DeepSeek-V3.1 is a hybrid large language model released by DeepSeek AI, featuring significant upgrades over its predecessor. A key innovation is the integration of both a 'Thinking Mode' for deliberative, chain-of-thought reasoning and a 'Non-thinking Mode' for direct responses, which can be switched via the chat template to suit various tasks. The model's capabilities in tool use and agent tasks have been substantially improved through post-training optimization, enabling better support for external search tools and complex multi-step instructions. DeepSeek-V3.1 is post-trained on top of the DeepSeek-V3.1-Base model, which underwent a two-phase long-context extension with a vastly expanded dataset, enhancing its ability to process long documents and codebases. As an open-source model, DeepSeek-V3.1 demonstrates performance comparable to leading closed-source models on various benchmarks, particularly in coding, math, and reasoning, while its Mixture-of-Experts (MoE) architecture maintains a massive parameter count while reducing inference costs

DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness

The new version of DeepSeek-V3 (DeepSeek-V3-0324) utilizes the same base model as the previous DeepSeek-V3-1226, with improvements made only to the post-training methods. The new V3 model incorporates reinforcement learning techniques from the training process of the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. Additionally, the model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities.

Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2. It is a state-of-the-art mixture-of-experts (MoE) language model, featuring 32 billion activated parameters and a total of 1 trillion parameters. Key features include enhanced agentic coding intelligence, with the model demonstrating significant improvements on public benchmarks and real-world coding agent tasks; an improved frontend coding experience, offering advancements in both the aesthetics and practicality of frontend programming

gpt-oss-120b is OpenAI’s open-weight large language model with ~117B parameters (5.1B active), using a Mixture-of-Experts (MoE) design and MXFP4 quantization to run on a single 80 GB GPU. It delivers o4-mini-level or better performance in reasoning, coding, health, and math benchmarks, with full Chain-of-Thought (CoT), tool use, and Apache 2.0-licensed commercial deployment support.

gpt-oss-20b is OpenAI’s lightweight open-weight model with ~21B parameters (3.6B active), built on an MoE architecture and MXFP4 quantization to run locally on 16 GB VRAM devices. It matches o3-mini in reasoning, math, and health tasks, supporting CoT, tool use, and deployment via frameworks like Transformers, vLLM, and Ollama.

GLM-4.5 is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture. It has been extensively optimized for tool use, web browsing, software development, and front-end development, enabling seamless integration with coding agents such as Claude Code and Roo Code. GLM-4.5 employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of application scenarios—from complex reasoning tasks to everyday use cases

GLM-4.5-Air is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture. It has been extensively optimized for tool use, web browsing, software development, and front-end development, enabling seamless integration with coding agents such as Claude Code and Roo Code. GLM-4.5 employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of application scenarios—from complex reasoning tasks to everyday use cases

GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. The model is built upon the flagship text model GLM-4.5-Air, which has 106B total parameters and 12B active parameters, and it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. Technically, GLM-4.5V follows the lineage of GLM-4.1V-Thinking and introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. Through optimization across pre-training, supervised fine-tuning, and reinforcement learning phases, the model is capable of processing diverse visual content such as images, videos, and long documents, achieving state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks. Additionally, the model features a 'Thinking Mode' switch, allowing users to flexibly choose between quick responses and deep reasoning to balance efficiency and effectiveness

Ling-flash-2.0 is a language model from inclusionAI with a total of 100 billion parameters, of which 6.1 billion are activated per token (4.8 billion non-embedding). As part of the Ling 2.0 architecture series, it is designed as a lightweight yet powerful Mixture-of-Experts (MoE) model. It aims to deliver performance comparable to or even exceeding that of 40B-level dense models and other larger MoE models, but with a significantly smaller active parameter count. The model represents a strategy focused on achieving high performance and efficiency through extreme architectural design and training methods

Ling-mini-2.0 is a small yet high-performance large language model built on the MoE architecture. It has 16B total parameters, but only 1.4B are activated per token (non-embedding 789M), enabling extremely fast generation. Thanks to the efficient MoE design and large-scale high-quality training data, despite having only 1.4B activated parameters, Ling-mini-2.0 still delivers top-tier downstream task performance comparable to sub-10B dense LLMs and even larger MoE models

The Hunyuan Translation Model consists of a translation model, Hunyuan-MT-7B, and an ensemble model, Hunyuan-MT-Chimera. Hunyuan-MT-7B is a lightweight translation model with 7 billion parameters used to translate source text into the target language. The model supports mutual translation among 33 languages, including five ethnic minority languages in China. In the WMT25 machine translation competition, Hunyuan-MT-7B won first place in 30 out of the 31 language categories it participated in, demonstrating its outstanding translation capabilities. For translation tasks, Tencent Hunyuan proposed a comprehensive training framework covering pre-training, supervised fine-tuning, translation enhancement, and ensemble refinement, achieving state-of-the-art performance among models of a similar scale. The model is computationally efficient and easy to deploy, making it suitable for various application scenarios