The main differences in the context window offered by AWS and Azure for DeepSeek-R1 primarily revolve around the capabilities and limitations of the model itself, rather than differences in how AWS and Azure implement it. DeepSeek-R1 is a large language model that can handle up to 128,000 tokens in its input context window across both platforms. This capability makes it suitable for complex tasks such as code reviews, legal document analysis, or multi-step mathematical problem-solving[1][4][6].
In terms of output, DeepSeek-R1 can generate up to 32,000 tokens at once, which is consistent across both AWS and Azure implementations[4][6]. This high output capacity is beneficial for tasks requiring detailed responses, such as writing in-depth reports or analyzing large datasets.
The key distinction between AWS and Azure is not in the context window itself but in how the model is integrated and accessed. AWS offers DeepSeek-R1 as a fully managed serverless model in Amazon Bedrock, allowing users to access it via APIs like `InvokeModel` and `Converse`, which can be used through the AWS CLI or SDKs[2]. On the other hand, Azure provides DeepSeek-R1 through Azure AI Foundry, where it is part of a broader portfolio of AI models, offering tools for model evaluation and integration into enterprise workflows[3][9].
In summary, while the context window capabilities of DeepSeek-R1 are consistent across AWS and Azure, the platforms differ in their integration and access methods, reflecting their respective cloud service offerings and user interfaces.
Citations:
[1] https://futurework.blog/2025/01/30/deepseek-r1-at-azure-ai/
[2] https://aws.amazon.com/blogs/aws/deepseek-r1-now-available-as-a-fully-managed-serverless-model-in-amazon-bedrock/
[3] https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/tutorials/get-started-deepseek-r1?context=%2Fazure%2Fai-foundry%2Fcontext%2Fcontext
[4] https://docsbot.ai/models/compare/deepseek-r1/mistral-large
[5] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[6] https://www.popai.pro/templatesasset/resources/everything-about-deepseek/
[7] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx?admgarea=news
[8] https://huggingface.co/blog/deepseek-r1-aws
[9] https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github/