# Multilingual Reasoning

Qwen3-0.6B is the latest 0.6B-parameter large language model in the Qwen series, supporting mode switching between reasoning and non-reasoning modes, with powerful reasoning, instruction-following, and multilingual capabilities.

Falcon-H1 is a high-performance hybrid architecture language model developed by TII, combining the strengths of Transformers and Mamba architectures, supporting English and multilingual tasks.

Qwen3 is the latest generation of large language models in the Qwen series, offering a range of dense and mixture-of-experts (MoE) models, achieving breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest generation of large language models in the Qwen series, offering a range of dense and mixture-of-experts (MoE) models. Through extensive training, Qwen3 has achieved groundbreaking progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest generation of large language models in the Qwen series, offering a range of dense and Mixture of Experts (MoE) models. Based on extensive training, Qwen3 has achieved groundbreaking progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest version in the Tongyi Qianwen series of large language models, offering a 1.7B-parameter GPTQ 8-bit quantized model that supports switching between reasoning and non-reasoning modes, enhancing inference capabilities and multilingual support.

The latest version of the Tongyi Qianwen series of large language models, supporting switching between thinking and non-thinking modes, with powerful reasoning, multilingual, and agent capabilities.

Qwen3 is the latest version of the Tongyi Qianwen series of large language models, offering a range of dense and mixture-of-experts (MoE) models. Based on large-scale training, Qwen3 has achieved breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3-235B-A22B is the latest generation large language model in the Qwen series, adopting a Mixture of Experts (MoE) architecture with 235 billion parameters and 22 billion active parameters. It excels in reasoning, instruction following, agent capabilities, and multilingual support.

Falcon-H1 is an efficient hybrid architecture language model developed by TII, combining the strengths of Transformers and Mamba architectures, supporting English and multilingual tasks.

Falcon-H1 is a causal decoder-only language model developed by TII with a hybrid Transformers+Mamba architecture, supporting English and multilingual tasks.

Falcon-H1 is a causal decoder model developed by the UAE's Technology Innovation Institute, featuring a hybrid Transformer and Mamba architecture, supporting English and multilingual tasks.

Falcon-H1 is an efficient hybrid architecture language model developed by TII, combining the strengths of Transformers and Mamba architectures, supporting English and multilingual tasks.

Falcon-H1 is a causal decoder-only language model with a hybrid Transformers + Mamba architecture developed by TII, supporting multilingual processing with excellent performance.

Falcon-H1 is a decoder-only causal model with a hybrid Transformers + Mamba architecture developed by TII, supporting English and multilingual tasks.

Qwen3 is the latest generation of large language models in the Qwen series, offering a range of dense and mixture-of-experts (MoE) models. Based on extensive training, Qwen3 has achieved breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete system of dense and mixture-of-experts (MoE) models. Based on extensive training, Qwen3 achieves breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest 8B-parameter version in the Tongyi Qianwen series of large language models, supporting switching between thinking and non-thinking modes, featuring 128K context length and exceptional multilingual capabilities.

Qwen3 is the latest generation large language model in the Tongyi Qianwen series, offering a complete suite of dense and Mixture of Experts (MoE) models. Based on large-scale training, Qwen3 has achieved breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest generation of large language models in the Qwen series, offering a range of dense and mixture of experts (MoE) models. Based on extensive training, Qwen3 has achieved breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete suite of dense and mixture-of-experts (MoE) models. Through large-scale training, Qwen3 has achieved breakthroughs in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3-4B is the latest generation large language model in the Qwen series with 4B parameters, supporting over 100 languages, excelling in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3-4B-FP8 is the latest large language model in the Qwen series, offering a 4-billion-parameter FP8 quantized version that supports switching between thinking and non-thinking modes, excelling in reasoning, instruction following, and agent capabilities.

Qwen3-1.7B is the 1.7B parameter version of the latest generation in the Qwen series of large language models, supporting mode switching, multilingual processing, and agent capabilities.

Qwen3-1.7B is the latest generation of the Qwen series with 1.7B parameters, supporting switching between thinking and non-thinking modes, featuring enhanced reasoning capabilities and multilingual support.

Qwen3 is the latest generation of the Qwen series large language model, offering a comprehensive set of dense and mixture-of-experts (MoE) models. Based on extensive training, Qwen3 achieves groundbreaking progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3-0.6B is a 0.6B-parameter large language model developed by Alibaba Cloud, the latest member of the Qwen3 series, supporting over 100 languages with strong reasoning, instruction-following, and multilingual capabilities.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering both dense models and mixture-of-experts (MoE) models. Through large-scale training, Qwen3 achieves breakthrough progress in reasoning capabilities, instruction following, agent functionalities, and multilingual support.

Qwen3 is the latest large language model developed by Alibaba Cloud, featuring powerful reasoning, instruction-following, and multilingual support capabilities, with the ability to switch between thinking and non-thinking modes.

Qwen3 is the latest version of Alibaba Cloud's large-scale language model series, featuring exceptional reasoning, instruction-following, and multilingual support capabilities. The 32B version is one of its dense models, supporting switching between thinking and non-thinking modes.

Qwen3-4B is the latest generation of the Qwen series large language model, featuring 4B parameters and supporting over 100 languages, with outstanding performance in reasoning, instruction following, and agent capabilities.

Qwen3-4B is the latest generation large language model in the Qwen series with 4B parameters, supporting over 100 languages and demonstrating exceptional reasoning, instruction following, and agent capabilities.

A fine-tuned version based on Qwen/Qwen2.5-7B-Instruct, specifically optimized for advanced reasoning tasks

Llama-3.1-Storm-8B is an improved model based on Llama-3.1-8B-Instruct, demonstrating excellent performance in multiple benchmarks and is suitable for dialogue and function calling tasks.

WizardLM-2 8x22B is the state-of-the-art Mixture of Experts (MoE) model developed by Microsoft's WizardLM team, with significant performance improvements in complex dialogues, multilingual tasks, reasoning, and agent tasks.

A 14B-parameter causal language model fully compatible with Meta LLaMA 2 architecture, outperforming all sub-70B models in multiple benchmarks