Ling-flash-2.0
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探索 AI 模型 84999
Ling-lite
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查看详情 ↗Ling-lite-1.5
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查看详情 ↗Ling-lite-1.5-2507
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查看详情 ↗Ling-lite-base-1.5
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查看详情 ↗Ling-mini-2.0
inclusionAIToday, we are excited to announce the open-sourcing of **Ling 2.0** — a family of MoE-based large language models that combine **SOTA performance** with **high efficiency**. The first released version, Ling-mini-2.0, is compact yet powerful. It has **16B total parameters**, but only **1.4B** are activated per input token (non-embedding 789M). Trained on more than **20T tokens** of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-mini-2.0 achieves remarkable improvements in complex reasoning and instruction following. With just 1.4B activated parameters, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models. We evaluated Ling-mini-2.0 on challenging general reasoning tasks in coding (LiveCodeBench, CodeForces) and mathematics (AIME 2025, HMMT 2025), as well as knowledge-intensive reasoning tasks across multiple domains (MMLU-Pro, Humanity's Last Exam). Compared with sub-10B dense models (e.g., Qwen3-4B-instruct-2507, Qwen3-8B-nothinking) and larger-scale MoE models (Ernie-4.5-21B-A3B-PT, GPT-OSS-20B/low), Ling-mini-2.0 demonstrated outstanding overall reasoning capabilities. Guided by Ling Scaling Laws, Ling 2.0 adopts a **1/32 activation ratio** MoE architecture, with empirically optimized design choices in expert granularity, shared expert ratio, attention ratio, aux-loss free + sigmoid routing strategy, MTP loss, QK-Norm, half RoPE, and more. This enables small-activation MoE models to achieve over **7× equivalent dense performance**. In other words, **Ling-mini-2.0 with only 1.4B activated parameters (non-embedding 789M) can deliver performance equivalent to a 7–8B dense model**.
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查看详情 ↗Ling-mini-2.0-GGUF
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查看详情 ↗Ling-mini-base-2.0
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查看详情 ↗Ling-plus
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查看详情 ↗Ling-plus-base
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查看详情 ↗LLaDA-Image
inclusionAIWelcome to the official repository for LLaDA-Image, a unified model for high-quality image generation and editing. LLaDA-Image is a competitive 6B-parameter open-source unified image generation and editing model family. It includes **LLaDA-Image**, a 50-step Base model for high-quality text-to-image generation and instruction-guided editing, and **LLaDA-Image-Turbo**, a 4-step distilled model for fast generation and editing. Both variants support practical text-to-image generation, VQ-conditioned generation, reference-image editing, and Chinese--English text rendering. This repository provides the checkpoints and Diffusers-based inference code for the LLaDA-Image model family.
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查看详情 ↗LLaDA-Image-Turbo
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查看详情 ↗LLaDA-MoE-7B-A1B-Base
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查看详情 ↗LLaDA-MoE-7B-A1B-Instruct
inclusionAI**LLaDA-MoE** is a new and upgraded series of the LLaDA diffusion language model. This pre-release includes two cutting-edge models: On HumanEval, dInfer achieves over 1,100 TPS at batch size 1, and averages more than 800 TPS across six benchmarks on a single node with 8 H800 GPUs. For questions, collaborations, or feedback, please reach out via Hugging Face or open an issue in the repository.
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查看详情 ↗LLaDA-UI
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查看详情 ↗LLaDA2.0-mini
inclusionAI**LLaDA2.0-mini** is a diffusion language model featuring a 16BA1B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA series, it is optimized for practical applications. + **Leading MoE Architecture**: The open-source **Mixture-of-Experts (MoE) diffusion large language model** continually trained on the Ling2.0 series with approximately **20 trillion tokens**. + **Efficient Inference**: With **16 billion total parameters**, only **1.4 billion** are activated during inference. LLaDA2.0-mini significantly reduces computational costs while outperforming open-source dense models of similar scale. + **Impressive Performance on Code & Complex Reasoning**: Excels in tasks such as **code generation** and **advanced mathematical reasoning**, demonstrating strong reasoning capabilities. + **Tool Use**: Supports **tool calling** and achieves excellent performance in complex agent-based tasks. + **Open & Extensible**: Fully open-source with commitment to transparency. We plan to release a **leading inference framework** in the future and continue investing in cutting-edge areas like **diffusion LLMs (dLLM)** to drive disruptive innovation. + **Supercharged Reasoning with LLaDA 2.0:** LLaDA 2.0 series will be fine-tuned with **Reinforcement Learning**, unlocking a new level of sophisticated reasoning and problem-solving abilities. + **Tools for Innovators:** The model was finetuned on the dFactory framework using Fully Sharded Data Parallel (FSDP2). We have begun open-sourcing dFactory and will continuously release our advanced post-training technologies. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned for more updates!
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查看详情 ↗LLaDA2.0-mini-preview
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查看详情 ↗LLaDA2.0-Uni
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查看详情 ↗LLaDA2.1-flash
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查看详情 ↗LLaDA2.1-mini
inclusionAI🚀 **LLaDA2.1-flash** is now live on **ZenmuxAI**! Try it via API 🛠️ or Chat 💬: https://zenmux.ai/inclusionai/llada2.1-flash **LLaDA2.1-mini** is a diffusion language model of the LLaDA series featuring the editing enhancement. It significantly improves inference speed while delivering strong task performance. Benchmark Qwen3-8B (no_think) (Score) Ling-mini-2.0 (Score) LLaDA2.0-mini (Score | TPF) LLaDA2.1-mini (S Mode) (Score | TPF) LLaDA2.1-mini (Q Mode) (Score | TPF)
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查看详情 ↗LLaDA2.2-flash
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查看详情 ↗LLaDA2.2-mini
inclusionAI**LLaDA2.2-mini** is the lightweight variant of the agentic diffusion language model in the LLaDA2 series. Built upon the LLaDA2.0-mini architecture, it inherits the core innovations of the LLaDA2.2 series — **Levenshtein Editing** (introducing `DELETE` and `INSERT` control tokens) — enabling long-context tool calling, multi-turn interaction, and robust error correction, while maintaining a smaller parameter footprint and lower inference cost. For more details, please refer to our technical report. The following table compares **LLaDA2.0-mini**, **LLaDA2.1-mini**, and **LLaDA2.2-mini** across General and Agentic capabilities. General Function Calling BFCL v4 25.05 28.44 47.68 BFCL v3 70.72 72.06 69.02 Math AIME 2026 37.71 40.37 35.05 OlympiadBench 67.70 64.30 61.11 Coding LiveCodeBench v6 27.70 28.80 28.14 MultiPL-E 67.46 64.16 65.26 Instruction Following IFBench 32.33 31.60 24.93 Multi-IF 60.58 58.43 57.03 Reasoning KOR-Bench 49.92 46.64 43.60 Knowledge GPQA-Diamond 47.76 48.36 44.41 Long Context LongBench v2 15.51 12.13 34.99 General Average 45.68 45.03 46.47 Agentic Agent τ²-Bench - - 57.50 Claw-Eval - - 57.16 PinchBench - - 62.33 Agentic Average - - 59.00
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查看详情 ↗Ming-3.0-asr-tiny
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查看详情 ↗Ming-3.0-audio-flash
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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。