Claude 3.7 Sonnet is Anthropic's most advanced AI model yet and the first hybrid reasoning system on the market. It offers both standard and extended thinking modes, with the latter providing transparent, step-by-step reasoning. The model demonstrates significant improvements in coding and front-end web development, achieving state-of-the-art results on SWE-bench Verified and TAU-bench. Available via Claude.ai, the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI, it sets a new standard for intelligent AI-powered problem-solving.
Web Site AI Model Web Page | |
Provider The entity that provides this model. | |
Chat Input a message to start chatting | - |
Release Date When the model was first released. | 1 year ago Feb 24, 2025 |
Modalities Types of data this model can process | text images |
API Providers The providers that offer this model. (This is not an exhaustive list.) | Claude.ai, Anthropic API, Amazon Bedrock, Google Cloud Vertex AI |
Knowledge Cut-off Date When the model's knowledge was last updated. | - |
Open Source Whether the model's code is available for public use. | No |
Pricing Input Cost for processing tokens in your prompts | $3.00 per million tokens |
Pricing Output Cost for tokens generated by the model | $15.00 per million tokens |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | Not available |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | Not available |
MMMU Massive Multitask Multimodal Understanding - Tests understanding across text, images, audio, and video | 75% Source |
HellaSwag A challenging sentence completion benchmark | Not available |
HumanEval Evaluates code generation and problem-solving capabilities | Not available |
MATH Tests mathematical problem-solving abilities across various difficulty levels | 96.2% Source |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 84.8% Diamond Source |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | 93.2% Source |
SimpleQA Assessing the accuracy of simple questions | - |
AIME 2024 | - |
AIME 2025 | - |
Aider Polyglot Multilingual programming benchmark. | - |
LiveCodeBench v5 Benchmark for real-time programming | - |
Global MMLU (Lite) A simplified version of the benchmark for assessing the universality of models at the global level. | - |
MathVista Evaluates the mathematical reasoning abilities of AI models within visual contexts | - |
Mobile Application |
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