从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. You can directly download the model in this repository. You also can download the model in python script: For face encoder, you need to manutally download via this URL to `models/antelopev2`.
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Diffusers / PEFT conversion of `larryvrh/MiniMax-H3-Turbo-Lora`: a LoRA that lets MiniMax-H3 render joint **video + synchronized stereo audio** in about **4 sampling steps** instead of the usual ~20. > ⚠️ **Early prototype.** Same caveat as the upstream release: under-trained preview weights, not production quality. They already beat the base model at 4 steps (sharper detail, cleaner / better-synced audio), but treat this as a work-in-progress taste, not a finished product. Prefer the non-EMA `ckpt500` weights by default. Converted from the upstream Turbo LoRA (bf16, `W_eff = W + lora_B @ lora_A`, **alpha = rank** so scale is 1). QKV is split into `to_q` / `to_k` / `to_v`, and SwiGLU `fc1` halves are swapped to match Diffusers' `[value; gate]` layout.
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This repository provides a ControlNet that supports mask-based image inpainting and outpainting for Qwen-Image. ComfyUI offers native support for Qwen-Image-ControlNet-Inpainting. The official workflow can be found here. Make sure your ComfyUI version is >=0.3.59. Liblib AI offers native support for Qwen-Image-ControlNet-Inpainting. Visit for online WebUI or ComfyUI inference.
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This repository provides a unified ControlNet that supports 4 common control types (canny, soft edge, depth, pose) for Qwen-Image. - This ControlNet consists of 5 double blocks copied from the pretrained transformer layers. - We train the model from scratch for 50K steps using a dataset of 10M high-quality general and human images. - We train at 1328x1328 resolution in BFloat16, batch size=64, learning rate=4e-5. We set the text drop ratio to 0.10. - This model supports multiple control modes, including canny, soft edge, depth, pose. You can use it just as a normal ControlNet. You can adjust control strength via controlnet_conditioning_scale. - Canny: use cv2.Canny, set controlnet_conditioning_scale in [0.8, 1.0] - Soft Edge: use AnylineDetector, set controlnet_conditioning_scale in [0.8, 1.0] - Depth: use depth-anything, set controlnet_conditioning_scale in [0.8, 1.0] - Pose: use DWPose, set controlnet_conditioning_scale in [0.8, 1.0]
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K2-Horizon-375B-A23B is the flagship of the K2-Horizon family: a sparse Mixture-of-Experts model that stores 375B parameters and runs 23B per token, with a 512K context window. We have released the checkpoints, along with the data and the training code. - **Frontier-class agentic performance.** On agentic tool use, terminal, and long-horizon workflow benchmarks it matches or beats open-weight MoE models up to 2.6× its size and is competitive with closed frontier models (see Benchmark Results). - **512K context.** Native 524,288-token context from the midtraining stages onward. - **Intermediate checkpoints.** Intermediate checkpoints are released so capability changes can be studied across training rather than at a single checkpoint. - **Fully open.** Training data/recipe and the training code are public. Open-weight models Closed models K2-Horizon-375B-A23B Nemotron 3 Ultra Inkling (xhigh) MiniMax-M3 GLM 5.2 (max) GPT 5.6 Luna (max) GPT 5.6 Terra (high) Claude Sonnet5 (max) # Params 375B 550B 975B 428B 753B -- -- -- # Activated params 23B 55B 41B 23B 40B -- -- -- Architecture MoE MoE MoE MoE MoE Closed Closed Closed Agents GDPVal-AA Real-world professional tasks (Elo) 1,441 1,162 1,234 1,380 1,498 1,569 1,503 1,584 tau3-Banking Agentic tool use 34.0 14.2 29.1 15.3 34.6 31.1 28.7 37.3 Toolathlon Verified Agentic tool use 65.3 34.3 45.5 53.7 59.9 67.5 64.8 71.6 Automation Bench Public Workflow automation 25.3 8.0 12.8 20.5 26.2 33.5 28.0 34.7 Apex-Agents (pass@1) Long-horizon professional workflows 24.8 9.0 19.0 23.8 26.9 28.6 25.4 31.7 MCPMark MCP tool use 67.7 45.7 51.2 48.8 72.4 66.9 74.0 65.3 BrowseComp Deep web research 72.8 44.4 77.1 83.5 -- 83.3 -- 84.7 WildClawBench In-the-wild agentic tasks 50.9 34.2 52.3 56.4 55.0 50.4 60.0 -- Coding Terminal-Bench 2.1 Agentic terminal use 70.2 53.9 55.1 65.2 77.9 80.9 75.7 80.5 SciCode Scientific coding 42.7 39.9 46.1 45.4 50.5 52.5 50.1 53.6 SWE-Atlas-QnA (strict) Repo-level code Q&A 48.4 -- 25.5 42.3 46.4 -- -- -- SWE Bench Pro (strict) Software engineering 42.6 38.7 43.1 43.8 46.7 48.8 -- -- Scientific Reasoning Humanity's Last Exam (without tools) Expert-level reasoning 32.0 28.4 31.9 39.0 41.1 39.5 38.5 41.3 GPQA Diamond Graduate-level science QA 87.3 86.7 87.2 92.9 89.5 91.1 89.6 91.1 CritPt Frontier physics reasoning 8.6 3.1 5.4 3.7 20.9 21.0 22.9 16.9 General AA-LCR Long-context reasoning 76.0 71.0 73.3 80.3
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
