GLM-4.7 is Zhipu’s new-generation flagship model, with 355B total parameters and 32B activated parameters, delivering comprehensive upgrades in general conversation, reasoning, and agent capabilities. Responses are more concise and natural; writing feels more immersive; tool-call instructions are followed more reliably; and the front-end polish of artifacts and agentic coding—along with long-horizon task completion efficiency—has been further improved....

DeepSeek-V3.2 is a model that harmonizes high computational efficiency with superior reasoning and agent performance. Its approach is built upon three key technical breakthroughs: DeepSeek Sparse Attention (DSA), an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios; a Scalable Reinforcement Learning Framework, which enables performance comparable to GPT-5 and reasoning proficiency on par with Gemini-3.0-Pro in its high-compute variant; and a Large-Scale Agentic Task Synthesis Pipeline to integrate reasoning into tool-use scenarios, improving compliance and generalization in complex interactive environments. The model has achieved gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI)...

DeepSeek-V3.2-Exp is an experimental version of DeepSeek model, built on V3.1-Terminus. It debuts DeepSeek Sparse Attention(DSA) for faster, more efficient training & inference on long context....

GLM-4.6V achieves SOTA (State-of-the-Art) accuracy in visual understanding among models of the same parameter scale. For the first time, it natively integrates Function Call capabilities into the visual model architecture, bridging the gap between "Visual Perception" and "Executable Action." This provides a unified technical foundation for multimodal Agents in real-world business scenarios. Additionally, the visual context window has been expanded to 128k, supporting long video stream processing and high-resolution multi-image analysis....

DeepSeek-V3.1-Terminus is an updated version built on V3.1’s strengths while addressing key user feedback. It improves in language consistency, reducing instances of mixed Chinese-English text and occasional abnormal characters. And also upgrades in stronger Code Agent & Search Agent performance....

DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved. DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly....

DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects, including major boost in reasoning performance, stronger front-end development skills and smarter tool-use capabilities....

DeepSeek-R1-0528 is an upgraded model shows significant improvements in handling complex reasoning tasks，also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding. It achieves performance comparable to O3 and Gemini 2.5 Pro....

DeepSeek-V3.1-Nex-N1 is a large language model developed based on leading open-source models and optimized through post-training. This optimization significantly enhances its agency, leading to outstanding performance in Agent tasks and code generation and understanding, tool usage, and role-playing. The model excels at decomposing complex tasks into multi-step plans and proactively clarifying ambiguities to ensure reliable and accurate execution....

MiniMax-M2 redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, MiniMax-M2 provides the sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever...

Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, we built it as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 262k context window, achieving lossless reductions in inference latency and GPU memory usage...

Kimi K2-Instruct-0905, a state-of-the-art mixture-of-experts (MoE) language model, is the latest, most capable version of Kimi K2. Key Features include enhanced coding capabilities, esp. front-end & tool-calling, context length extended to 256k tokens, and improved integration with various agent scaffolds....

The gpt-oss series is OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. gpt-oss-120b is for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X)....

The gpt-oss series is OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. gpt-oss-20b is for lower latency, and local or specialized use cases....

Compared with GLM-4.5, GLM-4.6 brings several key improvements， including longer context window expanded to 200K tokens, superior coding performance, advanced reasoning, more capable agents, and refined writing....

The GLM-4.5 series models are foundation models designed for intelligent agents, by unifying reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, providing two modes: thinking and non-thinking....

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. It’s also a hybrid reasoning model providing both thinking and non-thinking mode. ...

Ring-flash-2.0 is a high-performance thinking model, deeply optimized based on Ling-flash-2.0-base. It is a Mixture-of-Experts (MoE) model with a total of 100B parameters, but only 6.1B are activated per inference. The model leverages the independently developed 'icepop' algorithm to address the training instability challenges in reinforcement learning (RL) for MoE LLMs, enabling continuous improvement of its complex reasoning capabilities throughout extended RL training cycles. Ring-flash-2.0 demonstrates significant breakthroughs across challenging benchmarks, including math competitions, code generation, and logical reasoning. Its performance surpasses that of SOTA dense models under 40B parameters and rivals larger open-weight MoE models and closed-source high-performance thinking model APIs. More surprisingly, although Ring-flash-2.0 is primarily designed for complex reasoning, it also shows strong capabilities in creative writing. Thanks to its efficient architecture, it achieves high-speed inference, significantly reducing inference costs for thinking models in high-concurrency scenarios...

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...

Qwen3-Next-80B-A3B-Thinking is a next-generation foundation model from Alibaba's Qwen team, specifically designed for complex reasoning tasks. It is built on the innovative Qwen3-Next architecture, which combines a Hybrid Attention mechanism (Gated DeltaNet and Gated Attention) with a High-Sparsity Mixture-of-Experts (MoE) structure to achieve ultimate training and inference efficiency. As an 80-billion-parameter sparse model, it activates only about 3 billion parameters during inference, significantly reducing computational costs and delivering over 10 times higher throughput than the Qwen3-32B model on long-context tasks exceeding 32K tokens. This 'Thinking' version is optimized for demanding multi-step problems like mathematical proofs, code synthesis, logical analysis, and agentic planning, and it outputs structured 'thinking' traces by default. In terms of performance, it surpasses more costly models like Qwen3-32B-Thinking and has outperformed Gemini-2.5-Flash-Thinking on multiple benchmarks...

Qwen3-Next-80B-A3B-Instruct is a next-generation foundation model released by Alibaba's Qwen team. It is built on the new Qwen3-Next architecture, designed for ultimate training and inference efficiency. The model incorporates innovative features such as a Hybrid Attention mechanism (Gated DeltaNet and Gated Attention), a High-Sparsity Mixture-of-Experts (MoE) structure, and various stability optimizations. As an 80-billion-parameter sparse model, it activates only about 3 billion parameters per token during inference, which significantly reduces computational costs and delivers over 10 times higher throughput than the Qwen3-32B model for long-context tasks exceeding 32K tokens. This is an instruction-tuned version optimized for general-purpose tasks and does not support 'thinking' mode. In terms of performance, it is comparable to Qwen's flagship model, Qwen3-235B, on certain benchmarks, showing significant advantages in ultra-long-context scenarios...

Qwen3-Coder-480B-A35B-Instruct is the most agentic code model released by Alibaba to date. It is a Mixture-of-Experts (MoE) model with 480 billion total parameters and 35 billion activated parameters, balancing efficiency and performance. The model natively supports a 256K (approximately 262,144) token context length, which can be extended up to 1 million tokens using extrapolation methods like YaRN, enabling it to handle repository-scale codebases and complex programming tasks. Qwen3-Coder is specifically designed for agentic coding workflows, where it not only generates code but also autonomously interacts with developer tools and environments to solve complex problems. It has achieved state-of-the-art results among open models on various coding and agentic benchmarks, with performance comparable to leading models like Claude Sonnet 4. Alongside the model, Alibaba has also open-sourced Qwen Code, a command-line tool designed to fully unleash its powerful agentic coding capabilities...

Qwen3-Coder-30B-A3B-Instruct is a code model from the Qwen3 series developed by Alibaba's Qwen team. As a streamlined and optimized model, it maintains impressive performance and efficiency while focusing on enhanced coding capabilities. It demonstrates significant performance advantages among open-source models on complex tasks such as Agentic Coding, Agentic Browser-Use, and other foundational coding tasks. The model natively supports a long context of 256K tokens, which can be extended up to 1M tokens, enabling better repository-scale understanding and processing. Furthermore, it provides robust agentic coding support for platforms like Qwen Code and CLINE, featuring a specially designed function call format...

Qwen3-30B-A3B-Instruct-2507 is the updated version of the Qwen3-30B-A3B non-thinking mode. It is a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion activated parameters. This version features key enhancements, including significant improvements in general capabilities such as instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. It also shows substantial gains in long-tail knowledge coverage across multiple languages and offers markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. Furthermore, its capabilities in long-context understanding have been enhanced to 256K. This model supports only non-thinking mode and does not generate `<think></think>` blocks in its output...

Qwen3-30B-A3B-Thinking-2507 is the latest thinking model in the Qwen3 series, released by Alibaba's Qwen team. As a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion active parameters, it is focused on enhancing capabilities for complex tasks. The model demonstrates significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. It also shows markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences. The model natively supports a 256K long-context understanding capability, which can be extended to 1 million tokens. This version is specifically designed for ‘thinking mode’ to tackle highly complex problems through step-by-step reasoning and also excels in agentic capabilities...

Qwen3-235B-A22B-Instruct-2507 is a flagship Mixture-of-Experts (MoE) large language model from the Qwen3 series, developed by Alibaba Cloud's Qwen team. The model has a total of 235 billion parameters, with 22 billion activated per forward pass. It was released as an updated version of the Qwen3-235B-A22B non-thinking mode, featuring significant enhancements in general capabilities such as instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. Additionally, the model provides substantial gains in long-tail knowledge coverage across multiple languages and shows markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. Notably, it natively supports an extensive 256K (262,144 tokens) context window, which enhances its capabilities for long-context understanding. This version exclusively supports the non-thinking mode and does not generate <think> blocks, aiming to deliver more efficient and precise responses for tasks like direct Q&A and knowledge retrieval...

Qwen3-235B-A22B-Thinking-2507 is a member of the Qwen3 large language model series developed by Alibaba's Qwen team, specializing in highly complex reasoning tasks. The model is built on a Mixture-of-Experts (MoE) architecture, with 235 billion total parameters and approximately 22 billion activated parameters per token, which enhances computational efficiency while maintaining powerful performance. As a dedicated 'thinking' model, it demonstrates significantly improved performance on tasks requiring human expertise, such as logical reasoning, mathematics, science, coding, and academic benchmarks, achieving state-of-the-art results among open-source thinking models. Furthermore, the model features enhanced general capabilities like instruction following, tool usage, and text generation, and it natively supports a 256K long-context understanding capability, making it ideal for scenarios that require deep reasoning and processing of long documents...

Seed-OSS is a series of open-source large language models developed by the ByteDance Seed team, designed for powerful long-context processing, reasoning, agent capabilities, and general-purpose abilities. Within this series, Seed-OSS-36B-Instruct is an instruction-tuned model with 36 billion parameters that natively supports an ultra-long context length, enabling it to process massive documents or complex codebases in a single pass. The model is specially optimized for reasoning, code generation, and agent tasks (such as tool use), while maintaining balanced and excellent general-purpose capabilities. A key feature of this model is the ‘Thinking Budget’ function, which allows users to flexibly adjust the reasoning length as needed, thereby effectively improving inference efficiency in practical applications...

ERNIE-4.5-300B-A47B is a large language model developed by Baidu based on a Mixture-of-Experts (MoE) architecture. The model has a total of 300 billion parameters, but only activates 47 billion parameters per token during inference, thus balancing powerful performance with computational efficiency. As one of the core models in the ERNIE 4.5 series, it is trained on the PaddlePaddle deep learning framework and demonstrates outstanding capabilities in tasks such as text understanding, generation, reasoning, and coding. The model utilizes an innovative multimodal heterogeneous MoE pre-training method, which effectively enhances its overall abilities through joint training on text and visual modalities, showing prominent results in instruction following and world knowledge memorization. Baidu has open-sourced this model along with others in the series to promote the research and application of AI technology...

Hunyuan-A13B-Instruct activates only 13 B of its 80 B parameters, yet matches much larger LLMs on mainstream benchmarks. It offers hybrid reasoning: low-latency “fast” mode or high-precision “slow” mode, switchable per call. Native 256 K-token context lets it digest book-length documents without degradation. Agent skills are tuned for BFCL-v3, τ-Bench and C3-Bench leadership, making it an excellent autonomous assistant backbone. Grouped Query Attention plus multi-format quantization delivers memory-light, GPU-efficient inference for real-world deployment, with built-in multilingual support and robust safety alignment for enterprise-grade applications....

Kimi K2 is a Mixture-of-Experts (MoE) foundation model with exceptional coding and agent capabilities, featuring 1 trillion total parameters and 32 billion activated parameters. In benchmark evaluations covering general knowledge reasoning, programming, mathematics, and agent-related tasks, the K2 model outperforms other leading open-source models...

Kimi-Dev-72B is a new open-source coding large language model achieving 60.4% on SWE-bench Verified, setting a state-of-the-art result among open-source models. Optimized through large-scale reinforcement learning, it autonomously patches real codebases in Docker and earns rewards only when full test suites pass. This ensures the model delivers correct, robust, and practical solutions aligned with real-world software engineering standards...

MiniMax-M1 is a open-weight, large-scale hybrid-attention reasoning model with 456 B parameters and 45.9 B activated per token. It natively supports 1 M-token context, lightning attention enabling 75% FLOPs savings vs DeepSeek R1 at 100 K tokens, and leverages a MoE architecture. Efficient RL training with CISPO and hybrid design yields state-of-the-art performance on long-input reasoning and real-world software engineering tasks....

Qwen3-30B-A3B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 30.5B total parameters and 3.3B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, superior human preference alignment in creative writing, role-playing, and multi-turn dialogues. The model excels in agent capabilities for precise integration with external tools and supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities...

Qwen3-32B is the latest large language model in the Qwen series with 32.8B parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. Additionally, it supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities...

Qwen3-14B is the latest large language model in the Qwen series with 14.8B parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. Additionally, it supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities...

Qwen3-8B is the latest large language model in the Qwen series with 8.2B parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, surpassing previous QwQ and Qwen2.5 instruct models in mathematics, code generation, and commonsense logical reasoning. The model excels in human preference alignment for creative writing, role-playing, and multi-turn dialogues. Additionally, it supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities...

GLM-Z1-32B-0414 is a reasoning model with deep thinking capabilities. This model was developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, as well as further training on tasks involving mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, the team also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities. Despite having only 32B parameters, its performance on certain tasks is comparable to DeepSeek-R1 with 671B parameters. Through evaluations on benchmarks such as AIME 24/25, LiveCodeBench, and GPQA, the model demonstrates strong mathematical reasoning abilities and can support solutions for a wider range of complex tasks...

GLM-4-32B-0414 is a new generation model in the GLM family with 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, the team enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. On several benchmarks, its performance approaches or even exceeds that of larger models like GPT-4o and DeepSeek-V3-0324 (671B)...

GLM-Z1-9B-0414 is a small-sized model in the GLM series with only 9 billion parameters that maintains the open-source tradition while showcasing surprising capabilities. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent performance in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. The research team employed the same series of techniques used for larger models to train this 9B model. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment. The model features deep thinking capabilities and can handle long contexts through YaRN technology, making it particularly suitable for applications requiring mathematical reasoning abilities with limited computational resources...

GLM-4-9B-0414 is a small-sized model in the GLM series with 9 billion parameters. This model inherits the technical characteristics of the GLM-4-32B series but offers a more lightweight deployment option. Despite its smaller scale, GLM-4-9B-0414 still demonstrates excellent capabilities in code generation, web design, SVG graphics generation, and search-based writing tasks. The model also supports function calling features, allowing it to invoke external tools to extend its range of capabilities. The model shows a good balance between efficiency and effectiveness in resource-constrained scenarios, providing a powerful option for users who need to deploy AI models under limited computational resources. Like other models in the same series, GLM-4-9B-0414 also demonstrates competitive performance in various benchmark tests...

Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities, superior human preference alignment in creative writing, role-playing, and multi-turn dialogues. The model excels in agent capabilities for precise integration with external tools and supports over 100 languages and dialects with strong multilingual instruction following and translation capabilities...

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini. The model incorporates technologies like RoPE, SwiGLU, RMSNorm, and Attention QKV bias, with 64 layers and 40 Q attention heads (8 for KV in GQA architecture)...

DeepSeek-R1-Distill-Qwen-32B is a distilled model based on Qwen2.5-32B. The model was fine-tuned using 800k curated samples generated by DeepSeek-R1 and demonstrates exceptional performance across mathematics, programming, and reasoning tasks. It achieved impressive results in various benchmarks including AIME 2024, MATH-500, and GPQA Diamond, with a notable 94.3% accuracy on MATH-500, showcasing its strong mathematical reasoning capabilities...

DeepSeek-R1-Distill-Qwen-14B is a distilled model based on Qwen2.5-14B. The model was fine-tuned using 800k curated samples generated by DeepSeek-R1 and demonstrates strong reasoning capabilities. It achieved impressive results across various benchmarks, including 93.9% accuracy on MATH-500, 69.7% pass rate on AIME 2024, and a rating of 1481 on CodeForces, showcasing its powerful abilities in mathematics and programming tasks...

DeepSeek-R1-Distill-Qwen-7B is a distilled model based on Qwen2.5-Math-7B. The model was fine-tuned using 800k curated samples generated by DeepSeek-R1 and demonstrates strong reasoning capabilities. It achieved impressive results across various benchmarks, including 92.8% accuracy on MATH-500, 55.5% pass rate on AIME 2024, and a rating of 1189 on CodeForces, showing remarkable mathematical and programming abilities for a 7B-scale model...

Qwen2.5-Coder-32B-Instruct is a code-specific large language model developed based on Qwen2.5. The model has undergone training on 5.5 trillion tokens, achieving significant improvements in code generation, code reasoning, and code repair. It is currently the most advanced open-source code language model, with coding capabilities comparable to GPT-4. Not only has the model enhanced coding abilities, but it also maintains strengths in mathematics and general capabilities, and supports long text processing....

Qwen2.5-72B-Instruct is one of the latest large language models series released by Alibaba Cloud. This 72B model demonstrates significant improvements in areas such as coding and mathematics. It supports a context length of up to 128K tokens. The model also offers multilingual support, covering over 29 languages, including Chinese, English, and others. It has shown notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-72B-Instruct is one of the latest large language model series released by Alibaba Cloud. The 72B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multilingual support, covering over 29 languages, including Chinese and English. It shows notable enhancements in following instructions, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-32B-Instruct is one of the latest large language models series released by Alibaba Cloud. This 32B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multi-language support, covering over 29 languages, including Chinese, English, and others. It shows notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-14B-Instruct is one of the latest large language model series released by Alibaba Cloud. This 14B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multi-language support, covering over 29 languages, including Chinese and English. It has shown notable advancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-7B-Instruct is one of the latest large language model series released by Alibaba Cloud. This 7B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multilingual support, covering over 29 languages, including Chinese, English, and others. The model shows notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly JSON....

Meta Llama 3.1 is a family of multilingual large language models developed by Meta, featuring pretrained and instruction-tuned variants in 8B, 70B, and 405B parameter sizes. This 8B instruction-tuned model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed chat models on common industry benchmarks. The model was trained on over 15 trillion tokens of publicly available data, using techniques like supervised fine-tuning and reinforcement learning with human feedback to enhance helpfulness and safety. Llama 3.1 supports text and code generation, with a knowledge cutoff of December 2023...