About Claude Models Overview

export const ModelId = ({children, style = {}}) => { const copiedNotice = 'Copied!'; const handleClick = e => { const element = e.currentTarget; const textSpan = element.querySelector('.model-id-text'); const copiedSpan = element.querySelector('.model-id-copied'); navigator.clipboard.writeText(children).then(() => { textSpan.style.opacity = '0'; copiedSpan.style.opacity = '1'; element.style.backgroundColor = '#d4edda'; element.style.borderColor = '#c3e6cb'; setTimeout(() => { textSpan.style.opacity = '1'; copiedSpan.style.opacity = '0'; element.style.backgroundColor = '#f5f5f5'; element.style.borderColor = 'transparent'; }, 2000); }).catch(error => { console.error('Failed to copy:', error); }); }; const handleMouseEnter = e => { const element = e.currentTarget; const copiedSpan = element.querySelector('.model-id-copied'); const tooltip = element.querySelector('.copy-tooltip'); if (tooltip && copiedSpan.style.opacity !== '1') { tooltip.style.opacity = '1'; } element.style.backgroundColor = '#e8e8e8'; element.style.borderColor = '#d0d0d0'; }; const handleMouseLeave = e => { const element = e.currentTarget; const copiedSpan = element.querySelector('.model-id-copied'); const tooltip = element.querySelector('.copy-tooltip'); if (tooltip) { tooltip.style.opacity = '0'; } if (copiedSpan.style.opacity !== '1') { element.style.backgroundColor = '#f5f5f5'; element.style.borderColor = 'transparent'; } }; const defaultStyle = { cursor: 'pointer', position: 'relative', transition: 'all 0.2s ease', display: 'inline-block', userSelect: 'none', backgroundColor: '#f5f5f5', padding: '2px 4px', borderRadius: '4px', fontFamily: 'Monaco, Consolas, "Courier New", monospace', fontSize: '0.75em', border: '1px solid transparent', ...style }; return <span onClick={handleClick} onMouseEnter={handleMouseEnter} onMouseLeave={handleMouseLeave} style={defaultStyle}> <span className="model-id-text" style={{ transition: 'opacity 0.1s ease' }}> {children} </span> <span className="model-id-copied" style={{ position: 'absolute', top: '2px', left: '4px', right: '4px', opacity: '0', transition: 'opacity 0.1s ease', color: '#155724' }}> {copiedNotice} </span> </span>; };

Choosing a model

If you're unsure which model to use, we recommend starting with Claude Sonnet 4.5. It offers the best balance of intelligence, speed, and cost for most use cases, with exceptional performance in coding and agentic tasks.

All current Claude models support text and image input, text output, multilingual capabilities, and vision. Models are available via the Anthropic API, AWS Bedrock, and Google Vertex AI.

Once you've picked a model, learn how to make your first API call.

Latest models comparison

^{1 - Aliases automatically point to the most recent model snapshot. When we release new model snapshots, we migrate aliases to point to the newest version of a model, typically within a week of the new release. While aliases are useful for experimentation, we recommend using specific model versions (e.g., claude-sonnet-4-5-20250929) in production applications to ensure consistent behavior.}

^{2 - See our pricing page for complete pricing information including batch API discounts, prompt caching rates, extended thinking costs, and vision processing fees.}

^{3 - Claude Sonnet 4.5 supports a 1M token context window when using the context-1m-2025-08-07 beta header. Long context pricing applies to requests exceeding 200K tokens.}

^{4 - Reliable knowledge cutoff indicates the date through which a model's knowledge is most extensive and reliable. Training data cutoff is the broader date range of training data used. For example, Claude Sonnet 4.5 was trained on publicly available information through July 2025, but its knowledge is most extensive and reliable through January 2025. For more information, see Anthropic's Transparency Hub.}

Note: Models with the same snapshot date (e.g., 20240620) are identical across all platforms and do not change. The snapshot date in the model name ensures consistency and allows developers to rely on stable performance across different environments.

Note: Starting with Claude Sonnet 4.5 and all future models, AWS Bedrock and Google Vertex AI offer two endpoint types: global endpoints (dynamic routing for maximum availability) and regional endpoints (guaranteed data routing through specific geographic regions). For more information, see the third-party platform pricing section.

Legacy models

The following models are still available but we recommend migrating to current models for improved performance:

Feature	Claude Sonnet 4	Claude Sonnet 3.7	Claude Opus 4	Claude Haiku 3.5	Claude Haiku 3
Claude API ID	claude-sonnet-4-20250514	claude-3-7-sonnet-20250219	claude-opus-4-20250514	claude-3-5-haiku-20241022	claude-3-haiku-20240307
Claude API alias	claude-sonnet-4-0	claude-3-7-sonnet-latest	claude-opus-4-0	claude-3-5-haiku-latest	—
AWS Bedrock ID	anthropic.claude-sonnet-4-20250514-v1:0	anthropic.claude-3-7-sonnet-20250219-v1:0	anthropic.claude-opus-4-20250514-v1:0	anthropic.claude-3-5-haiku-20241022-v1:0	anthropic.claude-3-haiku-20240307-v1:0
GCP Vertex AI ID	claude-sonnet-4@20250514	claude-3-7-sonnet@20250219	claude-opus-4@20250514	claude-3-5-haiku@20241022	claude-3-haiku@20240307
Pricing	\3 / input MTok<br />\15 / output MTok	\3 / input MTok<br />\15 / output MTok	\15 / input MTok<br />\75 / output MTok	\0.80 / input MTok<br />\4 / output MTok	\0.25 / input MTok<br />\1.25 / output MTok
Extended thinking	Yes	Yes	Yes	No	No
Priority Tier	Yes	Yes	Yes	Yes	No
Comparative latency	Fast	Fast	Moderate	Fastest	Fast
Context window	200K tokens / <br /> 1M tokens (beta)<sup>1</sup>	200K tokens	200K tokens	200K tokens	200K tokens
Max output	64K tokens	64K tokens / 128K tokens (beta)<sup>4</sup>	32K tokens	8K tokens	4K tokens
Reliable knowledge cutoff	Jan 2025<sup>2</sup>	Oct 2024<sup>2</sup>	Jan 2025<sup>2</sup>	<sup>3</sup>	<sup>3</sup>
Training data cutoff	Mar 2025	Nov 2024	Mar 2025	Jul 2024	Aug 2023

^{1 - Claude Sonnet 4 supports a 1M token context window when using the context-1m-2025-08-07 beta header. Long context pricing applies to requests exceeding 200K tokens.}

^{2 - Reliable knowledge cutoff indicates the date through which a model's knowledge is most extensive and reliable. Training data cutoff is the broader date range of training data used.}

^{3 - Some Haiku models have a single training data cutoff date.}

^{4 - Include the beta header output-128k-2025-02-19 in your API request to increase the maximum output token length to 128K tokens for Claude Sonnet 3.7. We strongly suggest using our streaming Messages API to avoid timeouts when generating longer outputs. See our guidance on long requests for more details.}

Prompt and output performance

Claude 4 models excel in:

• Performance: Top-tier results in reasoning, coding, multilingual tasks, long-context handling, honesty, and image processing. See the Claude 4 blog post for more information.

• Engaging responses: Claude models are ideal for applications that require rich, human-like interactions.

  • If you prefer more concise responses, you can adjust your prompts to guide the model toward the desired output length. Refer to our prompt engineering guides for details.
  • For specific Claude 4 prompting best practices, see our Claude 4 best practices guide.

• Output quality: When migrating from previous model generations to Claude 4, you may notice larger improvements in overall performance.

Migrating to Claude 4.5

If you're currently using Claude 3 models, we recommend migrating to Claude 4.5 to take advantage of improved intelligence and enhanced capabilities. For detailed migration instructions, see Migrating to Claude 4.5.

Get started with Claude

If you're ready to start exploring what Claude can do for you, let's dive in! Whether you're a developer looking to integrate Claude into your applications or a user wanting to experience the power of AI firsthand, we've got you covered.

Note: Looking to chat with Claude? Visit claude.ai!

If you have any questions or need assistance, don't hesitate to reach out to our support team or consult the Discord community.