Reasoned Extraction: DeepSeek v4 vs Frontier LLMs

About this course

You will learn a practical, browser-based workflow to evaluate how well different language models extract structured clinical judgments from real narrative cases. Instead of accepting a model’s answer at face value, you will inspect the extracted value, the model’s explanation, and the exact source citations used to support it.

We will compare DeepSeek v4 against closed-weight models using visual review tools and simple, repeatable checks. No programming, no notebook setup, and no API coding is required to complete this crash course.

To be transparent: the underlying system is code-based, and setting up or extending the full pipeline does require some technical implementation. In this course, you do not need to do that—you will learn the practical evaluation process through the UI and clear explanations of how comparison logic works.

If you want deeper implementation knowledge and if you have a specific domain or workflow in mind, contact the instructor directly and we can map this approach to your use case.

Why cost matters hereA major reason to compare models systematically is inference cost. DeepSeek v4–class models are often around 10× cheaper than the premium closed-weight APIs used as references in this course (OpenAI GPT, Anthropic Claude, Google Gemini Pro). At volume, that difference can be substantial.

This course does not present DeepSeek as perfect. The evaluation design uses three closed-weight models as the gold reference panel, and scores DeepSeek (or any candidate) as the test model against that panel. That reflects a practical judgment: the gold tier is the higher-trust baseline for comparison—not the lowest-cost option.

You will learn to measure the trade-off: where cheaper models match gold, where they diverge, and whether the gap is acceptable for your use case. Many teams can accept slightly lower alignment to gold when savings and throughput gains are large—but only if they measure the gap with citation-backed review, not

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