Kimi-K2-Instruct API, Deployment, Pricing

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

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-4.5 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-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-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-4-32B-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-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

GLM-4.1V-9B-Thinking is an open-source Vision-Language Model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG lab, designed to advance general-purpose multimodal reasoning. Built upon the GLM-4-9B-0414 foundation model, it introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling (RLCS) to significantly enhance its capabilities in complex tasks. As a 9B-parameter model, it achieves state-of-the-art performance among models of a similar size, and its performance is comparable to or even surpasses the much larger 72B-parameter Qwen-2.5-VL-72B on 18 different benchmarks. The model excels in a diverse range of tasks, including STEM problem-solving, video understanding, and long document understanding, and it can handle images with resolutions up to 4K and arbitrary aspect ratios

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.

Step3 is a cutting-edge multimodal reasoning model from StepFun. It is built on a Mixture-of-Experts (MoE) architecture with 321B total parameters and 38B active parameters. The model is designed end-to-end to minimize decoding costs while delivering top-tier performance in vision-language reasoning. Through the co-design of Multi-Matrix Factorization Attention (MFA) and Attention-FFN Disaggregation (AFD), Step3 maintains exceptional efficiency across both flagship and low-end accelerators. During pretraining, Step3 processed over 20T text tokens and 4T image-text mixed tokens, spanning more than ten languages. The model has achieved state-of-the-art performance for open-source models on various benchmarks, including math, code, and multimodality

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)

Qwen3-Reranker-8B is the 8-billion parameter text reranking model from the Qwen3 series. It is designed to refine and improve the quality of search results by accurately re-ordering documents based on their relevance to a query. Built on the powerful Qwen3 foundational models, it excels in understanding long-text with a 32k context length and supports over 100 languages. The Qwen3-Reranker-8B model is part of a flexible series that offers state-of-the-art performance in various text and code retrieval scenarios