DeepSeek-R1-0528
Version: 1
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Key capabilities
About this model
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model's accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.Key model capabilities
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528 |
|---|---|---|---|
| General | |||
| MMLU-Redux (EM) | 92.9 | 93.4 | |
| MMLU-Pro (EM) | 84.0 | 85.0 | |
| GPQA-Diamond (Pass@1) | 71.5 | 81.0 | |
| SimpleQA (Correct) | 30.1 | 27.8 | |
| FRAMES (Acc.) | 82.5 | 83.0 | |
| Humanity's Last Exam (Pass@1) | 8.5 | 17.7 | |
| Code | |||
| LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3 | |
| Codeforces-Div1 (Rating) | 1530 | 1930 | |
| SWE Verified (Resolved) | 49.2 | 57.6 | |
| Aider-Polyglot (Acc.) | 53.3 | 71.6 | |
| Math | |||
| AIME 2024 (Pass@1) | 79.8 | 91.4 | |
| AIME 2025 (Pass@1) | 70.0 | 87.5 | |
| HMMT 2025 (Pass@1) | 41.7 | 79.4 | |
| CNMO 2024 (Pass@1) | 78.8 | 86.9 | |
| Tools | |||
| BFCL_v3_MultiTurn (Acc) | - | 37.0 | |
| Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail) |
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic.Out of scope use cases
Microsoft and external researchers have found Deepseek R1 to be less aligned than other models -- meaning the model appears to have undergone less refinement designed to make its behavior and outputs more safe and appropriate for users -- resulting in (i) higher risks that the model will produce potentially harmful content and (ii) lower scores on safety and jailbreak benchmarks. We recommend customers use Azure AI Content Safety in conjunction with this model and conduct their own evaluations on production systems. The model's reasoning output (contained within the tags) may contain more harmful content than the model's final response. Consider how your application will use or display the reasoning output; you may want to suppress the reasoning output in a production setting. When deployed via Azure AI Foundry, prompts and completions are passed through a default configuration of Azure AI Content Safety classification models to detect and prevent the output of harmful content. Learn more about Azure AI Content Safety . Configuration options for content filtering vary when you deploy a model for production in Azure AI; learn more .Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of$0.6, a top-p value of 0.95, and generate 16 responses per query to estimate pass@1.
Training cut-off date
The provider has not supplied this information.Training time
The provider has not supplied this information.Input formats
The provider has not supplied this information.Output formats
The provider has not supplied this information.Supported languages
The provider has not supplied this information.Sample JSON response
The provider has not supplied this information.Model architecture
The provider has not supplied this information.Long context
For all our models, the maximum generation length is set to 64K tokens.Optimizing model performance
We recommend adhering to the following configurations when utilizing the DeepSeek-R1-0528 series models, including benchmarking, to achieve the expected performance:- Avoid adding a system prompt; all instructions should be contained within the user prompt.
- For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
- When evaluating model performance, it is recommended to conduct multiple tests and average the results.
Additional assets
Learn more: [original model announcement ]Extract from the original model evaluation
Training disclosure
Training, testing and validation
The provider has not supplied this information.Distribution
Distribution channels
The provider has not supplied this information.More information
The provider has not supplied this information.Responsible AI considerations
Safety techniques
Microsoft and external researchers have found Deepseek R1 to be less aligned than other models -- meaning the model appears to have undergone less refinement designed to make its behavior and outputs more safe and appropriate for users -- resulting in (i) higher risks that the model will produce potentially harmful content and (ii) lower scores on safety and jailbreak benchmarks. We recommend customers use Azure AI Content Safety in conjunction with this model and conduct their own evaluations on production systems. The model's reasoning output (contained within the tags) may contain more harmful content than the model's final response. Consider how your application will use or display the reasoning output; you may want to suppress the reasoning output in a production setting. When deployed via Azure AI Foundry, prompts and completions are passed through a default configuration of Azure AI Content Safety classification models to detect and prevent the output of harmful content. Learn more about Azure AI Content Safety . Configuration options for content filtering vary when you deploy a model for production in Azure AI; learn more .Safety evaluations
The provider has not supplied this information.Known limitations
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.Acceptable use
Acceptable use policy
The provider has not supplied this information.Quality and performance evaluations
Source: DeepSeek For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of$0.6, a top-p value of 0.95, and generate 16 responses per query to estimate pass@1.
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528 |
|---|---|---|---|
| General | |||
| MMLU-Redux (EM) | 92.9 | 93.4 | |
| MMLU-Pro (EM) | 84.0 | 85.0 | |
| GPQA-Diamond (Pass@1) | 71.5 | 81.0 | |
| SimpleQA (Correct) | 30.1 | 27.8 | |
| FRAMES (Acc.) | 82.5 | 83.0 | |
| Humanity's Last Exam (Pass@1) | 8.5 | 17.7 | |
| Code | |||
| LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3 | |
| Codeforces-Div1 (Rating) | 1530 | 1930 | |
| SWE Verified (Resolved) | 49.2 | 57.6 | |
| Aider-Polyglot (Acc.) | 53.3 | 71.6 | |
| Math | |||
| AIME 2024 (Pass@1) | 79.8 | 91.4 | |
| AIME 2025 (Pass@1) | 70.0 | 87.5 | |
| HMMT 2025 (Pass@1) | 41.7 | 79.4 | |
| CNMO 2024 (Pass@1) | 78.8 | 86.9 | |
| Tools | |||
| BFCL_v3_MultiTurn (Acc) | - | 37.0 | |
| Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail) |
Benchmarking methodology
Source: DeepSeek We recommend adhering to the following configurations when utilizing the DeepSeek-R1-0528 series models, including benchmarking, to achieve the expected performance:- Avoid adding a system prompt; all instructions should be contained within the user prompt.
- For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
- When evaluating model performance, it is recommended to conduct multiple tests and average the results.
Public data summary
Source: DeepSeek Microsoft and external researchers have found Deepseek R1 to be less aligned than other models -- meaning the model appears to have undergone less refinement designed to make its behavior and outputs more safe and appropriate for users -- resulting in (i) higher risks that the model will produce potentially harmful content and (ii) lower scores on safety and jailbreak benchmarks. We recommend customers use Azure AI Content Safety in conjunction with this model and conduct their own evaluations on production systems.Model Specifications
Context Length128000
Quality Index0.87
LicenseMit
Last UpdatedDecember 2025
Input TypeText
Output TypeText
ProviderDeepSeek
Languages2 Languages
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