Claude Opus 4.5

Chat Completions

claude-opus-4.5-20251101

Anthropic|Created Nov 24, 2025|200k context

Chat Completions

Claude Opus 4.5 is Anthropic’s latest reasoning model, developed for advanced software engineering, complex agent workflows, and extended computer tasks. It features strong multimodal capabilities and performs competitively on real-world coding and reasoning benchmarks, with enhanced resilience against prompt injection. Optimized for efficiency at varying effort levels, it allows developers to balance speed, depth, and token usage according to their specific needs, thanks to a new parameter for controlling token efficiency. Opus 4.5 excels in advanced tool integration, contextual management, and multi-agent coordination, making it ideal for autonomous research, debugging, complex planning, and spreadsheet or browser manipulation. Compared to previous Opus generations, it delivers significant improvements in structured reasoning, long-duration task performance, execution reliability, and alignment, all while reducing token overhead.

Overview Specifications Activity Performance Uptime Examples

Pricing-50%

Pay-as-you-go rates for this model. More details can be found here.

Input Tokens (1M)

$2.50

Cached Input Tokens (1M)

$0.25

Output Tokens (1M)

$12.50

Capabilities

Input Modalities

TextImageFile

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Frequency PenaltyLogit BiasMax Completion TokensPresence PenaltyReasoning EffortResponse FormatStopTemperatureTool ChoiceToolsTop P

Usage Analytics

Token usage of this model on our platform

Throughput

Time-To-First-Token (TTFT)

Code Example

Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.

Basic request example. Ensure API key permissions. For more details, see our documentation.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

resp = client.chat.completions.create(
    model="claude-opus-4.5-20251101",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)