Opus-level reasoning with reduced latency. It is notable for aiming to balance top-tier capability with faster response speeds for interactive applications.
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 4.7 (Fast)?
Claude Opus 4.7 (Fast) is a fast, high-capability configuration of Anthropic’s Claude Opus large language model designed to deliver strong reasoning and language understanding with improved throughput. It is used for tasks like complex question answering, multi-step reasoning, and drafting or editing content where near–frontier quality is required but responsiveness matters. It is also applied in chatbots, productivity tools, and developer workflows that need powerful models integrated into real-time user experiences. It belongs to the Claude Opus family of models from Anthropic, which evolve through iterative versions that improve capability, safety, and performance characteristics such as speed.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, follows complex instructions, and maintains context for detailed, helpful, and coherent assistance.
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Document Analysis
Summarizes, critiques, and restructures long or technical documents, extracting key points and answering questions about the content.
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Image Understanding
Interprets images, identifying objects, text, layout, and visual patterns to support explanations, descriptions, and downstream reasoning.
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Text Recognition
Reads and transcribes textual content from images or screenshots, enabling extraction of information from visually embedded documents.
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Language Translation
Translates text between multiple languages while preserving meaning, tone, and style for both short passages and longer documents.
Use cases
6 Most Valuable Use Cases
- Software Code Generation
- Customer Support Chatbots
- Enterprise Document Analysis
- Legal Research Assistance
- Contract Monitoring Alerts
- Business Strategy Consulting
Transparent pricing
Cost Comparison
Save up to ~70% vs standard Claude Opus 4.7 (Fast) pricing
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~120 tps | 99.99% | ~$9.00 | ~$27.00 | 200K |
| Anthropic | US East | ~400ms | ~60 tps | 99.9% | ~$30.00 | ~$75.00 | 200K |
| Amazon Bedrock | US West | ~420ms | ~55 tps | 99.9% | ~$32.00 | ~$80.00 | 200K |
| Google Cloud | Global | ~380ms | ~50 tps | 99.9% | ~$28.00 | ~$70.00 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.7 (Fast) | GPT-4.1 Preview | Gemini 1.5 Pro |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $3.00 | $5.00 | $3.50 |
| Output Price ($/1M) | $15.00 | $15.00 | $10.50 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ~80 tps | ~60 tps | ~50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 38.5B
- Prompt tokens processed (last 30 days)
- 11.2M
- API requests served (last 30 days)
- 41.7B
- Completion tokens generated (last 30 days)
- 99.8%
- Average 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
Dynamically route each request to the best model across providers based on latency, capability, and cost—without changing your client code or deployment setup.
One endpoint, every model. -
Cost-Aware Control
Set hard budgets, price caps, and tiered routing rules so LLM.API automatically balances performance and spend across premium and cheap models per request.
Optimize performance per dollar. -
Resilient Fallbacks
Define graceful failover chains so if a model or provider degrades, traffic automatically falls back to healthy alternatives—no downtime, no emergency redeploys.
Stay up, even when they’re down. -
Deep Observability
Get unified logs, traces, and metrics for every provider and model in one place, making debugging, performance tuning, and regression tracking actually manageable.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, constraints, and tools once and let LLM.API pick and orchestrate the right models, prompts, and tools for each request automatically.
Think tasks, not models. -
High-Throughput Batching
Send massive batches through one endpoint while LLM.API optimizes concurrency, chunking, and provider limits—cutting costs and latency for large-scale workloads.
Scale up without re-architecting.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose model that balances reasoning quality with faster responses.
- You need robust multi-turn chat for agents, copilots, or complex user assistants.
- Your use case involves moderately complex analysis, writing, or coding without maximal depth.
- You need a reliable fallback when slower, top-tier flagship models are overkill or expensive.
- Your use case involves interactive tools where good reasoning and lower latency both matter.
- You need to prototype AI features quickly before committing to heavier, costlier models.
Avoid if...
- You need the absolute best Claude reasoning quality and can tolerate higher latency.
- You need ultra-long-context processing at the maximum context window Anthropic offers today.
- Your workload requires the lowest possible cost per token for massive batch inference.
- You need extremely tight real-time latency, such as high-frequency trading or gaming.
- Your workload requires specialized vision, audio, or multimodal capabilities beyond text-focused tasks.
- You need a fully open-source, self-hostable model without dependence on a cloud provider.
FAQ
Frequently Asked Questions
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What is Claude Opus 4.7 (Fast)?
Claude Opus 4.7 (Fast) is an Anthropic large language model variant optimized for lower latency while retaining strong reasoning and coding capabilities.
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What is Claude Opus 4.7 (Fast) best suited for?
It is best for complex reasoning, multi-step tool use, code generation, and production chatbots where responsiveness matters more than absolute peak accuracy.
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How is Claude Opus 4.7 (Fast) priced when used through LLM.API?
Pricing is pay-per-token via LLM.API, with exact input and output token rates defined in the LLM.API model pricing table.
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What context window does Claude Opus 4.7 (Fast) support on LLM.API?
Claude Opus 4.7 (Fast) supports a large context window determined by LLM.API’s Anthropic integration limits, typically suitable for long conversations and multi-file prompts.
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How fast is Claude Opus 4.7 (Fast) compared to the standard Opus variant?
It is tuned for lower latency and higher throughput than the standard Opus tier, making it better for interactive and high-traffic applications.
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Which modalities does Claude Opus 4.7 (Fast) support via LLM.API?
Through LLM.API it supports text input and output, and may support image input depending on the configured capabilities in your LLM.API account.
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How do I call Claude Opus 4.7 (Fast) through the LLM.API gateway?
Specify the model name "Claude Opus 4.7 (Fast)" in your LLM.API request payload using the standard chat or completion endpoint format.
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How does Claude Opus 4.7 (Fast) compare to other Anthropic models on LLM.API?
It typically offers a balance of Opus-level reasoning quality with performance characteristics closer to faster Anthropic tiers, at intermediate cost.
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What limitations should I be aware of when using Claude Opus 4.7 (Fast)?
It can still hallucinate, may struggle with highly domain-specific data without grounding, and must respect LLM.API context, rate, and safety limits.
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Does Claude Opus 4.7 (Fast) support tools, functions, or structured outputs via LLM.API?
Yes, it can be used with LLM.API’s tool-calling and JSON-structured output features where supported for Anthropic models.
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