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Qwen/Qwen2.5-14B-Instruct

Qwen2.5-14B-Instruct is a large language model by Alibaba’s Qwen team, optimized for instruction-following tasks with 14 billion parameters. It offers strong reasoning, multilingual capabilities, and efficient performance, making it suitable for chatbots, content creation, and various AI-driven applications.
Run time: 1 second
2929 runs
0

Qwen/Qwen2.5-32B-Instruct

Qwen2.5-32B-Instruct is a powerful large language model developed by Alibaba’s Qwen team, designed for instruction-following tasks with enhanced reasoning and natural language understanding capabilities. Optimized for efficiency and accuracy, it supports multi-turn conversations and complex queries, making it suitable for applications such as chatbots, content generation, and AI assistants.
Run time: 1 second
1496 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is a distilled version of the DeepSeek-R1 model with 32 billion parameters, leveraging the Qwen architecture to deliver high-quality language understanding and generation. Through knowledge distillation, it retains the strengths of larger models while offering improved efficiency and reduced computational requirements. This model is ideal for large-scale AI applications that demand robust performance with optimized resource utilization.
Run time: 1 second
1532 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

DeepSeek-R1-Distill-Qwen-14B is a distilled version of the DeepSeek-R1 model, featuring 14 billion parameters. Built on the Qwen architecture, it utilizes advanced knowledge distillation techniques to achieve a balance between high performance and computational efficiency. This model is well-suited for a wide range of natural language processing tasks, providing accurate and context-aware responses while optimizing resource consumption for deployment in production environments.
Run time: 1 second
0 runs
0

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

DeepSeek-R1-Distill-Llama-8B is a distilled version of the DeepSeek-R1 model based on the LLaMA architecture, featuring 8 billion parameters. It offers a balance between performance and efficiency by leveraging knowledge distillation techniques to reduce computational costs while maintaining high-quality language processing capabilities. This model is ideal for applications that require powerful text generation and understanding with optimized resource usage.
Run time: 1 second
2732 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

DeepSeek-R1-Distill-Qwen-7B is a distilled version of the DeepSeek-R1 model with 7 billion parameters, designed to provide high-quality language understanding while optimizing efficiency. Leveraging advanced knowledge distillation techniques, it retains the core capabilities of larger models with improved speed and lower resource consumption. This model is well-suited for tasks requiring robust natural language processing while maintaining cost-effective deployment.
Run time: 1 second
2614 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

DeepSeek-R1-Distill-Qwen-1.5B is a distilled version of the DeepSeek-R1 model, designed to offer a balance between efficiency and performance. With 1.5 billion parameters, it leverages knowledge distillation techniques to retain the capabilities of larger models while optimizing for faster inference and reduced resource consumption. Ideal for applications requiring high-quality language understanding with lower computational overhead.
Run time: 1 second
2311 runs
0

utter-project/EuroLLM-9B-Instruct

utter-project/EuroLLM-9B-Instruct is a 9 billion parameter multilingual AI model developed to understand and generate text across all European Union languages and additional relevant languages. It has been trained on a vast dataset of 4 trillion tokens and further fine-tuned on EuroBlocks to improve its performance in instruction-following and machine translation tasks.
Run time: 1 second
2159 runs
0

utter-project/EuroLLM-1.7B-Instruct

utter-project/EuroLLM-1.7B-Instruct is a 1.7 billion parameter multilingual AI model designed to understand and generate text in all European Union languages and additional relevant languages. It has been trained on 4 trillion tokens from diverse sources and further instruction-tuned on EuroBlocks to enhance its capabilities in general instruction-following and machine translation.
Run time: 1 second
1392 runs
0

m42-health/Llama3-Med42-8B

m42-health/Llama3-Med42-8B is an open-access clinical large language model fine-tuned by M42, based on LLaMA-3, with 8 billion parameters. It is designed to provide high-quality, reliable answers to medical queries, enhancing access to medical knowledge.
Run time: 1 second
1794 runs
0

meta-llama/Llama-3.2-3B-Instruct

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.
Run time: 1 second
2418 runs
0

meta-llama/CodeLlama-34b-Instruct-hf

meta-llama/CodeLlama-34B-Instruct-hf is a 34 billion parameter instruction-tuned AI model developed by Meta, designed to provide advanced code generation, completion, and understanding capabilities across various programming languages, offering high accuracy and efficiency for complex coding tasks.
Run time: 1 second
2058 runs
0

meta-llama/CodeLlama-7b-Instruct-hf

meta-llama/CodeLlama-7B-Instruct-hf is a 7 billion parameter instruction-tuned AI model developed by Meta, designed to assist with code generation, completion, and understanding across multiple programming languages with high efficiency and accuracy.
Run time: 1 second
2127 runs
0

CohereForAI/aya-expanse-8b

Aya Expanse 8B is an open-weight research release of a model with highly advanced multilingual capabilities. It focuses on pairing a highly performant pre-trained Command family of models with the result of a year’s dedicated research from Cohere For AI, including data arbitrage, multilingual preference training, safety tuning, and model merging. The result is a powerful multilingual large language model.
Run time: 1 second
1792 runs
0

mistralai/Mistral-Nemo-Instruct-2407

The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
Run time: 1 second
2473 runs
0

HuggingFaceTB/SmolLM2-1.7B-Instruct

HuggingFaceTB/SmolLM2-1.7B-Instruct is a 1.7 billion parameter instruction-tuned AI language model designed to provide efficient and accurate responses for a wide range of tasks while maintaining a lightweight and accessible architecture.
Run time: 1 second
1739 runs
0

mistralai/Mathstral-7B-v0.1

mistralai/Mathstral-7B-v0.1 is a 7 billion parameter AI model optimized for mathematical reasoning and problem-solving, designed to deliver precise and efficient solutions across a variety of mathematical domains.
Run time: 1 second
2981 runs
0

deepseek-ai/deepseek-math-7b-instruct

deepseek-ai/deepseek-math-7b-instruct is a 7 billion parameter AI model specifically designed to excel in mathematical reasoning and problem-solving tasks, providing accurate and detailed step-by-step solutions.
Run time: 1 second
2712 runs
0

microsoft/Phi-3.5-mini-instruct

microsoft/Phi-3.5-mini-instruct is a lightweight AI language model optimized for instruction-based tasks, offering efficient performance and high-quality responses with a compact architecture.
Run time: 1 second
2761 runs
0

Qwen/Qwen2.5-3B-Instruct

Qwen/Qwen2.5-3B-Instruct is a powerful 3 billion parameter AI language model designed to deliver high-quality, human-like responses across a wide range of natural language processing tasks.
Run time: 1 second
1116 runs
0

Qwen/Qwen2.5-0.5B-Instruct

Qwen/Qwen2.5-0.5B-Instruct is a compact 500 million parameter AI language model optimized for generating human-like responses and assisting with various natural language understanding tasks.
Run time: 1 second
2382 runs
0

Qwen/Qwen2.5-1.5B-Instruct

Qwen/Qwen2.5-1.5B-Instruct is a 1.5 billion parameter AI language model designed to generate human-like responses based on user inputs.
Run time: 1 second
1596 runs
0

Qwen/Qwen2-7B-Instruct

Qwen2-7B-Instruct is a versatile large language model with 7 billion parameters, optimized for instruction-following tasks. It delivers high performance in natural language understanding, content generation, and complex reasoning, making it ideal for a wide range of AI-driven applications, from chatbots to educational tools.
Run time: 1 second
2050 runs
0

Qwen/Qwen2.5-Math-7B-Instruct

Qwen2.5-Math-7B-Instruct is a large language model with 7 billion parameters, specialized in advanced mathematical problem-solving and reasoning. It is fine-tuned for instruction-based tasks and excels in fields like algebra, calculus, and quantitative analysis, making it suitable for research, education, and technical applications.
Run time: 1 second
1414 runs
0

Qwen/Qwen2.5-Math-1.5B-Instruct

Qwen2.5-Math-1.5B-Instruct is a math-specialized large language model with 1.5 billion parameters, fine-tuned for solving complex mathematical problems and handling instruction-based tasks. It excels in areas like algebra, calculus, and numerical reasoning, making it ideal for educational tools and research applications.
Run time: 1 second
2405 runs
0

Qwen/Qwen2.5-Coder-7B-Instruct

Qwen2.5-Coder-7B-Instruct is a specialized large language model with 7 billion parameters, designed for code understanding and generation. Fine-tuned for instruction-based tasks, it supports multiple programming languages, making it ideal for coding assistance, code completion, and educational applications.
Run time: 1 second
1299 runs
0

Qwen/Qwen1.5-0.5B-Chat

Qwen1.5-0.5B-Chat is a lightweight conversational AI model with 500 million parameters, designed for efficient and interactive chat-based applications. Despite its smaller size, it offers fast performance and reliable natural language understanding, making it ideal for real-time AI interactions on resource-constrained systems.
Run time: 1 second
2463 runs
0

microsoft/phi-4

PHI-4 by Microsoft is an advanced large language model designed for precise natural language understanding and generation. It offers robust performance in complex conversational AI and NLP tasks, making it well-suited for enterprise and research applications.
Run time: 1 second
1583 runs
0

Qwen/Qwen2.5-Coder-32B-Instruct

Qwen2.5-Coder-32B-Instruct is a powerful code-focused large language model with 32 billion parameters, fine-tuned for coding and instruction-based tasks. It excels in code generation, code completion, and understanding complex programming instructions across multiple languages.
Run time: 1 second
1894 runs
0

google/gemma-2-2b-it

Gemma-2-2B is a compact large language model by Google, featuring 2 billion parameters and optimized for instruction-based and multilingual tasks. It provides efficient natural language processing, making it suitable for a variety of AI-driven applications with lower computational requirements.
Run time: 1 second
2226 runs
0

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