ShareGPT4V-7B
Lin-Chen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。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 85001
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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗MiniMax-H3
lin929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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗animagine-xl
Linaqruf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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗anime-detailer-xl-lora
Linaqruf.title-container { display: flex; flex-direction: column; justify-content: center; align-items: center; height: 100vh; background-color: #f5f5f5; } .title { font-size: 3em; text-align: center; color: #333; text-transform: uppercase; padding: 0.5em; font-weight: bold; } .title span { background: -webkit-linear-gradient(45deg, #ff9a9e, #fad0c4, #f6d365); -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗hitokomoru-diffusion
Linaqruf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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pastel-anime-xl-lora
Linaqruf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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗style-enhancer-xl-lora
Linaqruf.title-container { display: flex; flex-direction: column; /* Allow vertical stacking of title and subtitle */ justify-content: center; align-items: center; height: 100vh; background-color: #f5f5f5; } .title { font-size: 2em; text-align: center; color: #333; font-family: 'Verdana', sans-serif; text-transform: uppercase; padding: 1em; box-shadow: 0px 0px 0px rgba(0,0,0,0.1); } .title span { background: -webkit-linear-gradient(45deg, #ff9a9e, #fad0c4, #f6d365); -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗clip-japanese-base
line-corporationThis is a Japanese CLIP (Contrastive Language-Image Pre-training) model developed by LY Corporation. This model was trained on ~1B web-collected image-text pairs, and it is applicable to various visual tasks including zero-shot image classification, text-to-image or image-to-text retrieval. The model uses an Eva02-B Transformer architecture as the image encoder and a 12-layer BERT as the text encoder. The text encoder was initialized from rinna/japanese-clip-vit-b-16. - STAIR Captions (v2014 val set of MSCOCO) for image-to-text (i2t) and text-to-image (t2i) retrieval. We measure performance using R@1, which is the average recall of i2t and t2i retrieval. - Recruit Datasets for image classification. - ImageNet-1K for image classification. We translated all classnames into Japanese. The classnames and templates can be found in ja-imagenet-1k-classnames.txt and ja-imagenet-1k-templates.txt.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗clip-japanese-base-v2
line-corporationThis is a Japanese CLIP (Contrastive Language-Image Pre-training) model developed by LY Corporation. This model is an updated version of line-corporation/clip-japanese-base. It increases the training data to approximately ~2B image–text pairs and applies model distillation to improve overall performance. The model uses an Eva02-B Transformer architecture as the image encoder and a 12-layer BERT as the text encoder. The text encoder was initialized from rinna/japanese-clip-vit-b-16.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-large-lm-1.7b
line-corporation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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-large-lm-3.6b
line-corporation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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-large-lm-3.6b-instruction-sft
line-corporation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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗line-distilbert-base-japanese
line-corporationThis is a DistilBERT model pre-trained on 131 GB of Japanese web text. The teacher model is BERT-base that built in-house at LINE. The model was trained by LINE Corporation. The model architecture is the DitilBERT base model; 6 layers, 768 dimensions of hidden states, 12 attention heads, 66M parameters. The texts are first tokenized by MeCab with the Unidic dictionary and then split into subwords by the SentencePiece algorithm. The vocabulary size is 32768.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Gated-Deltanet-1.3B
linear-moe-hub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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗whisper-small-sinhala
Lingalingeswaran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。
Basic profile indexed · More details to follow
Input Audio→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗ganga-1b
LingoIITGN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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗qwen-indic-v1
LingoIITGN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。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Lingshu-32B
lingshu-medical-mllm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。
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Lingshu-7B
lingshu-medical-mllmWebsite 🤖 7B Model 🤖 32B Model MedEvalKit Technical Report Lingshu MCP This repository contains the model of the paper Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. We also release a comprehensive medical evaluation toolkit in MedEvalKit, which supports fast evaluation of major multimodal and textual medical tasks. > **Disclaimer**: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Lingshu-I-8B
lingshu-medical-mllm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。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.