Cohere
The Cohere family of models includes various models optimized for different use cases, including rerank, chat completions, and embeddings models.Inference examples: Cohere command and embed
The following table provides links to examples of how to use Cohere models.| Description | Language | Sample |
|---|---|---|
| Web requests | Bash | Command-R Command-R+ cohere-embed.ipynb |
| Azure AI Inference package for C# | C# | Link |
| Azure AI Inference package for JavaScript | JavaScript | Link |
| Azure AI Inference package for Python | Python | Link |
| OpenAI SDK (experimental) | Python | Link |
| LangChain | Python | Link |
| Cohere SDK | Python | Command Embed |
| LiteLLM SDK | Python | Link |
Retrieval Augmented Generation (RAG) and tool use samples: Cohere command and embed
| Description | Packages | Sample |
|---|---|---|
| Create a local Facebook AI similarity search (FAISS) vector index, using Cohere embeddings - Langchain | langchain, langchain_cohere | cohere_faiss_langchain_embed.ipynb |
| Use Cohere Command R/R+ to answer questions from data in local FAISS vector index - Langchain | langchain, langchain_cohere | command_faiss_langchain.ipynb |
| Use Cohere Command R/R+ to answer questions from data in AI search vector index - Langchain | langchain, langchain_cohere | cohere-aisearch-langchain-rag.ipynb |
| Use Cohere Command R/R+ to answer questions from data in AI search vector index - Cohere SDK | cohere, azure_search_documents | cohere-aisearch-rag.ipynb |
| Command R+ tool/function calling, using LangChain | cohere, langchain, langchain_cohere | command_tools-langchain.ipynb |
Cohere rerank
To perform inferencing with Cohere rerank models, you’re required to use Cohere’s custom rerank APIs. For more information on the Cohere rerank model and its capabilities, see Cohere rerank.Pricing for Cohere rerank models
Queries, not to be confused with a user’s query, is a pricing meter that refers to the cost associated with the tokens used as input for inference of a Cohere Rerank model. Cohere counts a single search unit as a query with up to 100 documents to be ranked. Documents longer than 500 tokens (for Cohere-rerank-v3.5) or longer than 4096 tokens (for Cohere-rerank-v3-English and Cohere-rerank-v3-multilingual) when including the length of the search query are split up into multiple chunks, where each chunk counts as a single document. See the Cohere model collection in Azure AI Foundry portal.Core42
The following table provides links to examples of how to use Jais models.DeepSeek
DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks, DeepSeek-V3-0324, a Mixture-of-Experts (MoE) language model, and more. The following table provides links to examples of how to use DeepSeek models.Meta
Meta Llama models and tools are a collection of pretrained and fine-tuned generative AI text and image reasoning models. Meta models range is scale to include:- Small language models (SLMs) like 1B and 3B Base and Instruct models for on-device and edge inferencing
- Mid-size large language models (LLMs) like 7B, 8B, and 70B Base and Instruct models
- High-performant models like Meta Llama 3.1-405B Instruct for synthetic data generation and distillation use cases.
- High-performant natively multimodal models, Llama 4 Scout and Llama 4 Maverick, leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
Microsoft
Microsoft models include various model groups such as MAI models, Phi models, healthcare AI models, and more. To see all the available Microsoft models, view the Microsoft model collection in Azure AI Foundry portal. The following table provides links to examples of how to use Microsoft models.
See the Microsoft model collection in Azure AI Foundry portal.
Mistral AI
Mistral AI offers two categories of models, namely:- Premium models: These include Mistral Large, Mistral Small, Mistral-OCR-2503, Mistral Medium 3 (25.05), and Ministral 3B models, and are available as serverless APIs with pay-as-you-go token-based billing.
- Open models: These include Mistral-small-2503, Codestral, and Mistral Nemo (that are available as serverless APIs with pay-as-you-go token-based billing), and Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01(that are available to download and run on self-hosted managed endpoints).
| Description | Language | Sample |
|---|---|---|
| CURL request | Bash | Link |
| Azure AI Inference package for C# | C# | Link |
| Azure AI Inference package for JavaScript | JavaScript | Link |
| Azure AI Inference package for Python | Python | Link |
| Python web requests | Python | Link |
| OpenAI SDK (experimental) | Python | Mistral - OpenAI SDK sample |
| LangChain | Python | Mistral - LangChain sample |
| Mistral AI | Python | Mistral - Mistral AI sample |
| LiteLLM | Python | Mistral - LiteLLM sample |
Nixtla
Nixtla’s TimeGEN-1 is a generative pre-trained forecasting and anomaly detection model for time series data. TimeGEN-1 can produce accurate forecasts for new time series without training, using only historical values and exogenous covariates as inputs. To perform inferencing, TimeGEN-1 requires you to use Nixtla’s custom inference API. For more information on the TimeGEN-1 model and its capabilities, see Nixtla.Estimate the number of tokens needed
Before you create a TimeGEN-1 deployment, it’s useful to estimate the number of tokens that you plan to consume and be billed for. One token corresponds to one data point in your input dataset or output dataset. Suppose you have the following input time series dataset:| Unique_id | Timestamp | Target Variable | Exogenous Variable 1 | Exogenous Variable 2 |
|---|---|---|---|---|
| BE | 2016-10-22 00:00:00 | 70.00 | 49593.0 | 57253.0 |
| BE | 2016-10-22 01:00:00 | 37.10 | 46073.0 | 51887.0 |
| Unique_id | Timestamp | Forecasted Target Variable |
|---|---|---|
| BE | 2016-10-22 02:00:00 | 46.57 |
| BE | 2016-10-22 03:00:00 | 48.57 |
Estimate pricing based on tokens
There are four pricing meters that determine the price you pay. These meters are as follows:| Pricing Meter | Description |
|---|---|
| paygo-inference-input-tokens | Costs associated with the tokens used as input for inference when finetune_steps = 0 |
| paygo-inference-output-tokens | Costs associated with the tokens used as output for inference when finetune_steps = 0 |
| paygo-finetuned-model-inference-input-tokens | Costs associated with the tokens used as input for inference when finetune_steps > 0 |
| paygo-finetuned-model-inference-output-tokens | Costs associated with the tokens used as output for inference when finetune_steps > 0 |
Stability AI
Stability AI models deployed via serverless API deployment implement the Model Inference API on the route/image/generations.
For examples of how to use Stability AI models, see the following examples:
- Use OpenAI SDK with Stability AI models for text to image requests
- Use Requests library with Stability AI models for text to image requests
- Use Requests library with Stable Diffusion 3.5 Large for image to image requests
- Example of a fully encoded image generation response