Anthropic's coding- and agent-optimized Sonnet model built to run autonomous, tool-using workflows for hours at a time.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Claude Sonnet 4.5 Earns Its Place: Coding and Autonomous Agents
Benchmarks
Claude Sonnet 4.5 Benchmarks: Coding Leadership, Mid-Tier Speed
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 = {
"model": "claude-sonnet-4.5",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url": { "url": "https://your-image.jpg" }
}
]
}
],
"max_tokens": 300,
"stream": False,
"tool_choice": "auto",
"logprobs": False
}
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: '{"model":"claude-sonnet-4.5","messages":[{"role":"user","content":[{"type":"text","text":"What is in this image?"},{"type":"image_url","image_url":{"url":"https://your-image.jpg"}}]}],"max_tokens":300,"stream":false,"tool_choice":"auto","logprobs":false}'
};
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 '{
"model": "claude-sonnet-4.5",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://your-image.jpg"
}
}
]
}
],
"max_tokens": 300,
"stream": false,
"tool_choice": "auto",
"logprobs": false
}'
Comparison
Claude Sonnet 4.5 vs Claude Opus 4.1: Coding Value or Maximum Capability?
At launch, the realistic decision for teams inside the Claude ecosystem was Sonnet 4.5 versus Opus 4.1, then Anthropic's most capable model. Both handle coding, reasoning, and agentic tool use with the same text/image/PDF input and text output, and both target complex, multi-step work. The practical question is value: Sonnet 4.5 actually surpassed Opus 4.1 on SWE-bench Verified (77.2% vs 74.5%) while sitting in a substantially lower cost tier, whereas Opus retained an edge on the broadest, hardest reasoning tasks.
Choose Claude Sonnet 4.5 when coding accuracy, agentic tool use, and cost efficiency drive the decision—it matches or beats Opus 4.1 on software-engineering benchmarks at a much lower price, making it the better default for high-volume coding agents and computer-use workflows. Choose Opus 4.1 when you need the maximum general reasoning ceiling on the most demanding analytical or open-ended problems and are willing to pay a premium tier for it. For most production coding pipelines, Sonnet 4.5 is the more economical choice.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous coding agents
Sonnet 4.5's 77.2% SWE-bench Verified score and reported 30+ hour sustained focus make it well-suited to agents that plan, edit, test, and iterate on real codebases. It works effectively with a simple scaffold of bash and file-editing tools. Production uses include automated issue resolution, refactoring assistants, and CI-integrated fix generation. Plan around the 200K window for very large repositories.
Computer-use and browser automation
With 61.4% on OSWorld—a large jump over prior models—Sonnet 4.5 is a strong choice for agents that operate a browser or desktop: clicking buttons, filling forms, navigating apps, and recovering from errors. This suits workflow-automation products that drive real UIs. Note computer use carries prompt-injection risk, so sandboxing and guardrails are essential in production.
Long-running tool-orchestration agents
The model is designed to coordinate tools and sub-tasks over extended horizons while tracking token usage across calls, backed by context-editing and memory tooling on the Claude API. This fits research agents, financial-analysis pipelines, and cybersecurity workflows that run many steps. The main constraint is the 200K context ceiling, which requires deliberate context management on the longest runs.
Document and PDF analysis
Because Sonnet 4.5 accepts PDF pages and images directly and returns structured text, it fits contract review, report extraction, and technical-document Q&A within the 200K window (up to 100 images/PDF pages per request for 200K-window models). For strict schema-valid extraction, pair it with tool calling and downstream validation, since native JSON-schema enforcement is unavailable.