Start with the current official model name
DeepSeek’s first-call documentation recommends deepseek-flash. It states that this alias is served by DeepSeek-V4.1-Flash-1017, while certain older Flash names are still accepted but map to the newer served model. Confirm the current documentation when you deploy.
A third-party catalog may use its own namespaced identifier. Copying a label from a blog into a different provider’s request is not sufficient: check that provider’s API contract and returned model metadata.
Decide what you want to evaluate
Choose one output task such as extracting fields from a document, writing a short answer from supplied notes or drafting a creative brief from an image. If your task uses images, verify the accepted input contract and keep the output expectation textual.
Make a small fixed set of representative examples with an expected answer or an explicit rubric. Include at least one ambiguous case and one case where the correct response should admit missing information. Avoid changing the benchmark prompts between model runs.
Use Jev for a focused comparison
Jev’s structured questions can score or classify the resulting text against a rubric you define. Keep the original input, the model answer and the grading criteria separate so an attractive response is not automatically treated as a correct one.
For example, ask whether an answer preserves all required facts, whether it invents an unsupported claim and how well it follows your requested format. Compare those outcomes with latency and your real provider bill rather than copying another site’s pricing table.
Keep model roles explicit
DeepSeek produces the answer being tested; Jev evaluates the supplied state and questions. An internal use of a model by a tool does not imply that the same model is a public generation endpoint on this website. Use the official DeepSeek access route or your documented provider for generation, and the Jev playground for supported evaluation tasks.