Anthropic's precision coding and agentic model tuned for multi-file refactoring, debugging, and long-horizon tool use.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Claude Opus 4.1 Earned Its Place: Multi-File Code Work
Benchmarks
Claude Opus 4.1 Benchmarks: Coding-Led, Incremental Over Opus 4
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": "claude-opus-4.1",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_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 AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"claude-opus-4.1","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
};
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 AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "claude-opus-4.1",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}'
Comparison
Claude Opus 4.1 vs Claude Opus 4: What Actually Changed?
Claude Opus 4.1 was a direct, drop-in upgrade to Claude Opus 4, sharing the same 200K context window, text-and-image input, extended-thinking architecture, and pricing tier. The practical decision was never about capability breadth—it was about whether the incremental quality gains justified moving. Opus 4.1 lifted SWE-bench Verified from 72.5% to 74.5% and delivered its most noticeable improvements in multi-file refactoring, debugging precision, and detail tracking across longer agentic runs. Anthropic explicitly recommended upgrading from Opus 4 to Opus 4.1 for all uses, since behavior and cost were otherwise aligned.
Choose Claude Opus 4.1 over Opus 4 whenever both were available: it offered measurably better coding and agentic precision at the same tier with no meaningful downside. There is essentially no scenario where Opus 4 was preferable once 4.1 shipped. In practice, however, both models are now retired, so the real modern decision is migrating either legacy integration to a current Opus-tier model. Use this comparison to understand behavioral expectations before porting prompts and agent scaffolds forward.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Multi-file code refactoring
Opus 4.1's most characteristic strength was surgical edits across interrelated files. Anthropic and partners including GitHub and Rakuten reported gains specifically in multi-file refactoring and pinpointing exact corrections without introducing unnecessary changes. For coding agents that must modify several files coherently, this precision reduced review burden and regressions. The 32K output cap meant very large diffs still needed to be staged across turns.
Autonomous coding agents
The model was built for long-horizon agentic execution—planning, calling tools, and verifying across many steps. Tool calling plus extended thinking supported multi-turn loops where the model tracked state over extended interactions. Its 74.5% SWE-bench Verified score and improvements on TAU-bench and Terminal-Bench underpinned this use. Higher latency and cost made it best reserved for the hardest agent tasks rather than every step.
Debugging in large codebases
Opus 4.1 was praised for identifying precise fixes within large repositories without collateral edits, a behavior developers value during everyday debugging. Supplying failing tests, relevant files, and constraints let the model reason toward targeted corrections. The 200K context window comfortably held substantial slices of a repository, though careful context curation still improved results over dumping unrelated files.
Research and data analysis
Anthropic highlighted improvements in in-depth research and data analysis, particularly detail tracking and agentic search across sources. Extended thinking helped the model work through multi-step analytical problems before answering. This suited detail-sensitive investigative tasks where accuracy mattered more than speed, though the premium tier meant such analysis was best applied selectively rather than to every routine query.