Claude 4.6 Opus

Approved Data Classifications

This model is only approved for Open Data. When leveraging NaviGator AI’s suite offerings, please ensure you are selecting models that meet the compliance requirements in accordance with UF’s Data Classifications, grant awards or project specifications, and state and federal law.

Description

Claude Opus 4.6 is Anthropic’s most capable hybrid-reasoning and agentic model, designed for complex coding, long-running AI agents, and enterprise workflows, with a 1 million-token context window, up to 128,000 output tokens, adaptive reasoning, and pricing of $5 per million input tokens and $25 per million output tokens.

Capabilities

Model	Knowledge Cutoff	Input	Output	Context Length	Cost (per 1 million tokens)
claude-4.6-opus	May 31 2025	Text, Image, Pdf	Text	1,000,000	$5.50/1M input $27.50/1M output

info

• 1M represents 1 Million Tokens
• All prices listed are based on 1 Million Tokens

Availability

Cloud Provider

Usage

curl

curl -X POST https://api.ai.it.ufl.edu/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <API_TOKEN>" \
-d '{
    "model": "claude-4.6-opus",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Write a haiku about an Alligator."
        }
    ]
}'

python

from openai import OpenAI
client = OpenAI(
    api_key="your_api_key",
    base_url="https://api.ai.it.ufl.edu/v1"
)

response = client.chat.completions.create(
    model="claude-4.6-opus", # model to send to the proxy
    messages = [
        { "role": "system", "content": "You are a helpful assistant." },
        {
            "role": "user",
            "content": "Write a haiku about an Alligator."
        }
    ]
)

print(response.choices[0].message)

javascript

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'your_api_key',
  baseURL: 'https://api.ai.it.ufl.edu/v1'
});

const completion = await openai.chat.completions.create({
    model: "claude-4.6-opus",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        {
            role: "user",
            content: "Write a haiku about an Alligator.",
        },
    ],
});

console.log(completion.choices[0].message)

When to use

• Frontier-intelligence tasks where accuracy matters most
• Professional software engineering and architecture
• Complex, multi-step agentic workflows
• High-stakes enterprise decision support
• Advanced computer-use and tool orchestration
• Large-context tasks needing a 200K window

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References

1. Anthropic
   https://www.anthropic.com
2. LLM Stats
   https://llm-stats.com
3. Artificial Analysis
   https://artificialanalysis.ai