A.X-3.1
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 13
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查看详情 ↗A.X-4.0
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查看详情 ↗SK Telecom released **A.X 4.0** (pronounced "A dot X"), a large language model (LLM) optimized for Korean-language understanding and enterprise deployment, on July 03, 2025. Built on the open-source Qwen2.5 model, A.X 4.0 has been further trained with large-scale Korean datasets to deliver outstanding performance in real-world business environments. - **Superior Korean Proficiency**: Achieved a score of 78.3 on KMMLU, the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming GPT-4o (72.5). - **Deep Cultural Understanding**: Scored 83.5 on CLIcK, a benchmark for Korean cultural and contextual comprehension, surpassing GPT-4o (80.2). - **Efficient Token Usage**: A.X 4.0 uses approximately 33% fewer tokens than GPT-4o for the same Korean input, enabling more cost-effective and efficient processing. - **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light). - **Long Context Handling**: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length) Benchmarks A.X 4.0 Qwen3-235B-A22B (w/o reasoning) Qwen2.5-72B GPT-4o
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查看详情 ↗A.X-K1
skt**A.X K1** is a large-scale Mixture-of-Experts (MoE) language model designed for efficient high-capacity reasoning and instruction following, and was trained from scratch without initializing from an existing pretrained model. The model contains **519 billion total parameters**, with **33 billion active parameters**, enabling strong performance while maintaining practical inference efficiency. This hybrid design allows the user to choose between in-depth reasoning and response latency depending on task requirements. A.X K1 follows a sparse Mixture-of-Experts architecture in which only a subset of experts is activated per token. This design substantially increases model capacity while keeping the computational cost comparable to dense models with much smaller parameter counts.
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查看详情 ↗A.X-K2
skt**A.X K2** is a large-scale Mixture-of-Experts (MoE) language model trained from scratch as a high-performance, **agentic** foundation model, and the successor to A.X K1. The model contains **688 billion total parameters**, with **33 billion active parameters**, delivering strong reasoning and instruction-following performance while maintaining practical inference efficiency. Through a **Think-Fusion** training recipe, a single unified model supports both a **thinking** mode for complex problem solving and a **non-thinking** mode for concise, low-latency responses, allowing the user to trade quality for cost on a per-request basis. A.X K2 is developed as part of the Korean government's **Sovereign AI foundation model** project, aiming to build a frontier-scale model with deep understanding of the Korean language and culture.
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查看详情 ↗A.X-K2-NVFP4
skt**A.X K2** is a large-scale Mixture-of-Experts (MoE) language model trained from scratch as a high-performance, **agentic** foundation model, and the successor to A.X K1. The model contains **688 billion total parameters**, with **33 billion active parameters**, delivering strong reasoning and instruction-following performance while maintaining practical inference efficiency. Through a **Think-Fusion** training recipe, a single unified model supports both a **thinking** mode for complex problem solving and a **non-thinking** mode for concise, low-latency responses, allowing the user to trade quality for cost on a per-request basis. A.X K2 is developed as part of the Korean government's **Sovereign AI foundation model** project, aiming to build a frontier-scale model with deep understanding of the Korean language and culture.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。