Anthropic's flagship model for autonomous coding agents, long-horizon tool use, and multi-step engineering tasks with tunable reasoning effort.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Claude Opus 4.5 Earns Its Place: Coding and Agentic Reasoning
Benchmarks
Independent Benchmarks: Intelligence and Coding at the Frontier
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "anthropic/claude-opus-4.5",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"image_url": {
"detail": "auto",
"url": "https://your-image.jpg"
},
"type": "image_url"
}
]
}
]
}
headers = {
"Authorization": "Bearer your_api_key",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer your_api_key', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"anthropic/claude-opus-4.5","messages":[{"role":"user","content":[{"type":"text","text":"What is in this image?"},{"image_url":{"detail":"auto","url":"https://your-image.jpg"},"type":"image_url"}]}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer your_api_key' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "anthropic/claude-opus-4.5",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"image_url": {
"detail": "auto",
"url": "https://your-image.jpg"
},
"type": "image_url"
}
]
}
]
}'
Comparison
Claude Opus 4.5 vs Claude Sonnet 4.5: When Is Opus Worth It?
Opus 4.5 and Sonnet 4.5 share the same Claude 4 lineage, the same 200,000-token context window, and the same 64,000-token output ceiling. Both support extended thinking, tool calling, and structured outputs. The realistic decision is not about capabilities on paper but about reasoning depth versus cost and speed. Opus 4.5 sits at the top of the family for coding and agentic reasoning, while Sonnet 4.5 is the more economical, faster workhorse. For many teams the practical question is which turns actually require Opus-level reasoning and which can be routed to Sonnet.
Choose Claude Opus 4.5 when the workload involves complex multi-system debugging, long-horizon agent loops, or planning where a wrong step is expensive—cases where deeper reasoning reduces failed iterations. Choose Claude Sonnet 4.5 when you need lower cost per task, faster responses, and high request volume for routine generation, classification, or simpler coding. A common production pattern is routing most traffic to Sonnet 4.5 and reserving Opus 4.5 for the steps that genuinely need frontier reasoning.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous coding agents
Opus 4.5's launch-time 80.9% on SWE-bench Verified and its gains on Terminal-Bench Hard support use in coding agents that edit multiple files, run tests, and iterate toward a fix. It handles ambiguity and multi-system bugs with fewer failed attempts, making orchestration in tools like Claude Code more reliable. Reserve it for the genuinely hard steps and route routine edits to a cheaper model to control cost.
Long-horizon tool-use agents
The model is positioned for long-running, autonomous tasks and shows strong agentic-benchmark performance on tool-use evaluations such as τ²-Bench Telecom. Combined with tool calling and structured outputs, it suits agents that plan, call external systems, and synthesize results across many steps. Its slower output speed means these agents should run asynchronously rather than in tight interactive loops.
Complex reasoning and planning
With a 70 Intelligence Index score in reasoning mode at launch and a tunable effort parameter, Opus 4.5 fits planning tasks where reasoning quality directly affects downstream results—architecture decisions, refactor planning, or analyzing tradeoffs under constraints. Developers can dial effort up for hard problems and down for routine turns, keeping one model across a range of reasoning depths without switching endpoints.
Long-context analysis with RAG
The 200,000-token context window (extendable to 1M via a beta header) lets Opus 4.5 hold large codebases, document sets, and retrieved context in a single call. This suits RAG pipelines and codebase-wide analysis where reasoning over a lot of context matters. As with all long-context models, effective use depends on sending relevant content rather than maximizing volume, since mid-context recall still degrades at length.