DeepSeek V4.1 Flash (Text + Vision)

Overview

DeepSeek V4.1 Flash is a multimodal chat model from DeepSeek, released on September 10, 2026. It takes text plus images, then produces a text answer.

It uses a 552B-parameter Mixture-of-Experts backbone with a Causal Encoder–Decoder design. That design cuts long-context memory costs, so long sessions stay practical. You can enable a “thinking” mode when you want deeper reasoning, then return a clean final answer.

What you can build

• Screenshot and UI understanding for QA, support, and bug reports
• Chart and table explanation from images, with text-only conclusions
• Long-document Q&A and summarization with very long context
• Code writing, refactors, debugging, and test generation
• Agent-style workflows that call your functions, then continue with the results
• Multimodal RAG: use images as evidence alongside text notes

Inputs

• A single instruction written as plain text for one-off tasks.
• Or a multi-turn chat history as a JSON list of messages with roles like system, user, and assistant. Provide either a single instruction or a message list, not both.
• Optional images attached to a user turn. Wiro accepts up to 10 images per run in JPEG, PNG, or WebP.
• Optional user and session identifiers (string or number) to group chat history across turns.
• Optional system instructions that set behavior for the first turn of a session.
• Optional “thinking” toggle (auto, on, off) plus a reasoning-effort level (from minimal to high). Thinking is enabled by default on DeepSeek.
• Optional sampling controls for non-thinking mode, including randomness and nucleus sampling.
• Optional repetition and topic-shift penalties for non-thinking mode.
• Optional maximum response length cap, up to 384,000 output tokens.
• Optional function tool definitions, described with JSON Schema, when your workflow expects the model to request tool calls.
• Optional continuation input for tool https://wiro.ai pass a prior run token plus the tool results so the model can continue the same turn.

Outputs

The model returns a text response meant to be shown to an end user. For agent workflows, the response can also include tool-call requests that name a function and provide JSON arguments.

When thinking mode is enabled, the model may also return a separate reasoning segment before the final answer. The final answer is still plain text.

Recommended settings

• Hard reasoning and coding: keep thinking enabled and set reasoning effort to high. DeepSeek’s published instruct evaluations use the maximum effort setting.
• Tool-call agents: if you need to force tool calling (rather than letting the model choose), turn thinking off first. Some “must call a tool” behaviors are rejected in thinking mode.
• Benchmark-style generation: DeepSeek’s instruct tables report results using randomness set to 1.0 and nucleus sampling set to 0.95.

Limitations

• Model type: it reads images and text, but it only generates text. It does not output images.
• Context window is 1,000,000 tokens, but output is capped at 384,000 tokens.
• In thinking mode, several sampling knobs have no effect. Randomness and repetition/topic penalties are ignored.
• Image handling has strict limits. DeepSeek detects image format from file content, not the filename.
• DeepSeek automatically resizes images before inference. Very large images may cost similar tokens after resizing.
• Image limits in the DeepSeek API are higher than Wiro’s per-run upload cap. DeepSeek supports up to 600 images per request, with request size and per-image size limits.
• Low-quality inputs reduce reliability. Blurry screenshots, heavy compression, tiny text, and skewed photos can cause missed details.
• Tool calling is not tool execution. The model can request a function call, but your app must run it and send results back.

Safety & compliance

Follow DeepSeek’s platform terms and usage rules. Don’t send content that is illegal, exploitative, or harmful.

Treat images as sensitive inputs. Don’t upload secrets, private personal data, or confidential screenshots unless you have permission.

If you build tool-using agents, validate tool-call arguments before running any action. Restrict tool access and log tool use for audits.