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 ↗DISCOVER & COMPARE
Explore AI models 2428
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。
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 ↗Mizan-v3-3B-GGUF
Nimruz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 ↗bert-base_claimbuster
Nithiwat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 ↗mdeberta-v3-base_claim-detection
Nithiwat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 ↗mdeberta-v3-base_claimbuster
Nithiwat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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗MixLID
nlpai-lab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 ↗ner-vietnamese-electra-base
NlpHUST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 ↗vi-word-segmentation
NlpHUST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 ↗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 ↗Visit the NLP Town website for an updated version of this model, with a 40% error reduction on product reviews. This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗nomic-embed-text-v1
nomic-ai`nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. **Exciting Update!**: `nomic-embed-text-v1` is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal! **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗nomic-embed-text-v1.5
nomic-ai**Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! nomic-embed-vision-v1.5 is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal! **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. For example, if you are implementing a RAG application, you embed your documents as `search_document: ` and embed your user queries as `search_query: `.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗nomic-embed-text-v2-moe
nomic-aiThis model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. `nomic-embed-text-v2-moe` is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval: - **High Performance**: SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size - **Multilinguality**: Supports ~100 languages and trained on over 1.6B pairs - **Flexible Embedding Dimension**: Trained with Matryoshka Embeddings with 3x reductions in storage cost with minimal performance degradations - **Fully Open-Source**: Model weights, code, and training data (see code repo) released
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗armiger-ultra
NorviTech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 ↗
Minos-v1
NousResearch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 ↗nerkor-cars-onpp-hubert
novakatThe underlying corpus, NerKor+CARS-OntoNotes++, was derived from NYTK-NerKor, a Hungarian gold standard named entity annotated corpus containing about 1 million tokens. It includes a small addition of 12k tokens of text (individual sentences) concerning motor vehicles (cars, buses, motorcycles) from the news archive of hvg.hu. While the annotation in NYTK-NerKor followed the CoNLL2002 labelling standard with just four NE categories (`PER`, `LOC`, `MISC`, `ORG`), this version of the corpus features over 30 entity types, including all entity types used in the [OntoNotes 5.0] English NER annotation. The new annotation elaborates on subtypes of the `LOC` and `MISC` entity types, and includes annotation for non-names like times and dates, quantities, languages and nationalities or religious or political groups. The annotation was elaborated with further entity subtypes not present in the Ontonotes 5 annotation (see below).
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗mmarco-mMiniLMv2-L12-H384-v1
nreimers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 ↗mmarco-mMiniLMv2-L6-H384-v1
nreimers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 ↗local_politics
nruigrok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.