import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "anthropic/claude-sonnet-5.5", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "anthropic/claude-sonnet-5.5", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"anthropic/claude-sonnet-5.5","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 56 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 28, 2026 |
| Terminal-Bench | 70.6% (v4.0) | Autonomous shell/terminal task completion | Source | September 28, 2026 |
| OSWorld | 80.1% (v2.1, partial) | Computer-use across real desktop applications | Source | September 28, 2026 |
| Humanity's Last Exam | 64.5% (with tools) | Expert-level questions across many domains | Source | September 28, 2026 |
Terminal-Bench, OSWorld and Humanity's Last Exam as published by Anthropic at release. Intelligence Index measured independently by Artificial Analysis.
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Claude Sonnet 5.5 This page | Multimodal reasoning | |||
| Complex agentic coding and enterprise workflows | ||||
| Long-horizon agentic coding and research | ||||
| Agentic workflows and structured output |
It is built for coding, agents, and professional work at scale. It is also described as suitable for reasoning, streaming, tools, and vision.
Anthropic positions it for coding, multi-step workflows and autonomous agents, and reports its largest gains over Claude Sonnet 5 on agentic evaluations such as Terminal-Bench 4.0 and CursorBench 4.0. The 1M-token context window also suits long documents and whole-repository work.
Yes. Anthropic says it is designed to navigate real codebases, land multi-file changes, and carry longer debugging and refactoring tasks through to completion.
Confirm that your workflow needs coding, agentic automation, tool use, or vision. The model is positioned for professional tasks at scale, so it is a strong fit when those capabilities matter most.
