DeepSeek V4 Flash Vision Exp
Neural Network
DeepSeek V4 Flash Vision Exp is a DeepSeek model with image input support for agentic tasks, coding, and API usage.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works DeepSeek V4 Flash Vision Exp?
DeepSeek V4 Flash Vision Exp is an experimental build from DeepSeek that adds image understanding to DeepSeek V4 Flash 0731: the model accepts images along with text and analyzes their content. In terms of text capabilities, including agentic scenarios, it matches the base version, so switching to it does not require rebuilding your workflow. In BotHub, the model is accessible from Russia without a VPN or foreign card, and you pay per token — no subscription, and the balance does not expire. Over 250 models are available in the same window, allowing you to switch between them and compare answers for a single task. A unified OpenAI-compatible API lets you change models in a multi-model system without rewriting the integration; chats are encrypted via AES-GCM. Developers will find it useful for analyzing interface screenshots and error logs alongside code, analysts for reading charts, tables, and document scans, product teams for building agents that work with both text and images, and support teams for classifying user-submitted images. Companies have access to contracts, invoices, EDI, and an admin panel with limits.Frequently asked questions about DeepSeek V4 Flash Vision Exp
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
The model works with text, images, and documents: it analyzes screenshots, diagrams, and files, writes and edits code, and solves reasoning tasks. The context window is over a million tokens, so a large project, a voluminous collection of materials, or a long client correspondence fits into a single dialogue.
In a single response, the model outputs up to 262,144 tokens — this is a very long text: an entire technical document, a large code module, or a detailed analysis. You usually don't need to break the task into parts, but for readability, it is still better to ask for the response to be structured by sections.
Yes, the reasoning mode is confirmed in the specifications: before answering, the model builds a chain of steps rather than grabbing the first option that comes to mind. This is noticeable in mathematics, logic, code analysis, and tasks where it is easy to lose track of the conditions. In simple queries, the analysis is barely noticeable.
Yes, the model supports function calling and structured JSON output. You describe the available tools, the model decides when to call them, and returns arguments in a strict format. Through the unified OpenAI-compatible BotHub API, this is convenient to integrate into agents, chatbots, and internal services.
Images and documents — yes: send screenshots, photos, diagrams, tables, and files, and the model will analyze them along with the text of the request. Audio and video as input formats are not noted in our data. If you need them, there are specialized models nearby in BotHub, and you won't have to rewrite the integration.
There are no separate measurements for Russian in our data, so we won't make any bold promises. Practice is simpler: run a couple of your typical tasks — document analysis, email, code with comments — and compare it with another model in the same window. Tokens are deducted based on usage and do not expire.