- Developers & Engineers: Integrate state-of-the-art reasoning and function calling in chatbots, code assistants, and automation pipelines.
- Researchers & Benchmarkers: Study large-token chain-of-thought behaviors and compare performance on open-source models.
- Enterprises & API Users: Deploy via DeepSeek’s API or private instances for production systems.
- Tool Makers: Utilize JSON output & function calling to build structured workflows.
- Open-Source Advocates: Use permissively licensed weights for fine-tuning or distillation.
How to Use DeepSeek R1 0528?
- Via API or Chat Website: Continue using the DeepSeek-R1 endpoint; “DeepThink” mode activates deeper reasoning.
- Download Weights: Available under MIT license on Hugging Face (`deepseek-ai/DeepSeek-R1-0528`).
- Call Features: Supports JSON output, function calling, context caching, and multi-round conversation.
- Performance Settings: Uses temperature
0.6, top_p 0.95, max gen length64K tokens. - Scale Up: Use distilled Qwen3-8B variant for lighter deployment with similar reasoning behavior.
- Benchmarks Leap: MMLU+
1 pt, GPQA Diamond jumps from71.5% to81.0%, AIME accuracy87.5%. - Deeper Reasoning Chains: Now uses ~23K tokens per reasoning step—nearly double previous depth.
- Structured Output & Functions: Supports JSON, function calling, and persistent context caching for structured applications.
- Open-Source Release: Fully MIT licensed and available for private deployment or fine-tuning.
- Significant boosts in reasoning and coding benchmarks
- Supports function calling and structured JSON output
- Reduced hallucination rates in complex tasks
- “DeepThink” mode enables deeper thought chains
- Fully open-source weights under permissive license
- Increased compute and memory demands due to deeper reasoning
- Still slightly behind OpenAI's o4‑mini in some coding benchmarks
- Distilled variants (Qwen3‑8B) may not fully match full model across all tasks
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Editorial Note
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