言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
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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基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
