DeepSeek-V3.1
Version: 1
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Key capabilities
About this model
DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved. DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.Key model capabilities
- Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template.
- Smarter tool calling: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
- Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
The provider has not supplied this information.Out of scope use cases
Microsoft and external researchers have found Deepseek V3.1 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.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format on both model weights and activations to ensure compatibility with microscaling data formats. Please refer to DeepGEMM for more details.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
DeepSeek-V3.1 is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens.Optimizing model performance
The provider has not supplied this information.Additional assets
The provider has not supplied this information.Training disclosure
Training, testing and validation
We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format on both model weights and activations to ensure compatibility with microscaling data formats.Distribution
Distribution channels
The provider has not supplied this information.More information
Learn more: original model announcementContent filtering
When deployed via Microsoft 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 .Responsible AI considerations
Safety techniques
The provider has not supplied this information.Safety evaluations
Microsoft and external researchers have found Deepseek V3.1 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.Known limitations
The provider has not supplied this information.Acceptable use
Acceptable use policy
The provider has not supplied this information.Quality and performance evaluations
Source: DeepSeek| Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |
|---|---|---|---|---|---|
| General | |||||
| MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4 | |
| MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0 | |
| GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0 | |
| Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7 | |
| Search Agent | |||||
| BrowseComp | - | - | 30.0 | 8.9 | |
| BrowseComp_zh | - | - | 49.2 | 35.7 | |
| Humanity's Last Exam (Python + Search) | - | - | 29.8 | 24.8 | |
| SimpleQA | - | - | 93.4 | 92.3 | |
| Code | |||||
| LiveCodeBench (2408-2505) (Pass@1) | 56.4 | 43.0 | 74.8 | 73.3 | |
| Codeforces-Div1 (Rating) | - | - | 2091 | 1930 | |
| Aider-Polyglot (Acc.) | 68.4 | 55.1 | 76.3 | 71.6 | |
| Code Agent | |||||
| SWE Verified (Agent mode) | 66.0 | 45.4 | - | 44.6 | |
| SWE-bench Multilingual (Agent mode) | 54.5 | 29.3 | - | 30.5 | |
| Terminal-bench (Terminus 1 framework) | 31.3 | 13.3 | - | 5.7 | |
| Math | |||||
| AIME 2024 (Pass@1) | 66.3 | 59.4 | 93.1 | 91.4 | |
| AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5 | |
| HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 |
Benchmarking methodology
Source: DeepSeek The provider has not supplied this information.Public data summary
Source: DeepSeek Microsoft and external researchers have found Deepseek V3.1 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 Index0.85
LicenseCustom
Last UpdatedJanuary 2026
Input TypeText
Output TypeText
ProviderDeepSeek
Languages2 Languages
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