kimi-k2-250905
月之暗面 · Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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Kimi K2.5
月之暗面 · Kimikimi-k2.5 支持同时处理文本、图像或视频输入，并可通过 enable_thinking 参数开启思考模式
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$0.5 / M input tokensOpenRouter · moonshotai/kimi-k2.5
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View details ↗Kimi K2.6
月之暗面 · KimiKimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...
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$0.65 / M input tokensOpenRouter · moonshotai/kimi-k2.6
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View details ↗Kimi K2.7 Code
月之暗面 · KimiMoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...
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$0.6 / M input tokensOpenRouter · moonshotai/kimi-k2.7-code
View details ↗Kimi K2.7 Code
Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗kimi-k2.7-code-highspeed
月之暗面 · Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kimi K2
Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kimi-K2-Base
月之暗面 · Kimi📰 Tech Blog | 📄 Paper Link (comming soon) Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. - Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability. - MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up. - Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
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View details ↗Kimi K2 Thinking
月之暗面 · KimiKimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...
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$0.6 / M input tokensOpenRouter · moonshotai/kimi-k2-thinking
View details ↗Kimi K2 Thinking
Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kimi K3
月之暗面 · KimiKimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...
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$0.192 / M input tokensOpenRouter · moonshotai/kimi-k3
View details ↗kimi-k3.1-pro-highspeed
月之暗面 · KimiKimi-K3.1-Pro-Highspeed is the high-speed version of K3.1-Pro. It offers identical model capabilities to the standard edition, with output speeds 2–3 times faster. It supports ultra-long contexts of up to one million tokens, native image understanding, long-document parsing, code development, and tool integration, making it ideal for dialogue and Agent‑based tasks that demand rapid response times.
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View details ↗Kimi K3 (batch)
月之暗面 · KimiKimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...
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$2.28 / M input tokensOpenRouter · moonshotai/kimi-k3:batch
View details ↗Kimi K3 (Max)
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View details ↗Kimi K3 (Low)
Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kimi-Linear-48B-A3B-Base
月之暗面 · Kimi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Kimi-Linear-48B-A3B-Instruct
月之暗面 · Kimi(a) On MMLU-Pro (4k context length), Kimi Linear achieves 51.0 performance with similar speed as full attention. On RULER (128k context length), it shows Pareto-optimal performance (84.3) and 3.98x speedup. (b) Kimi Linear achieves 6.3x faster TPOT compared to MLA, offering significant speedups at long sequence lengths (1M tokens). Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods across various contexts, including short, long, and reinforcement learning (RL) scaling regimes. At its core is Kimi Delta Attention (KDA)—a refined version of Gated DeltaNet that introduces a more efficient gating mechanism to optimize the use of finite-state RNN memory. Kimi Linear achieves superior performance and hardware efficiency, especially for long-context tasks. It reduces the need for large KV caches by up to 75% and boosts decoding throughput by up to $6\times$ for contexts as long as 1M tokens.
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View details ↗Kimi-VL-A3B-Instruct
月之暗面 · KimiWe present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B). Kimi-VL demonstrates strong performance across challenging domains: as a general-purpose VLM, Kimi-VL excels in multi-turn agent interaction tasks (e.g.,OSWorld), achieving state-of-the-art results comparable to flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, optical character recognition (OCR), mathematical reasoning, multi-image understanding, and etc. In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several specialized domains.
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View details ↗Kimi-VL-A3B-Thinking
月之暗面 · Kimi> [!Warning] > This model has a new version: Kimi-VL-A3B-Thinking-2506. Please consider using the new 2506 version for better abilties on general visual understanding, reasoning, video and agent scenarios. *Please set a higher temperature for thinking model, especially when your problem requires relatively long thinking process. We have updated the following sample script for HF inference.* We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).
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