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DeepSeek's vision-capable sparse MoE model for text and image input, with a 1M-token context window and MIT-licensed open weights.
Overview. DeepSeek-V4.1-Flash-1017 is a refresh of the DeepSeek V4.1 Flash weights, published by DeepSeek on 17 October 2026. It takes text and image input and produces text, over a context window of up to 1,048,576 tokens. The architecture is a sparse mixture of experts with 384 routed experts and six active per token. DeepSeek states that the architecture, tokenizer and chat format are unchanged from V4.1 Flash, so switching is a change of model name.
Key strengths. Image and text input in one model, a 1M-token context window, and open weights under the MIT licence. Tool use, structured output and reasoning effort levels were confirmed on the serving endpoint by Stav's own tests on 19 October 2026. DeepSeek reports better scores than V4.1 Flash on agentic coding and terminal benchmarks; those figures come from the vendor's model card, not from an independent benchmark.
When to use it. Use it where you would use V4.1 Flash and want the newer checkpoint: long documents or conversations that would otherwise need chunking, workloads that mix images and text, and agentic or multi-step tasks where you raise the reasoning effort only when a query needs it.
Limitations. Independent benchmarks are not yet available for this checkpoint, and the training data cutoff, total parameter count and maximum output length have not been published by DeepSeek. Stav's tests confirm that tool use, structured output and reasoning levels work, but not how well. Output length is bounded by the serving provider.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.45 / 1.35 | Online |
13 use cases classified by the curator. The note says why.
The 1,048,576-token context window supports grounding answers in very large document collections supplied in context; no retrieval or answer-generation benchmarks are documented.
A general-purpose language model can handle text classification, and the 1,048,576-token context window accommodates large batches or contextual inputs; no classification scores are provided.
The large context window supports retaining substantial conversation or reference material, but no tool-calling capability, latency data, or customer-support benchmarks are specified.
The 1,048,576-token context window is well suited to questions over lengthy documents; no document-QA scores or vision capabilities are specified.
The model can process very large inputs with its 1,048,576-token context window, though no entity-extraction benchmarks or specific extraction capabilities are listed.
The 1,048,576-token context window can support synthesis across a large body of supplied literature; no literature-review benchmarks are provided.
The 1,048,576-token context window can provide broad context for search-related tasks, but no embedding or retrieval capability is flagged, so it is not evidenced as a dedicated semantic-search model.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-ai/deepseek-v4-1-flash-1017",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at