- Text Generation
Ministral 3 3B 2512 is a 3-billion-parameter variant in Mistral’s Ministral 3 family, designed as a compact, efficient language model. It targets scenarios where a smaller…
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Claude Opus 4.7
- Text Generation
Claude Opus 4.7 is Anthropic’s most capable generally available large language model, designed for advanced coding, long-horizon agentic workflows, and high-resolution vision tasks. It emphasizes stronger multi-step reasoning, reliability on complex work, and improved instruction following compared to earlier Opus releases.
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?
Claude Opus 4.7 is a flagship large language model from Anthropic optimized for frontier-level coding, reasoning, and knowledge work. It is used for complex software engineering and agentic automation, where it can plan and execute long-running, multi-tool workflows with minimal oversight. It is also applied to professional productivity tasks such as working with documents, spreadsheets, and presentations while leveraging a long context window and improved vision capabilities. Claude Opus 4.7 is part of the Claude 4 model family and succeeds earlier Opus versions like Claude Opus 4.6 as Anthropic’s top generally available model.
Model capabilities
5 Core Capabilities
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Advanced Dialogue
Engages in complex, context-aware conversations, following instructions, maintaining long context, and adapting tone to user needs.
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Code Reasoning
Understands, writes, and debugs code in multiple languages, explaining logic, algorithms, and software design choices in detail.
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Image Understanding
Interprets images, identifying objects, text, layout, and visual relationships to support description, analysis, and reasoning tasks.
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Language Translation
Translates between major languages, preserving meaning and tone while handling idioms, technical terms, and long-form text.
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Visual Text Extraction
Extracts and structures text from images, screenshots, and scanned documents, enabling search, analysis, and downstream processing.
Use cases
6 Most Valuable Use Cases
- Complex Code Generation
- Enterprise Knowledge Work
- Legal Case Analysis
- Workflow Monitoring Agents
- Financial Report Drafting
- Content Tagging Automation
Transparent pricing
Cost Comparison
Save up to ~65% vs Claude Opus 4.7 retail pricing with LLM API.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $10.00 | $30.00 | 200K |
| Anthropic | US East | ~300ms | ~60 tps | 99.9% | ~$30.00 | ~$60.00 | 200K |
| Amazon Bedrock | US West | ~350ms | ~50 tps | 99.9% | ~$32.00 | ~$64.00 | 200K |
| Google Cloud Vertex AI | Global | ~320ms | ~55 tps | 99.9% | ~$34.00 | ~$68.00 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus 4.7 | GPT-4.1 | Gemini 1.5 Pro |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $15.00 | $15.00 | $7.50 |
| Output Price ($/1M) | $75.00 | $60.00 | $30.00 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | ~40 tps | ~50 tps | ~45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 82B
- Prompt tokens processed (30 days)
- 61B
- Completion tokens generated (30 days)
- 7.4M
- API requests served (30 days)
- 99.8%
- 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
Dynamically route requests across models and providers based on latency, cost, and performance. One endpoint abstracts away vendor differences and keeps your stack future-proof.
One endpoint, any model -
Cost-Aware Orchestration
Optimize every call with price-aware routing, cheaper fallbacks, and configurable caps. Ship powerful AI features while keeping your unit economics predictable and under control.
Max performance, min spend -
Resilient Fallbacks
Define automatic failover chains so your app keeps working through timeouts, rate limits, or outages. No more hardcoding provider-specific recovery logic in your codebase.
Never ship brittle calls -
Full-Stack Observability
Get unified traces, metrics, and logs across all AI providers in one place. Debug bad outputs, compare models, and tune prompts with production-grade visibility.
See every token, everywhere -
Task-Level Abstractions
Describe tasks like chat, extraction, or tools—not model APIs. LLM.API normalizes capabilities so you can swap models without rewriting business logic or prompts.
Code to tasks, not models -
High-Throughput Batching
Batch thousands of requests into efficient, provider-optimized calls. Cut latency, slash API overhead, and unlock scalable workloads like evaluations, backfills, and bulk inference.
Scale from 10 to 10M
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a highly capable general-purpose assistant for complex reasoning and nuanced instruction following.
- You need strong performance on multi-step problem solving, analysis, and code explanation tasks.
- Your use case involves detailed writing, editing, or drafting of long-form professional content.
- Your use case involves in-depth data analysis, synthesis, or summarization from long inputs.
- You need robust safety tuning and conservative behavior for sensitive or high-risk domains.
- Your use case involves advanced conversational agents that must maintain coherent context over time.
Avoid if...
- You need the absolute lowest-cost model for very high-volume, low-value API calls.
- You need ultra-low latency responses for real-time or embedded edge applications.
- Your workload requires intensive image, audio, or multimodal processing beyond text-only capabilities.
- You need on-premise or fully self-hosted deployment rather than a cloud API service.
- You need guaranteed compatibility with non-Anthropic tooling, SDKs, or proprietary ecosystem features.
- Your workload requires fine-grained model customization or training beyond prompt engineering options.
FAQ
Frequently Asked Questions
-
What is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic’s flagship large language model, optimized for complex reasoning, coding, and high‑accuracy enterprise workloads.
-
What is the context window of Claude Opus 4.7 via LLM.API?
Claude Opus 4.7 supports a context window of up to 200,000 tokens when accessed through LLM.API.
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How fast is Claude Opus 4.7 on LLM.API?
Typical latencies range from a few hundred milliseconds for short prompts to several seconds for long, streaming responses, depending on load and prompt size.
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What modalities does Claude Opus 4.7 support?
Claude Opus 4.7 supports text input and output, plus image inputs for vision understanding, but does not generate images or audio.
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How is Claude Opus 4.7 priced on LLM.API?
Claude Opus 4.7 is billed per token for prompts and completions, with exact rates defined in your LLM.API pricing plan.
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How do I call Claude Opus 4.7 through the LLM.API?
Specify the model name "claude-opus-4.7" in your LLM.API request and authenticate with your LLM.API key as usual.
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What is Claude Opus 4.7 particularly good at?
Claude Opus 4.7 excels at long‑form reasoning, advanced coding assistance, multi‑step data analysis, and following detailed business or product instructions.
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How does Claude Opus 4.7 compare to other Anthropic Claude models?
Claude Opus 4.7 generally offers stronger reasoning and coding performance than mid‑tier Claude models, at higher cost and latency.
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What are the main limitations of Claude Opus 4.7?
Claude Opus 4.7 can still hallucinate, lacks real‑time internet access, and should not be solely relied on for safety‑critical or legally binding decisions.
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Does Claude Opus 4.7 support streaming responses on LLM.API?
Yes, Claude Opus 4.7 supports token‑streaming responses when you enable streaming mode in your LLM.API request.
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