DeepSeek V4.1 Flash Economy

Overview

DeepSeek V4.1 Flash Economy is a long-context, multimodal chat model from DeepSeek. It uses a Mixture-of-Experts transformer and a causal encoder-decoder layout that reduces KV-cache size. You provide text and, if needed, up to 10 images, and it generates text responses. It’s a strong fit for agents and analysis workflows that must keep large context without aggressive truncation.

What you can build

• Long-context Q&A over large notes, logs, or multi-step plans
• Screenshot and UI analysis that returns actionable debug steps
• Image-grounded customer support replies with step-by-step instructions
• Code review chats that keep an entire repo summary in context
• Structured extraction that returns JSON for downstream automation
• Multi-turn “project memory” assistants using session-based chat history

Inputs

• A single instruction or question as plain text.
• An optional conversation transcript as a JSON array of role-based messages. It supports up to 4,096 message items.
• Optional images to ground the request. You can attach up to 10 files as JPEG, PNG, or WebP.
• Optional user and session identifiers (string or number). Use them to keep chat history across turns.
• Optional system-level instructions to set behavior for a session. They apply only on the first turn.
• An optional reasoning mode choice: automatic, forced on, or forced off.
• An optional reasoning effort level from minimal to high. Higher effort can increase output length.
• Sampling controls to trade off creativity and repetition. Temperature ranges from 0 to 2. Nucleus sampling ranges from 0.05 to 1. Repetition penalties range from -2 to 2.
• A maximum response length limit, counted in tokens. This deployment allows up to 384,000 tokens.
• Optional tool-call configuration objects for compatibility with OpenAI-style schemas. This deployment may not execute user-defined tools.
• Optional continuation input that resumes a previous run, plus prior tool results when continuing a tool call.

Outputs

The model returns generated text.

• For chat use, you’ll get an assistant message that can include formatted markdown, code blocks, and step lists.
• For extraction use, it can return strict JSON if you instruct it to output only JSON.
• When you provide images, the output is still text. The model describes what it sees and explains conclusions.

Recommended settings

• General chat and coding: keep temperature at 1.0.
• Deterministic outputs and extraction: keep temperature near 1.0 and use a higher nucleus cutoff like 0.95 or 1.0.
• Long reports: raise the response length limit to at least 256,000 tokens when you expect very large answers.

Limitations

• The model generates text only. It can analyze images, but it can’t output image files.
• Very low-quality inputs reduce accuracy. Blurry photos, tiny text, heavy compression, and scanned documents can fail.
• Long context can dilute attention. Important facts should be restated or summarized near the end of the prompt.
• Some hosts apply stricter caps than the model’s maximum context or output limits.
• Tool calling depends on the hosting provider. Some deployments accept tool fields but ignore custom tools.

Safety & compliance

• Follow applicable laws and third-party rights when generating or extracting content.
• Don’t submit secrets, passwords, or private personal data unless your policy allows it.
• If you request structured JSON, validate it before executing actions.
• Use clear user consent flows when analyzing user-provided images that include people or sensitive documents.