What is Claude Sonnet 5 API?
Claude Sonnet 5 is Anthropic's high-performance language model for coding, agentic workflows, and complex reasoning. It delivers performance approaching Claude Opus quality at Sonnet pricing — with a 1M token context window, adaptive thinking, and high-resolution vision built in.
The model is available via AI/ML API using the model ID anthropic/claude-sonnet-5 and ships under Anthropic's standard commercial terms.
[Model Specifications embed]
[Performance Benchmarks embed]
API Pricing
Where to Use Claude Sonnet 5
Coding and code review
Claude Sonnet 5 is optimized for writing, debugging, and reviewing code across all major languages. The 1M context window means entire codebases can be loaded for refactoring or cross-file analysis in a single prompt.
Agentic and multi-step workflows
Strong tool-calling and reliable instruction following make Claude Sonnet 5 a natural fit for agent loops that require planning, executing tools, evaluating results, and iterating over many steps.
Long-document analysis
A 1M token context window lets you pass full contracts, research papers, technical specs, and annual reports in one prompt — no chunking required.
Vision and multimodal tasks
High-resolution vision enables accurate processing of diagrams, screenshots, charts, and images — suitable for document intelligence, UI analysis, and visual QA workflows.
Complex reasoning tasks
Adaptive thinking routes harder problems through an extended reasoning process, producing more reliable answers for multi-step math, logic, and technical analysis.
Claude Sonnet 5 vs. the Alternatives
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Claude Sonnet 5 is documented as suited for coding workflows, including initial planning, implementation, debugging, maintenance, and large-scale refactors across complex multi-file codebases.
It is also suited for autonomous AI workflows and production-grade agents, with strong instruction following, tool selection, error correction, and sustained multi-step task handling for customer-facing agents and internal automation.
Another documented use case is browser-based computer use, where it can handle tasks such as competitive analysis, procurement workflows, and customer onboarding.
It is further suited to professional work involving finance, research, content, and business operations, including analyzing financial data, synthesizing information from multiple sources, and generating or editing documents and spreadsheets.
import requests
r = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={"Authorization": "Bearer " + AIMLAPI_KEY},
json={
"model": "anthropic/claude-sonnet-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",
"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","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
The full 1M context window is included at standard pricing. Free credits on signup.
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| GPQA Diamond | 96.2% | Google-proof graduate science questions (hardest subset) | Source | July 8, 2026 |
| OSWorld | 81.2% | Computer-use across real desktop applications | Source | July 8, 2026 |
| SWE-bench Verified | 85.2% | Resolving verified real GitHub issues | Source | July 8, 2026 |
| Intelligence | 38.4 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Coding | 71.5 | Composite score across standardised coding evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
Claude Sonnet 5 is documented as suited for coding workflows, including initial planning, implementation, debugging, maintenance, and large-scale refactors across complex multi-file codebases.
It is also suited for autonomous AI workflows and production-grade agents, with strong instruction following, tool selection, error correction, and sustained multi-step task handling for customer-facing agents and internal automation.
Another documented use case is browser-based computer use, where it can handle tasks such as competitive analysis, procurement workflows, and customer onboarding.
It is further suited to professional work involving finance, research, content, and business operations, including analyzing financial data, synthesizing information from multiple sources, and generating or editing documents and spreadsheets.
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Claude Sonnet 5 This page | Balanced coding + agents | |||
| Chat + assistants | ||||
| Chat + assistants | ||||
| Reasoning + agents | ||||
| Reasoning + agents with long context |
GPT-Image-1.5 is 4x Faster than the previous models – DALL-E 3 and GPT-Image-1. Detailed high resolution images now take near 30 seconds to be generated.
TTS-1-HD produces higher-fidelity audio with more natural prosody and tone, while TTS-1 is optimized for speed and lower cost.
GPT-Image-1.5 was released on December 16, 2025. OpenAI made the model available globally to all ChatGPT users and through the API on this date, marking a significant upgrade to their image generation capabilities.
It returns audio in mp3, opus, aac, flac, wav, pcm formats.
1. Register on our platform
2. Verify your account
3. Create an API key
4. Copy the code from this page
5. Paste it in your project
