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text-generation

automatic-speech-recognition

zero-shot-image-classification

text-generation

google/gemma-3-12b-it cover image

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

$0.05 in, $0.15 out / 1M

text-generation

google/gemma-3-27b-it cover image

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

$0.08 in, $0.16 out / 1M

text-generation

google/gemma-3-4b-it cover image

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

$0.05 in, $0.10 out / 1M

text-generation

gemma-4-31B-it-turbo

google/gemma-4-31B-it-turbo cover image

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

$0.05 cached, $0.09 in, $0.34 out / 1M

text-generation

google/gemma-4-E4B-it cover image

$0.02 in, $0.10 out / 1M

hexgrad/Kokoro-82M cover image

Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.

$0.62 per 1M characters

intfloat/e5-base-v2 cover image

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Model has 24 layers and 1024 out dim.

$0.005 / 1M tokens

intfloat/e5-large-v2 cover image

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Model has 24 layers and 1024 out dim.

$0.010 / 1M tokens

multilingual-e5-large

intfloat/multilingual-e5-large cover image

The Multilingual-E5-large model is a 24-layer text embedding model with an embedding size of 1024, trained on a mixture of multilingual datasets and supporting 100 languages.

$0.010 / 1M tokens

multilingual-e5-large-instruct

intfloat/multilingual-e5-large-instruct cover image

The Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:” or “passage:”, as the model was explicitly trained with this format.

$0.010 / 1M tokens

text-generation

Llama-4-Maverick-17B-128E-Instruct-FP8

meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 cover image

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Maverick, a 17 billion parameter model with 128 experts

$0.20 in, $0.80 out / 1M

text-generation

Llama-4-Scout-17B-16E-Instruct

meta-llama/Llama-4-Scout-17B-16E-Instruct cover image

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Scout, a 17 billion parameter model with 16 experts

$0.10 in, $0.30 out / 1M

text-generation

Llama-Guard-4-12B

meta-llama/Llama-Guard-4-12B cover image

Llama Guard 4 is a natively multimodal safety classifier with 12 billion parameters trained jointly on text and multiple images. Llama Guard 4 is a dense architecture pruned from the Llama 4 Scout pre-trained model and fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It itself acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

$0.18 / 1M tokens

text-generation

Mistral-Small-3.2-24B-Instruct-2506

mistralai/Mistral-Small-3.2-24B-Instruct-2506 cover image

Mistral-Small-3.2-24B-Instruct is a drop-in upgrade over the 3.1 release, with markedly better instruction following, roughly half the infinite-generation errors, and a more robust function-calling interface—while otherwise matching or slightly improving on all previous text and vision benchmarks.

$0.075 in, $0.20 out / 1M

text-generation

Nemotron-3-Nano-30B-A3B

nvidia/Nemotron-3-Nano-30B-A3B cover image

NVIDIA Nemotron 3 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compute cost while maintaining strong reasoning quality.

$0.025 cached, $0.05 in, $0.20 out / 1M

text-generation

Nemotron-Content-Safety-3.5

nvidia/Nemotron-Content-Safety-3.5 cover image

Nemotron Content Safety 3.5 is a multimodal safety classifier developed by NVIDIA. A compact safety model that handles text, images, and custom policies. It outputs a safe/unsafe classification plus a reasoning trace, and can be used as an inference-time guardrail, as a judge for LLM safety testing and evaluation, or with the accompanying training dataset to post-train models for safer behavior.

$0.20 / 1M tokens

llama-nemotron-embed-vl-1b-v2

nvidia/llama-nemotron-embed-vl-1b-v2 cover image

The llama-nemotron-embed-vl-1b-v2 is a high-performance multimodal embedding model designed to transform text queries and document images into dense vector representations for advanced retrieval systems. It excels at understanding complex visual content like charts, tables, and infographics.

$0.010 / 1M tokens

llama-nemotron-rerank-vl-1b-v2

nvidia/llama-nemotron-rerank-vl-1b-v2 cover image

The llama-nemotron-rerank-vl-1b-v2 is a 1.7B parameter multimodal reranking model designed to evaluate and order the relevance of document images and text against specific user queries. It excels at understanding complex visual content like charts, tables, and infographics.

$0.010 / 1M tokens

text-generation

openai/gpt-oss-20b cover image

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

$0.03 in, $0.14 out / 1M

sentence-transformers/

all-MiniLM-L12-v2

sentence-transformers/all-MiniLM-L12-v2 cover image

We present a sentence transformation model that generates semantically similar sentences. Our model is based on the Sentence-Transformers architecture and was trained on a large dataset of sentence pairs. We evaluate the effectiveness of our model by measuring its ability to generate similar sentences that are close to the original sentence in meaning.

$0.005 / 1M tokens

sentence-transformers/

all-MiniLM-L6-v2

sentence-transformers/all-MiniLM-L6-v2 cover image

We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.

$0.005 / 1M tokens

sentence-transformers/

all-mpnet-base-v2

sentence-transformers/all-mpnet-base-v2 cover image

A sentence transformation model that has been trained on a wide range of datasets, including but not limited to S2ORC, WikiAnwers, PAQ, Stack Exchange, and Yahoo! Answers. Our model can be used for various NLP tasks such as clustering, sentiment analysis, and question answering.

$0.005 / 1M tokens

sentence-transformers/

sentence-transformers/clip-ViT-B-32 cover image

The CLIP model maps text and images to a shared vector space, enabling various applications such as image search, zero-shot image classification, and image clustering. The model can be used easily after installation, and its performance is demonstrated through zero-shot ImageNet validation set accuracy scores. Multilingual versions of the model are also available for 50+ languages.

$0.005 / 1M tokens

sentence-transformers/

clip-ViT-B-32-multilingual-v1

sentence-transformers/clip-ViT-B-32-multilingual-v1 cover image

This model is a multilingual version of the OpenAI CLIP-ViT-B32 model, which maps text and images to a common dense vector space. It includes a text embedding model that works for 50+ languages and an image encoder from CLIP. The model was trained using Multilingual Knowledge Distillation, where a multilingual DistilBERT model was trained as a student model to align the vector space of the original CLIP image encoder across many languages.

$0.005 / 1M tokens

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