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Co-authored-by: Océane <[email protected]>
Co-authored-by: Néda <[email protected]>

Author: 3 people

pages/generative-apis/how-to/query-reasoning-models.mdx

@@ -8,9 +8,9 @@ dates:
 ---
 import Requirements from '@macros/iam/requirements.mdx'
 
-Scaleway's Generative APIs service allows users to interact with language models benefitting from additional reasoning capabilities.
+Scaleway's Generative APIs service allows users to interact with language models benefiting from additional reasoning capabilities.
 
-A reasoning model is a language model that is capable of carrying out multiple inference steps and systematically verifying intermediate results before producing answers. You can specify how much effort it should put into reasoning via dedicated parameters, and access reasoning content in its outputs. Even with default parameters, such models are designed to perform better on reasoning tasks like maths and logic problems, than non-reasoning language models. 
+A reasoning model is a language model that is capable of carrying out multiple inference steps and systematically verifying intermediate results before producing answers. You can specify how much effort it should put into reasoning via dedicated parameters, and access reasoning content in its outputs. Even with default parameters, such models are designed to perform better on reasoning tasks like maths and logic problems than non-reasoning language models. 
 
 Language models supporting the reasoning feature include `gpt-oss-120b`. See [Supported Models](/generative-apis/reference-content/supported-models/) for a full list.
 
@@ -45,7 +45,7 @@ The web playground displays.
 4. Click **View code** to get code snippets configured according to your settings in the playground.
 
 <Message type="note">
-You cannot currently set values for parameters such as `reasoning_effort`, or access reasoning metadata in the model's output, via the console playground. Query the models programatically as shown below in order to access the full reasoning featureset.
+You cannot currently set values for parameters such as `reasoning_effort`, or access reasoning metadata in the model's output, via the console playground. Query the models programmatically as shown below in order to access the full reasoning feature set.
 </Message>
 
 ## Querying reasoning language models via API
@@ -57,7 +57,7 @@ In the example that follows, we will use the OpenAI Python client.
 
 Both the [Chat Completions API](/https://www.scaleway.com/en/developers/api/generative-apis/#path-chat-completions-create-a-chat-completion) and the [Responses API](https://www.scaleway.com/en/developers/api/generative-apis/#path-chat-completions-create-a-chat-completion) allow you to access and control reasoning for supported models. Scaleway's support of the Responses API is currently in beta.
 
-Note however, that the Responses API was introduced in part to better support features for reasoning workflows, among other tasks. It provides richer support for reasoning than Chat Completions, for example by providing chain-of-thought reasoning content in its responses.
+Note however, that the Responses API was introduced in part to better support features for reasoning workflows, among other tasks. It provides richer support for reasoning than Chat Completions, for example, by providing chain-of-thought reasoning content in its responses.
 
 For more information on Chat Completions versus Responses API, see the information provided in the [querying language models](/generative-apis/how-to/query-language-models/#chat-completions-api-or-responses-api) documentation.
 
@@ -139,7 +139,7 @@ You can now create a chat completion with reasoning, using either the Chat Compl
     This code sends a message to the model, as well as specifying the effort to make with reasoning, and returns an answer based on your input. The model's reasoning metadata can be accessed as well as its answer, with outputs such as:
 
     ```python
-    Reasoning: The user asks: "Briefly describe a futuristic city with advanced technology and green energy solutions." They want a brief description. Should be concise but vivid. Provide details: architecture, transport, energy, AI, sustainability. Probably a paragraph or a few sentences. Ensure it's brief. Let's produce a short description.
+    Reasoning: The user asks: "Briefly describe a futuristic city with advanced technology and green energy solutions." They want a brief description. Should be concise but vivid. Provide details: architecture, transport, energy, AI, and sustainability. Probably a paragraph or a few sentences. Ensure it's brief. Let's produce a short description.
 
     Answer: **Solaris Arcadia** rises from a reclaimed river delta, its skyline a lattice of translucent, self‑healing bioglass towers that
     ```
@@ -154,12 +154,12 @@ Some legacy models such as `deepseek-r1-distill-llama-70b` do not output reasoni
 response.content = "<think> The user asks for questions about mathematics (...) </think>  Answer is 42."
 ```
 
-The reasoning content is inside the `<think>`...`</think>` tags, and you can parse the response accordingly to access such content. There is, however, a known bug which can lead to the model to omit the opening `<think>` tag, so we suggest taking care when parsing such outputs.
+The reasoning content is inside the `<think>`...`</think>` tags, and you can parse the response accordingly to access such content. There is, however, a known bug that can lead the model to omit the opening `<think>` tag, so we suggest taking care when parsing such outputs.
 
 Note that the `reasoning_effort` parameter is not available for this model.
 
 
 ## Impact on token generation
 
-Reasoning models generate reasoning tokens, which are billable. Generally these are in the model's output as part of the reasoning content. To limit generation of reasoning tokens, you can adjust settings for the **reasoning effort** and **max completion/output tokens** parameters. Alternatively, use a non-reasoning model to avoid generation of reasoning tokens and subsequent billing.
+Reasoning models generate reasoning tokens, which are billable. Generally these are in the model's output as part of the reasoning content. To limit the generation of reasoning tokens, you can adjust settings for the **reasoning effort** and **max completion/output tokens** parameters. Alternatively, use a non-reasoning model to avoid the generation of reasoning tokens and subsequent billing.