In this article
V4.1 uses numeric effort and changed DSML boundaries, so a valid JSON request can still encode the wrong prompt format.
DeepSeek-V4.1 supplies a prompt-format reference because its text conversation format has model-specific rules. Sending familiar message objects is only the first step; those objects must be encoded into the intended sequence.
Use the release’s own encoding reference
The 1017 model card points to standalone Python encoding and a maintained toolkit. The encoding README documents text conversations, tool calls, thinking modes and numeric reasoning effort without requiring the inference implementation.
This article covers text only. The same repository also documents media handling, but image examples should not be mixed into a minimal text-format check. Begin with a system instruction and a short user question whose final answer is easy to recognize.
Understand numeric effort at conversation start
The reference accepts reasoning_effort as an integer from 1 to 100. Its aliases map low to 50, high to 75 and max to 100, with high as the default. The prefix is rendered only in thinking mode and at the start of the conversation.
These values describe the reference encoding, not a universal API convention. Appending an effort instruction to a later user message is not equivalent to the encoder’s initial effort prefix. Inspect the rendered prompt rather than assuming two request forms are interchangeable.
Notice the changed text boundaries
| Format detail | V4.1 consequence |
|---|---|
| DSML tag names | A leading space appears before calls, invoke and parameter |
| Numeric effort | The prefix contains an explicit numeric budget |
| Mid-conversation system | A dedicated System token represents the message |
| Chat mode | The assistant prefix immediately closes thinking |
| Thinking mode | The assistant prefix opens a reasoning block |
The README contrasts these rules with V4. Copying an older tool-call tag can therefore create a format mismatch even when the surrounding JSON request is valid.
Separate encoding from output recovery
The reference output parser expects well-formed text. It does not promise to repair arbitrary malformed generations. A successful conversion of input messages consequently does not establish safe parsing of every possible output.
Use the maintained DeepSeek recipe toolkit as the first-party protocol-aware alternative described by the release. Whichever implementation is selected, preserve its version and compare a small fixture against the published reference before attributing a response difference to model capability.
Keep a minimal text fixture
Prepare three conversations: ordinary chat, thinking with a declared effort, and a follow-up system instruction. Store the structured messages and expected prompt shape for each. Add a separate tool conversation only after the simpler boundaries are understood.
Check role order, opening and closing thinking markers, the effort prefix and the final answer boundary. This establishes a bounded format acceptance record. It does not guarantee production robustness, task accuracy or identical behavior from every hosted API wrapper that accepts similarly named request fields.