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 ↗DISCOVER & COMPARE
Explore AI models 84853
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 ↗TabSTAR-v1.1-cls-20261011
alana89TabSTAR-v2 classification checkpoint, epoch 17 of `cls50k-active-261004_233909`. Pretrained checkpoint; this file records how it was trained and how it differs from the original TabSTAR (v1). - **Task type**: classification only (separate model per task type) - **Pool**: decontaminated T4 pool `t4_tabstarv1.1_runs_decontamination`, 200,142 classification tables; 50,000 sampled per epoch (`--num_datasets 50000`, `--pool_mode active`) - **Learning rate**: 1e-4, **weight decay**: 1e-3, linear warmup over the first 10% of steps - **Epochs**: 25 planned, early-stopping patience 5 - **Batch size**: 512 global (micro-batch 32, no gradient accumulation) - **Text encoder**: `intfloat/e5-small-v2`, top 6 layers unfrozen, text batch size 1024 - **Finetunability check**: every epoch, 30 minute timeout per check; epoch 17 is among the top finetunability Elo epochs Downstream finetuning (TabArena-Lite) uses the repo defaults, none tuned per checkpoint:
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗TabSTAR-v1.1-reg-20261009
alana89TabSTAR-v2 regression checkpoint, epoch 16 of `reg50k-active-261004_233909`. Pretrained checkpoint; this file records how it was trained and how it differs from the original TabSTAR (v1). - **Task type**: regression only (separate model per task type) - **Pool**: decontaminated T4 pool `t4_tabstarv1.1_runs_decontamination`, 200,013 regression tables; 50,000 sampled per epoch (`--num_datasets 50000`, `--pool_mode active`) - **Learning rate**: 1e-4, **weight decay**: 1e-3, linear warmup over the first 10% of steps - **Epochs**: 25 planned, early-stopping patience 5 - **Batch size**: 512 global (micro-batch 32, no gradient accumulation) - **Text encoder**: `intfloat/e5-small-v2`, top 6 layers unfrozen, text batch size 1024 - **Regression head**: dual head, a scalar sub-head plus a distributional sub-head over 200 bins - **Finetunability check**: every epoch, 30 minute timeout per check; epoch 16 had the top finetunability Elo Downstream finetuning (TabArena-Lite) uses the repo defaults, none tuned per checkpoint:
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kol-cleanup-360m-coreml
alandotcom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 ↗tiny-sd3-modular
AlanPonnachanThis is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗ruT5_q_a
AlanRobotics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 ↗experimental_merge_241026_v1
alansmithee-cpu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 ↗experimental_merge_241026_v2
alansmithee-cpu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 ↗AlayaWorld
AlayaLab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 Video
Price not verifiedCheck multimodal and channel pricing separately
View details ↗AnyGameRenderer
AlayaLab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 ↗Evoke-Turbo
AlayaLab**Alaya-EVOKE-Turbo** is a **controllability-optimized** version of Alaya-EVOKE, focusing on camera-motion following and scene/subject instruction adherence. It uses the same architecture and inference workflow: **3 steps, no CFG, 384 × 640 at 24 fps**. Merged Turbo transformer weights, stored in **FP32** to match Evoke. Download the shared base components and depth backend separately: See Evoke for camera control, other inference modes, and long rollouts.
Input Text→ Output Video
Price not verifiedCheck multimodal and channel pricing separately
View details ↗GLM-5.3-DFlash2
AlayaNeW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 ↗GLM-5.3-DSpark
AlayaNeW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 ↗GLM-5.3-INT4-FP8Mix
AlayaNeW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 ↗D12_model
Albadri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 ↗DTS6_model
Albadri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 ↗gemma-3-sudoku-coach
alban12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 ↗DINOv3_mirror
AlbeRota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 ↗albert-base-v1
albertPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗albert-base-v2
albertPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗albert-large-v1
albert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 ↗albert-large-v2
albert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 ↗albert-xlarge-v1
albert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.