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Powered by OpenAI's GPT-4-o-mini . Outperforms GPT-3.5 in quantitative questions, creative writing, and other challenging tasks. Comparable to GPT-4o on text in English and code, but less powerful on text in non-English languages.

Powered by X.AI's Grok-Beta.

This OpenAI model is a faster, cheaper version of o1 that is particularly good at coding, math, and science applications when broad general knowledge is not required. Supports 128k tokens of context.

An early preview of OpenAI's latest o1 model which is designed to spend more time thinking before it responds. It can reason through complex tasks and solve harder problems than previous models in science, coding, and math. Supports 128k tokens of context.

Powered by OpenAI's GPT-4 Turbo with Vision. Outperforms GPT-3.5 in quantitative questions, creative writing, and other challenging tasks. Comparable to GPT-4o on text in English and code, but less powerful on text in non-English languages.

OpenAI's most powerful model. Stronger than GPT-3.5 in quantitative questions (math and physics), creative writing, and many other challenging tasks. Powered by GPT-4o.

Anthropic's Claude 3.5 Sonnet New excels in complex tasks like autonomous coding and visual processing. In addition, it shines when working with long documents, ensuring accuracy in tasks like retrieval-augmented generation (RAG), search and retrieval, and comparing multiple lengthy documents.

Anthropic's Claude 3 Haiku outperforms models in its intelligence category on performance, speed and cost without the need for specialized fine-tuning.

Anthropic’s most intelligent model, which can handle complex analysis, longer tasks with multiple steps, and higher-order math and coding tasks. Supports 200k tokens of context.

Powered by gemini-2.0-flash-exp. This smaller Gemini model is optimized for narrower or high-frequency tasks where the speed of the model’s response time matters the most. The model accepts text, image, and video input from the entire conversation and provides text output, with a restriction of one video per message. The compute points value is subject to change.

Powered by ggemini-2.0-flash-thinking-exp-1219. This smaller Gemini model is optimized for narrower or high-frequency tasks where the speed of the model’s response time matters the most. The model accepts text, image, and video input from the entire conversation and provides text output, with a restriction of one video per message. The compute points value is subject to change.

Powered by gemini-1.5-flash-002. This smaller Gemini model is optimized for narrower or high-frequency tasks where the speed of the model’s response time matters the most. The model accepts text, image, and video input from the entire conversation and provides text output, with a restriction of one video per message. The compute points value is subject to change.

Powered by gemini-1.5-pro-002. The multi-modal model from Google's Gemini family that balances model performance and speed. The model accepts text, image, and video input from the entire conversation and provides text output, with a restriction of one video per message. The compute points value is subject to change.

DeepSeek Chat is a large language model with 100 billion parameters, trained on 2 trillion tokens. It is a general-purpose model that can be used for a wide range of tasks, including but not limited to text generation, question answering, and code generation.

DeepSeek Reasoner is a large language model with 100 billion parameters, trained on 2 trillion tokens. It is a general-purpose model that can be used for a wide range of tasks, including but not limited to text generation, question answering, and code generation.

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Anthropic's Claude 3.7 Sonnet New excels in complex tasks like autonomous coding and visual processing. In addition, it shines when working with long documents, ensuring accuracy in tasks like retrieval-augmented generation (RAG), search and retrieval, and comparing multiple lengthy documents.

Anthropic's Claude 3.7 Sonnet Thinking excels in complex tasks like autonomous coding and visual processing. In addition, it shines when working with long documents, ensuring accuracy in tasks like retrieval-augmented generation (RAG), search and retrieval, and comparing multiple lengthy documents.