DeepSeek V4.1 Flash 1017: API Names and Migration Checks

The announcement emphasizes stronger project-development performance with shorter outputs and faster completion. It recommends DeepSeek Harness v0.2.1-rc.1 or later for the new model. Those are publisher claims and integration guidance, not an independent finding that every agent task is now cheaper or more accurate.

• DeepSeek October 17 announcement

The official model card describes a minor V4.1 Flash update and identifies the harnesses used for reported benchmarks. Local inference has a separate setup: the card supplies a reference prompt encoder rather than a Jinja chat template. Its deepseek-recipe toolkit handles protocol conversion and output parsing, while inference, tool execution and HTTP transport remain the caller’s responsibility.

• Official 1017 model card and implementation links

For a hosted API client, a model upgrade does not mean you need to reproduce local weight loading. For a self-hosted evaluation, protocol compatibility requires more than loading the tokenizer. Choose the deployment path first and keep its dependencies in the evaluation record.

• DeepSeek model names and current mapping
• SJolt DeepSeek V4 Flash documentation

SJolt’s configuration still uses the older official Flash alias and retains older version metadata. Read together with DeepSeek’s remapping notice, that suggests the upstream alias can resolve to 1017; it does not establish a tested, explicitly versioned 1017 integration. No live SJolt request was made for this guide. In particular, do not infer image input or standalone Search/Fetch access from the provider announcement.

Treat this as a contract review item for an existing application. Confirm the supported input type, API format, thinking settings and billing behavior at the surface you actually call. A provider’s updated feature matrix cannot by itself widen a gateway’s validation rules.

DeepSeek’s quickstart uses the Chat Completions endpoint with bearer authentication and a messages array. This example asks for a text-only review plan and disables streaming. It is a billable request if run with your own key. It was checked for syntax, not executed against the model.

curl --fail-with-body 'https://api.deepseek.com/chat/completions' \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY" \
  -H 'Content-Type: application/json' \
  --data '{
    "model": "deepseek-flash",
    "messages": [
      {"role": "system", "content": "Return a concise review plan. Preserve stated constraints and flag missing facts."},
      {"role": "user", "content": "Plan checks for a media task ledger. A pending task must not create an empty image file or trigger a duplicate submission."}
    ],
    "stream": false
  }'

• DeepSeek first-call documentation

Keep the credential in the service environment and attach the response to a specific evaluation case. For a migration test, save completion status and usage as well as text. A shorter answer is useful only if it still contains the required checks; do not grade concision independently of completeness.

Use a small media-task ledger as a practical coding exercise. Supply invented task records with pending, successful and failed states. Ask the agent to implement state transitions and a review queue without making network requests. That isolates code and tool behavior from the cost and variability of generating actual images.

• Repeat a successful task event and require one accepted ledger entry.
• Include a failed download after generation succeeds; preserve the task ID for recovery.
• Change the brief revision while an earlier attempt is pending; keep both histories distinct.
• Pass a malformed tool result and require a visible error instead of an invented output URL.

Run the same fixtures before and after a migration with the same tool permissions and time limit. Check the produced code, executed commands and final files. Record elapsed time, review time and total usage across the loop. A single token count or provider benchmark cannot reveal whether a changed model introduced an extra retry or skipped a required validation.

If a result fails, classify the failure: wrong plan, tool argument, state handling, output parsing or review omission. Fix the smallest reproducible case before widening the rollout. Keep a known working configuration available, while remembering that an old alias is not necessarily a way to recover old weights.

DeepSeek now documents standalone Search and Fetch APIs at $0.75 per 1,000 successful requests each. The temporary free period mentioned for server-side tools in supported agent integrations ends October 31 at 16:00 UTC, which is November 1 at 00:00 Beijing time. That statement is not a promise that all direct Search/Fetch calls are currently free.

• Official pricing and tool-billing conditions

Keep model tokens, search calls and media generation in separate usage records. Compare complete accepted tasks under the same conditions, and read current peak/off-peak rates before estimating a real batch. No price from the direct DeepSeek service should be presented as an SJolt account quote.

After the fixture passes, a supported SJolt image route such as Nano Banana 2.1 can supply assets separately. Use the language model to organize approved requirements, then have your application submit and track the documented image task. The language response itself is not a generated image or a confirmation that a paid request succeeded.

• SJolt image request and polling guide
• SJolt model catalog

Retain the brief revision, media task ID, returned file and human acceptance decision. This keeps a language-model migration from silently changing which artwork is delivered. No inference, model installation or paid image generation was performed for this guide.