- Instruction Following
Ling-2.6-flash is an open-weight, high-efficiency instruct language model from inclusionAI, optimized for fast responses, strong execution, and low token usage in real-world agent workflows.
Powered by Anthropic
Claude Opus Latest
- Instruction Following
Claude Opus Latest is Anthropic’s current flagship Opus-tier large language model, designed for complex reasoning, coding, and knowledge work with strong safety and alignment features. It represents the most capable version of the Claude Opus line available in Anthropic’s API and products at this time.
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 Latest?
Claude Opus Latest is Anthropic’s most capable Opus-series large language model exposed through its platform as the default or most recent Opus variant. It is primarily used for sophisticated reasoning and analysis tasks, such as research assistance, strategic writing, and complex problem solving. It is also widely applied to advanced software development workflows, including code generation, refactoring, and debugging in professional environments. It belongs to the Claude Opus family of models, which has evolved through multiple frontier releases building on earlier Claude 3 Opus and subsequent Opus 4.x generations.
Model capabilities
5 Core Capabilities
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Advanced Dialogue
Handles complex, multi-step conversations, following nuanced instructions and maintaining context for detailed reasoning and problem-solving tasks.
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Visual Reasoning
Interprets images, diagrams, and visual scenes, explaining content, relationships, and structure to support analysis and understanding.
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Multilingual Translation
Translates between major languages, preserving meaning and tone while handling informal phrasing and moderately technical subject matter.
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Document Analysis
Processes long documents, extracting key points, comparing sections, and answering detailed questions about their content and structure.
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Text Extraction
Reads text from provided images or screenshots and converts it into structured, editable text for downstream processing.
Use cases
6 Most Valuable Use Cases
- Complex Document Drafting
- Legal Research Assistance
- Customer Support Automation
- Business Data Analysis
- Code Generation and Review
- Regulatory Compliance Monitoring
Transparent pricing
Cost Comparison
Up to ~70% cheaper than Anthropic direct Opus pricing with better latency and uptime
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~80 tps | 99.99% | ~$5.00 | ~$10.00 | ~256K |
| Anthropic | US + EU | ~350ms | ~40 tps | 99.9% | ~$15.00 | ~$75.00 | ~200K |
| Amazon Bedrock | US East, US West, EU West, AP | ~420ms | ~35 tps | 99.9% | ~$16.00 | ~$80.00 | ~200K |
| Google Cloud Vertex AI | US, EU | ~400ms | ~30 tps | 99.9% | ~$17.00 | ~$80.00 | ~200K |
| OpenRouter | Global | ~320ms | ~25 tps | 99.5% | ~$12.00 | ~$60.00 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Opus Latest | GPT-4o Latest | Gemini 1.5 Pro Latest |
|---|---|---|---|
| Avg Latency | ~800ms | ~600ms | ~900ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $3.00 | $5.00 | $3.50 |
| Output Price ($/1M) | $15.00 | $15.00 | $10.50 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | ~40 tps | ~60 tps | ~35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 210M
- Completion tokens generated (last 30 days)
- 5.4M
- API requests served (last 30 days)
- 99.8%
- Average API 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, quality, or custom rules—without changing your integration or redeploying code.
One endpoint, any model -
Predictable AI Costs
Optimize spend with centralized rate limits, usage policies, and per-model pricing controls so you can experiment freely without blowing your AI budget.
Control cost, not ideas -
Resilient Fallback Logic
Automatically fail over to backup models or providers on timeouts and errors, keeping production workloads running even when upstream APIs are flaky.
No single point of failure -
End‑to‑End Observability
Trace every request across models and providers with logs, metrics, and latency breakdowns so you can debug incidents and tune performance in one place.
See every token, everywhere -
Task‑Level Abstractions
Describe the job—chat, generate, extract, classify—and let LLM.API pick the right model and parameters, so your app code stays clean and future‑proof.
Code to tasks, not models -
High‑Throughput Batch Runs
Submit massive batches across providers through a single API, with automatic chunking, retries, and progress tracking for pipelines, backfills, and evaluations.
Ship thousands, not one
Decision guide
When to Use — When NOT to Use
Use it if...
- You need very strong general reasoning for complex, multi‑step problem solving or planning.
- You need high-quality, well-structured natural language generation for reports, briefs, or explanations.
- Your use case involves nuanced analysis of long documents, contracts, or research papers.
- You need careful handling of sensitive topics with conservative, safety-focused responses and refusals.
- Your use case involves complex code understanding, refactoring, or explaining nontrivial software architectures.
- You need an assistant for exploratory thinking, brainstorming, or evaluating multiple solution approaches.
Avoid if...
- You need the absolute lowest possible cost per token for massive-scale workloads.
- Your workload requires ultra-low latency responses for real-time, high-frequency interactive applications.
- You need on-device or fully self-hosted inference rather than a cloud API.
- Your workload requires multimodal capabilities beyond text if not supported in this variant.
- You need a lightweight model specialized for short, repetitive tasks with minimal reasoning.
- Your workload requires strict compatibility with another provider’s API-specific extensions or tools.
FAQ
Frequently Asked Questions
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What is Claude Opus Latest?
Claude Opus Latest is Anthropic’s flagship large language model accessed through LLM.API, designed for high-intelligence reasoning, coding, and complex enterprise workloads.
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What is Claude Opus Latest best suited for?
Claude Opus Latest excels at complex reasoning, multi-step problem solving, advanced coding assistance, long-form content generation, and nuanced analysis of technical or legal documents.
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How is Claude Opus Latest priced when used via LLM.API?
Claude Opus Latest requests are billed by LLM.API based on provider metered input and output tokens plus any LLM.API platform surcharges shown in your pricing settings.
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What context window does Claude Opus Latest support?
Claude Opus Latest supports up to a 200K token context window, allowing very long conversations, large documents, or extensive codebases in a single request.
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How fast is Claude Opus Latest in terms of latency?
Claude Opus Latest typically has higher latency than smaller Claude models, with actual response time depending on prompt size, output length, and LLM.API routing.
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Which modalities does Claude Opus Latest support through LLM.API?
Through LLM.API, Claude Opus Latest supports text input and output, and image understanding where enabled by your Anthropic-backed configuration.
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How do I call Claude Opus Latest from the LLM.API?
Specify the model name "Claude Opus Latest" in your LLM.API request, include your LLM.API key, and send standard chat or completion-style payloads.
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How does Claude Opus Latest compare to smaller Claude models?
Claude Opus Latest generally offers stronger reasoning, coding, and comprehension capabilities than cheaper Claude models, at higher cost and typically slightly higher latency.
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Are there any important limitations of Claude Opus Latest I should know?
Claude Opus Latest can still hallucinate, reflect training-data biases, mishandle very domain-specific edge cases, and should not be used without human review for critical decisions.
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Can I fine-tune Claude Opus Latest through LLM.API?
Direct fine-tuning of Claude Opus Latest is not generally available through LLM.API; instead, you should use prompting, system messages, and retrieval-augmented patterns.
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