claude-3-sonnet-20240229

Name: claude-3-sonnet-20240229
Brand: Anthropic

AnthropicChatVision

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Enterprise conversational model for long-document comprehension and chart analysis

claude-3-sonnet-20240229 is the date-fixed Sonnet version of the Anthropic Claude 3 family, designed for enterprise tasks that require both comprehension capabilities and response efficiency. It can generate answers by combining text, photos, and charts, making it suitable for knowledge Q&A, information extraction, code drafting, and brand content writing. It is positioned as a balanced assistant, rather than the fastest Haiku in the same family or the Opus focused on highly complex tasks.

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AnthropicModel brand

ConversationModel type

Visual understandingTask capability

STANDARD APIs · QUICK SETUP

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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 host: api.acedata.cloud
model: claude-3-sonnet-20240229

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OpenAI Python SDK

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ACEDATACLOUD_API_KEY"],
    base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
    model="claude-3-sonnet-20240229",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.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.

Specifications and interface features

Clarify capacity, input/output, and invocation methods before selecting a model.

Version positioning: Claude 3 Sonnet, date-fixed ID: claude-3-sonnet-20240229
Native context: 200K tokens, official published specification
Input method: Combined text and image input
Vision tasks: Understanding photos, charts, and technical diagrams, as well as image text parsing
Output method: Text responses, which can be organized as structured text such as JSON according to prompts
API access: Chat Completions or Messages API
Conversation and delivery: Standard text or streamed text; message history is organized by the application according to the selected protocol

200K is the native context specification at the launch of Claude 3 Sonnet. Image content, streamed responses, and message history are handled according to the formats of Chat Completions or Messages, respectively.

Core Capabilities

Learn what claude-3-sonnet-20240229 can bring to your work.

Turn lengthy materials into usable answers

Sonnet is suited to reading, summarizing, and answering questions about longer business materials. Enter product manuals, policy documents, or retrieval excerpts together with questions, and ask it to extract key points by topic, compare terms, and organize conclusions. Clearly defining the scope of materials and answer format helps turn open-ended reading into verifiable deliverables.

Understand charts and text together

It processes not only text, but can also answer questions by combining photos, charts, and technical diagrams. For report screenshots, you can ask it to extract visible fields, explain trends, and distinguish observations from inferences; for flowcharts, it can organize node relationships. Deliverables are primarily text analysis, not image generation or image editing.

Organize content according to business rules

Claude 3 has improved its ability to follow multi-step instructions and brand tone, allowing Sonnet to perform classification, summarization, and content rewriting accordingly. After providing field definitions, tone requirements, and examples, you can have it output JSON text or fixed sections. Structured expression makes integration with business applications easier, but formats and field contents should still be validated.

Use Cases

Start with specific tasks to find where the model can be effective.

Product knowledge and pre-sales Q&A

Enter product specifications, applicable conditions, and user requirements, and have Sonnet organize candidate solutions, explain differences, and list information that still needs confirmation. Deliverables can be customer response drafts or product comparison tables. Relevant materials need to be provided with each request for knowledge Q&A; the model itself does not automatically have access to real-time enterprise inventory and product data.

Report screenshots and material organization

Submit chart screenshots together with extraction requirements, and ask for a list of metrics, trend explanations, and anomalies. For pages with substantial text, first extract visible content, then categorize it by business fields. This is suitable for assisting with presentation materials or quality inspection records; key figures should be verified item by item against clear original images.

Code drafts and content standardization

Provide requirement descriptions, existing code, or brand writing guidelines, and have it generate function drafts, explain implementation approaches, or rewrite scattered information into copy with a consistent style. Deliverables are code and text recommendations; code must be tested in the actual environment, and an implementation provided by the model does not mean it has already been run or deployed.

How to choose this model

Choose based on task complexity, input materials, and expected results.

How to choose within the Claude 3 family

When a task involves lengthy materials, image and text understanding, and multiple output requirements, while also requiring responsive performance, Sonnet is a balanced choice. For simple classification, short Q&A, and low-latency interactions, consider Claude 3 Haiku; for more complex and open-ended analysis, consider Claude 3 Opus. The differences among the three lie in their capability positioning, so task suitability should not be judged by name alone.

Trade-offs between fixed versions and invocation methods

When existing prompts and acceptance examples were built around this version, you can continue using the explicit date ID for regression testing; it is not Claude 3.5 or a later Sonnet version. Before changing versions, compare accuracy and format stability using the same examples.

Start with a specific task

Based on the characteristics of claude-3-sonnet-20240229, first validate a small task whose results can be checked.

Summarization and Q&A for enterprise knowledge materials

You can ask directly: Based on the provided product specifications, answer the procurement questions, listing the conditions met, limitations, and information requiring confirmation separately. Cite the material location for each conclusion.

Prepare inputs that support decisions

Suitable as a balanced baseline for existing applications; respond based on actual materials, and do not treat outdated knowledge as current product facts.

Then integrate it into your workflow

Use the full model ID claude-3-sonnet-20240229, first confirm the public request format and available parameters on the API page, then connect your application. Retain result parsing, exception handling, and supporting evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.

Usage boundaries

Before formal use, understand output quality and capability scope.

A 200K context does not mean it can generate answers of equal length, nor does it guarantee that every detail in lengthy materials is extracted accurately. When processing multiple documents, define the question clearly, retain section identifiers, and require answers to reference the relevant materials, rather than putting all unrelated content into a single request.
Visual understanding depends on image clarity and visible information. Dense small text, blurry coordinates, or cropped legends can affect reading; chart trend descriptions also do not equal precise data reconstruction. Financial figures, technical dimensions, and key fields should be verified against the original materials.
This is a fixed version of Claude 3 Sonnet, and tasks should not be designed around the deep reasoning or agent capabilities of later Sonnet versions. The basic workflow is image and text input with text output; internet access, file handling, and external execution should be configured separately as application capabilities and cannot be completed by default through a normal Q&A request.

Frequently Asked Questions

Answers to common questions when using claude-3-sonnet-20240229.

Is it the same version as Claude 3.5 Sonnet?

No. claude-3-sonnet-20240229 corresponds to the date-fixed Sonnet version of the Claude 3 family; it does not mean it automatically updates to Claude 3.5 or later versions. When existing applications switch models, you should retest prompts, image understanding results, and output formats rather than just replacing the name.

Does a 200K context mean 200K output?

No. The context window describes the model's capacity to process conversations and materials, not the length of a single response. You also need to reserve space for the response; for long reports, it is better to generate an outline first and then expand it by section, rather than treating the entire context size as an output target.

How do I submit an image for analysis?

Combine a text question and a clear image in the text-and-image content blocks supported by the selected interface, and specify the area of interest and expected output. Chat Completions uses text and image_url, while Messages uses its native image content blocks; do not use PDF or video URLs as image_url.

Can it output JSON for programs to read?

You can request JSON text through prompting, and Claude 3 has improved in this kind of structured expression. It is recommended to provide field names, types, and rules for missing values, and to parse and validate on the application side; the ability to generate JSON does not mean it will meet strict Schema constraints every time.

Do I need to resend all history for each follow-up question?

Include user and assistant messages relevant to the current task in messages for Messages or Chat Completions, with the specific format handled according to the selected public interface. Keep the latest code, interim conclusions, and important constraints; when necessary, summarize longer history again to avoid relying on outdated information.

Use it in your favorite tools

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Input

$0.60per 1M tokens

Official ~~$3.00~~

Output

$3.00per 1M tokens

Official ~~$15.00~~

−80%vs official

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claude-3-sonnet-20240229

Enterprise conversational model for long-document comprehension and chart analysis

Keep your SDK. Connect in minutes.

Specifications and interface features

Core Capabilities