fix link warnings and cross-link

Author: msakande

articles/ai-foundry/concepts/models-inference-examples.md

@@ -7,7 +7,7 @@ ms.author: mopeakande
 manager: scottpolly
 reviewer: santiagxf
 ms.reviewer: fasantia
-ms.date: 07/10/2025
+ms.date: 07/11/2025
 ms.service: azure-ai-foundry
 ms.topic: concept-article
 ms.custom:
@@ -57,7 +57,7 @@ The following table provides links to examples of how to use Cohere models.
 
 ### Cohere rerank
 
-To perform inferencing with Cohere rerank models, you're required to use Cohere's custom rerank APIs. For more information, see the table for [Other Foundry Models available for serverless API deployment](../foundry-models/concepts/models.md#other-foundry-models-available-for-serverless-api-deployment).
+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](../foundry-models/concepts/models.md#cohere-rerank).
 
 
 #### Pricing for Cohere rerank models
@@ -163,7 +163,7 @@ The following table provides links to examples of how to use Mistral models.
 
 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.
+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](../foundry-models/concepts/models.md#nixtla).
 
 #### Estimate the number of tokens needed
 

articles/ai-foundry/foundry-models/concepts/models.md

@@ -134,6 +134,7 @@ The Cohere family of models includes various models optimized for different use
 | ------ | ---- | ------------ | ------------- | ------------ |
 | [Cohere-rerank-v3.5](https://ai.azure.com/explore/models/Cohere-rerank-v3.5/version/1/registry/azureml-cohere) | rerank <br> text classification | - **Input:** text <br /> - **Output:** text  <br /> - **Languages:** English, Chinese, French, German, Indonesian, Italian, Portuguese, Russian, Spanish, Arabic, Dutch, Hindi, Japanese, Vietnamese | [Cohere's v2/rerank API](https://docs.cohere.com/v2/reference/rerank) | Hub-based |
 
+For more details on pricing for Cohere rerank models, see [Pricing for Cohere rerank models](../../concepts/models-inference-examples.md#pricing-for-cohere-rerank-models).
 
 See [the Cohere model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=cohere).
 
@@ -161,7 +162,7 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 | [Llama-3.2-90B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-90B-Vision-Instruct/version/1/registry/azureml-meta) | chat-completion | - **Input:** text and image (128,000 tokens) <br /> - **Output:**  (8,192 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** No* <br /> - **Response formats:** Text | Foundry, Hub-based |
 | [Meta-Llama-3.1-405B-Instruct](https://ai.azure.com/explore/models/Meta-Llama-3.1-405B-Instruct/version/1/registry/azureml-meta) | chat-completion | - **Input:** text (131,072 tokens) <br /> - **Output:**  (8,192 tokens) <br /> - **Languages:** `en`, `de`, `fr`, `it`, `pt`, `hi`, `es`, and `th` <br />  - **Tool calling:** No* <br /> - **Response formats:** Text | Foundry, Hub-based |
 | [Meta-Llama-3.1-8B-Instruct](https://ai.azure.com/explore/models/Meta-Llama-3.1-8B-Instruct/version/4/registry/azureml-meta) | chat-completion | - **Input:** text (131,072 tokens) <br /> - **Output:**  (8,192 tokens) <br /> - **Languages:** `en`, `de`, `fr`, `it`, `pt`, `hi`, `es`, and `th` <br />  - **Tool calling:** No* <br /> - **Response formats:** Text | Foundry, Hub-based |
-| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
+| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | chat-completion | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=meta). There are also several Meta models available as [models sold directly by Azure](#meta-models-sold-directly-by-azure).
 
@@ -174,8 +175,8 @@ Microsoft models include various model groups such as MAI models, Phi models, he
 | [Phi-4-mini-instruct](https://ai.azure.com/explore/models/Phi-4-mini-instruct/version/1/registry/azureml) | chat-completion | - **Input:** text (131,072 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** `ar`, `zh`, `cs`, `da`, `nl`, `en`, `fi`, `fr`, `de`, `he`, `hu`, `it`, `ja`, `ko`, `no`, `pl`, `pt`, `ru`, `es`, `sv`, `th`, `tr`, and `uk` <br />  - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
 | [Phi-4-multimodal-instruct](https://ai.azure.com/explore/models/Phi-4-multimodal-instruct/version/1/registry/azureml) | chat-completion | - **Input:** text, images, and audio (131,072 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** `ar`, `zh`, `cs`, `da`, `nl`, `en`, `fi`, `fr`, `de`, `he`, `hu`, `it`, `ja`, `ko`, `no`, `pl`, `pt`, `ru`, `es`, `sv`, `th`, `tr`, and `uk` <br />  - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
 | [Phi-4](https://ai.azure.com/explore/models/Phi-4/version/2/registry/azureml) | chat-completion | - **Input:** text (16,384 tokens) <br /> - **Output:**  (16,384 tokens) <br /> - **Languages:** `en`, `ar`, `bn`, `cs`, `da`, `de`, `el`, `es`, `fa`, `fi`, `fr`, `gu`, `ha`, `he`, `hi`, `hu`, `id`, `it`, `ja`, `jv`, `kn`, `ko`, `ml`, `mr`, `nl`, `no`, `or`, `pa`, `pl`, `ps`, `pt`, `ro`, `ru`, `sv`, `sw`, `ta`, `te`, `th`, `tl`, `tr`, `uk`, `ur`, `vi`, `yo`, and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
-| [Phi-4-reasoning](https://aka.ms/azureai/landing/Phi-4-reasoning) | [chat-completion with reasoning content](../model-inference/how-to/use-chat-reasoning.md?context=/azure/ai-foundry/context/context) | - **Input:** text (32,768 tokens) <br /> - **Output:** text (32,768 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
-| [Phi-4-mini-reasoning](https://aka.ms/azureai/landing/Phi-4-mini-reasoning) | [chat-completion with reasoning content](../model-inference/how-to/use-chat-reasoning.md?context=/azure/ai-foundry/context/context) | - **Input:** text (128,000 tokens) <br /> - **Output:** text (128,000 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
+| [Phi-4-reasoning](https://aka.ms/azureai/landing/Phi-4-reasoning) | chat-completion with reasoning content | - **Input:** text (32,768 tokens) <br /> - **Output:** text (32,768 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
+| [Phi-4-mini-reasoning](https://aka.ms/azureai/landing/Phi-4-mini-reasoning) | chat-completion with reasoning content | - **Input:** text (128,000 tokens) <br /> - **Output:** text (128,000 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** No <br /> - **Response formats:** Text | Foundry, Hub-based |
 
 See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).  There are also several Microsoft models available as [models sold directly by Azure](#microsoft-models-sold-directly-by-azure).
 
@@ -210,6 +211,9 @@ To perform inferencing, TimeGEN-1 requires you to use Nixtla's custom inference
 | ------ | ---- | --- | ------------ | ------------ |
 | [TimeGEN-1](https://ai.azure.com/explore/models/TimeGEN-1/version/1/registry/azureml-nixtla) | Forecasting  | - **Input:** Time series data as JSON or dataframes (with support for multivariate input)  <br /> - **Output:**  Time series data as JSON <br /> - **Tool calling:** No <br /> - **Response formats:** JSON  | [Forecast client to interact with Nixtla's API](https://nixtlaverse.nixtla.io/nixtla/docs/reference/nixtla_client.html#nixtlaclient-forecast) | Hub-based |
 
+For more details on pricing for Nixtla models, see [Nixtla](../../concepts/models-inference-examples.md#nixtla).
+
+
 ### NTT Data
 
 **tsuzumi** is an autoregressive language optimized transformer. The tuned versions use supervised fine-tuning (SFT). tsuzumi handles both Japanese and English language with high efficiency.

articles/ai-foundry/foundry-models/overview.md

@@ -26,7 +26,6 @@ AI Foundry Models is part of Azure AI Foundry, and users can access the service
 
 You can get access to the key model providers in the industry. Explore the following model families available:
 
-- [AI21 Labs](concepts/models.md#ai21-labs)
 - [Azure OpenAI](concepts/models.md#azure-openai)
 - [Cohere](concepts/models.md#cohere)
 - [Core42](concepts/models.md#core42)