Claude Sonnet 5.5 is a model in the Anthropic Claude 5.5 family that balances speed and intelligence, designed for well-scoped engineering tasks, bug fixes, and work involving documents, slides, and spreadsheets. Compared with Sonnet 5, official reports indicate improvements in coding, image understanding, and knowledge work, while emphasizing clearer collaboration communication. This platform provides access through the native Messages interface, with support for image input and adaptive thinking.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
api.acedata.cloudclaude-sonnet-5-5import os
from anthropic import Anthropic
client = Anthropic(
auth_token=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud",
)
response = client.messages.create(
model="claude-sonnet-5-5",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.content)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity and the native Messages request format.
Capacity is based on the official model overview; actual parameters, account permissions, and fees are subject to the corresponding API and pricing sections on this platform. Maximum output and input must be planned together within the context budget.
Map model strengths to specific work.
Official release materials highlight Sonnet 5.5's progress on real engineering tasks, with Terminal-Bench 4.0, FrontierCode, and CursorBench all providing comparative evidence. It is suitable to provide reproduction information, relevant modules, and completion criteria, allowing the model to organize patches, check dependencies, and advance multi-file changes; benchmark scores do not directly guarantee correctness in a project.
Official materials report improvements in knowledge work and chart recognition, and the model also supports image input. It can combine report text, screenshots, and tabular materials to complete extraction, comparison, and draft writing. Requiring results to list sources and missing information helps turn analysis into business deliverables that are easy to review.
Release materials emphasize clearer expression and faster iteration on everyday tasks. You can first provide goals, examples, and constraints, then revise step by step based on review feedback; when using tools provided by an application to perform queries or changes, tool results must be used to confirm execution status.
Choose based on task scope and result format.
Provide the code version, error behavior, and relevant files so the model can identify the issue and develop a modification plan. Determine completion through actual project execution and review; this is especially suitable for development tasks with clear boundaries and a need for rapid feedback.
Give the model research materials, operational data, and template requirements together, and require a complete structure and supporting basis in the output. The creation capabilities described on the page require support from an editing or file-tool environment; ordinary Messages responses do not themselves automatically generate and save Office files.
Use it to interpret interfaces, charts, or document images and organize results according to business fields. If a task requires operating software or continuously reading the screen, the application must provide the corresponding tools, permissions, and execution environment; image understanding cannot replace actual operation.
Consider both task difficulty and the cost of the overall work.
Official reports state that Sonnet 5.5 produces output faster and uses less for similar tasks, while official per-Token pricing is the same for both generations. This does not treat changes in speed and task cost in official tests as platform commitments; use your own samples to compare output usage, retries, and manual revision costs.
For highly open-ended tasks requiring long-term judgment and sustained exploration, the official view remains that Opus 5.5 is stronger. Sonnet 5.5 is better suited to clearly scoped daily work; higher reasoning settings also increase waiting time and usage, so evaluate based on actual completion quality.
Based on the characteristics of claude-sonnet-5-5, first validate small tasks with checkable results.
You can ask directly: Based on the reproduction steps and relevant files, locate the defect, propose a small-scope change, and then provide a brief explanation for colleagues. List the necessary tests and actual run results separately.
Use native Messages requests; evaluate iteration quality and total usage around everyday engineering and documentation tasks.
Use the full model ID claude-sonnet-5-5, first confirm the public request format and available parameters on the API page, then connect your application. Retain result parsing, exception handling, and relevant evidence, and use the same set of real samples to assess whether it is suitable for continued use.
Understand the relationship between model capabilities and the application execution environment.
Answers to questions about selecting and integrating claude-sonnet-5-5.
Officially positioned as a model balancing speed and intelligence, it emphasizes everyday engineering tasks, bug fixes, and professional documentation work. For highly open-ended tasks that require ongoing judgment, you can continue comparing Opus 5.5.
The official model overview lists a 1M-token context and a 128K-token maximum output. Input and reserved output should be planned together; actual requests must still meet this platform's API limits.
Use POST /v1/messages, and specify claude-sonnet-5-5 in the model field. Native requests include messages and max_tokens; you can estimate input tokens through /v1/messages/count_tokens.
The official conclusions about speed and task cost come from its testing conditions. Actual wait time is also affected by input, output length, thinking settings, tool steps, and application network conditions, and should be measured in your own scenario.
The model can organize content, understand code, and propose tool actions. Completing file generation or repository writes requires the application to connect the corresponding tools and verify saving and execution results; a normal text response cannot be directly treated as completed file delivery.
Choose a development approach or application suited to this model and follow its setup guide.
Claude Code CLI Setup Guide
Coding tool setup · requires a Coding Plan application key