DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp is an experimental multimodal model released on August 21, 2026 under the separate API ID `deepseek-v4-flash-vision-exp`. It accepts text plus JPEG, PNG, GIF, or WebP images and returns text through Chat Completions, Responses, or Anthropic-compatible Messages.
DeepSeek now has a first-party image-understanding API for screenshots, charts, documents, and visual agents without changing the text Flash or Pro model identities. Teams must still treat the model as experimental and API-only: DeepSeek has not published weights, an architecture description, or a model card for this variant.
DeepSeek's August 21 release, Vision guide, and live price table confirm the model ID, experimental status, text-plus-image input, text output, supported API formats, file routes, limits, image-token billing, 1M context, 384K maximum output, and peak/off-peak rates. DeepSeek's performance comparisons are vendor-reported. Reddit discussions are used only to identify client-configuration friction and demand for screenshot, OCR, and multimodal-agent workflows.
- Read screenshots, forms, charts, and diagrams inside an agent workflow.
- Extract or reason over text and structure in images.
- Combine visual observations with function calls or external tools.
- Reuse uploaded images through the Files API.
- Evaluate low-cost multimodal routing before production rollout.
The text endpoint remains `deepseek-v4-flash` on the 0731 backend and does not accept images. Image workflows require `deepseek-v4-flash-vision-exp`. The Vision-Exp release does not prove that the public MIT-licensed Flash checkpoint gained a vision encoder or that the hosted vision model shares an identical architecture.
DeepSeek accepts JPEG, PNG, GIF, and WebP images as base64 data, public URLs, or reusable Files API IDs. Chat Completions and Anthropic-format Messages accept images only in user messages; Responses carries them as input-image parts. The Files API is useful when the same image is reused or when inline request-size limits would be exceeded.
- Base64 and external-URL images can be up to 32 MiB each; a Files API image can be up to 64 MiB.
- A request can contain up to 600 images within the documented aggregate size limits.
- The `detail: low` option downsizes an image to 512×512 for cheaper processing when fine detail is unnecessary.
DeepSeek resizes images and caps each image at roughly 384 billed tokens. The model uses V4 Flash rates: off peak, $0.007 cache-hit input, $0.22 cache-miss input, and $0.66 output per 1M tokens; peak rates are $0.014, $0.44, and $1.32. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday.
DeepSeek has not published Vision-Exp weights, license metadata for a checkpoint, parameter counts, vision encoder or projector details, training data, a technical report, or a self-hosting guide. Its claim that text capability matches V4 Flash and multimodal-agent performance approaches Opus 4.8 is vendor-reported and should be tested with the same harness and visual tasks before adoption.
DeepSeek V4 Flash Vision Exp FAQ
Common questions about DeepSeek V4 Flash Vision Exp.
Is DeepSeek V4 Flash Vision Exp available now?+
Yes through DeepSeek's hosted API under `deepseek-v4-flash-vision-exp`. It is explicitly experimental, so verify availability, behavior, and limits before relying on it for production.
Can ordinary DeepSeek V4 Flash accept images?+
No. The text model ID `deepseek-v4-flash` rejects images. Use `deepseek-v4-flash-vision-exp` and a client adapter that sends the documented multimodal content blocks.
Can I download DeepSeek V4 Flash Vision Exp weights?+
DeepSeek has not published a Vision-Exp checkpoint or self-hosting guide. The available MIT-licensed `DeepSeek-V4-Flash-0731` weights are for the separate text model and should not be described as Vision-Exp weights.
How much does an image cost?+
Images are converted to input tokens and capped at roughly 384 tokens per image after resizing. Those tokens use the current V4 Flash peak or off-peak input rate, so exact cost depends on image dimensions, cache behavior, time window, and surrounding text.