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In March, Amazon Internet Companies (AWS) grew to become the first cloud service supplier to ship DeepSeek-R1 in a serverless manner by launching it as a completely managed, typically out there mannequin in Amazon Bedrock. Since then, prospects have used DeepSeek-R1’s capabilities by Amazon Bedrock to construct generative AI purposes, benefiting from the Bedrock’s strong guardrails and complete tooling for protected AI deployment.
At the moment, I’m excited to announce DeepSeek-V3.1 is now out there as a completely managed basis mannequin in Amazon Bedrock. DeepSeek-V3.1 is a hybrid open weight mannequin that switches between considering mode (chain-of-thought reasoning) for detailed step-by-step evaluation and non-thinking mode (direct solutions) for sooner responses.
In response to DeepSeek, the considering mode of DeepSeek-V3.1 achieves comparable reply high quality with higher outcomes, stronger multi-step reasoning for advanced search duties, and massive features in considering effectivity in contrast with DeepSeek-R1-0528.
| Benchmarks | DeepSeek-V3.1 | DeepSeek-R1-0528 |
|---|---|---|
| Browsecomp | 30.0 | 8.9 |
| Browsecomp_zh | 49.2 | 35.7 |
| HLE | 29.8 | 24.8 |
| xbench-DeepSearch | 71.2 | 55.0 |
| Frames | 83.7 | 82.0 |
| SimpleQA | 93.4 | 92.3 |
| Seal0 | 42.6 | 29.7 |
| SWE-bench Verified | 66.0 | 44.6 |
| SWE-bench Multilingual | 54.5 | 30.5 |
| Terminal-Bench | 31.3 | 5.7 |
DeepSeek-V3.1 mannequin efficiency in device utilization and agent duties has considerably improved by post-training optimization in comparison with earlier DeepSeek fashions. DeepSeek-V3.1 additionally helps over 100 languages with near-native proficiency, together with considerably improved functionality in low-resource languages missing massive monolingual or parallel corpora. You may construct international purposes to ship enhanced accuracy and lowered hallucinations in comparison with earlier DeepSeek fashions, whereas sustaining visibility into its decision-making course of.
Listed here are your key use instances utilizing this mannequin:
- Code era – DeepSeek-V3.1 excels in coding duties with enhancements in software program engineering benchmarks and code agent capabilities, making it excellent for automated code era, debugging, and software program engineering workflows. It performs properly on coding benchmarks whereas delivering high-quality outcomes effectively.
- Agentic AI instruments – The mannequin options enhanced device calling by post-training optimization, making it robust in device utilization and agentic workflows. It helps structured device calling, code brokers, and search brokers, positioning it as a stable alternative for constructing autonomous AI programs.
- Enterprise purposes – DeepSeek fashions are built-in into varied chat platforms and productiveness instruments, enhancing consumer interactions and supporting customer support workflows. The mannequin’s multilingual capabilities and cultural sensitivity make it appropriate for international enterprise purposes.
As I discussed in my earlier put up, when implementing publicly out there fashions, give cautious consideration to knowledge privateness necessities when implementing in your manufacturing environments, examine for bias in output, and monitor your outcomes when it comes to knowledge safety, accountable AI, and mannequin analysis.
You may entry the enterprise-grade safety features of Amazon Bedrock and implement safeguards personalized to your software necessities and accountable AI insurance policies with Amazon Bedrock Guardrails. You too can consider and evaluate fashions to establish the optimum mannequin to your use instances through the use of Amazon Bedrock mannequin analysis instruments.
Get began with the DeepSeek-V3.1 mannequin in Amazon Bedrock
When you’re new to utilizing the DeepSeek-V3.1 mannequin, go to the Amazon Bedrock console, select Mannequin entry beneath Bedrock configurations within the left navigation pane. To entry the absolutely managed DeepSeek-V3.1 mannequin, request entry for DeepSeek-V3.1 within the DeepSeek part. You’ll then be granted entry to the mannequin in Amazon Bedrock.
Subsequent, to check the DeepSeek-V3.1 mannequin in Amazon Bedrock, select Chat/Textual content beneath Playgrounds within the left menu pane. Then select Choose mannequin within the higher left, and choose DeepSeek because the class and DeepSeek-V3.1 because the mannequin. Then select Apply.
Utilizing the chosen DeepSeek-V3.1 mannequin, I run the next immediate instance about technical structure choice.
Define the high-level structure for a scalable URL shortener service like bit.ly. Talk about key parts like API design, database alternative (SQL vs. NoSQL), how the redirect mechanism works, and the way you'd generate distinctive brief codes.
You may flip the considering on and off by toggling Mannequin reasoning mode to generate a response’s chain of thought previous to the ultimate conclusion.
You too can entry the mannequin utilizing the AWS Command Line Interface (AWS CLI) and AWS SDK. This mannequin helps each the InvokeModel and Converse API. You may try a broad vary of code examples for a number of use instances and a wide range of programming languages.
To study extra, go to DeepSeek mannequin inference parameters and responses within the AWS documentation.
Now out there
DeepSeek-V3.1 is now out there within the US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (London), and Europe (Stockholm) AWS Areas. Examine the full Area record for future updates. To study extra, try the DeepSeek in Amazon Bedrock product web page and the Amazon Bedrock pricing web page.
Give the DeepSeek-V3.1 mannequin a attempt within the Amazon Bedrock console right now and ship suggestions to AWS re:Submit for Amazon Bedrock or by your regular AWS Help contacts.
— Channy
