DeepSeek · Model guide

DeepSeek V4 Flash Vision Exp

deepseek/deepseek-v4-flash-vision-expReleased Aug 21, 202610% off

A compatibility alias, kept so existing integrations keep working. DeepSeek retired the experimental V4 Flash Vision Exp model on 2026-09-10, and requests to this id are served by DeepSeek V4.1 Flash, which has image understanding built in rather than as an experiment. New integrations should target DeepSeek V4.1 Flash directly.

Modalities

Context

Max output

384K

Price / 1M tokens

$ 0.30$ 0.27in$ 1.08out

StreamingTool callingJSON outputImage inputThinking mode · can be turned off

What this model is good at

What the vendor positions it for, and which of our models to reach for instead.

VendorDeepSeek positions it as an experimental multimodal model: on par with V4-Flash for pure text, with a large jump on vision-dependent agent benchmarks that brings multimodal agent capability close to Opus-4.8.source

Best for

Vision-dependent agents · The vendor reports a large jump over V4-Flash on agent benchmarks that require vision, approaching Opus-4.8.(vendor claim)source
Charts and document images · Takes up to 600 images per request via base64, an external URL or the Files API.source
Coding agents that read screenshots · Text capability is on par with V4-Flash, so vision is added without giving up the text agent scores.(vendor claim)source

Neighbouring models

deepseek/deepseek-v4.1-flashsuccessor —Use V4.1 Flash directly — it is what this id already routes to, it takes images natively, and its model page describes what you actually get.source
deepseek/deepseek-v4-prostronger —Choose Pro for the hardest knowledge, research and complex-agent tasks — it has no image input.(vendor claim)source

Compare them side by side →

Pricing and billing

Billed per token. Cached input is charged at the cache-read rate.

Price / 1M tokens	You pay
Input	$ 0.30$ 0.27
Output	$ 1.20$ 1.08
Cache read	$ 0.0060$ 0.0054
Cache write	$ 0.30$ 0.27

Estimate a request

Input tokens10,000

Output tokens2,000

Cache hit rate0%

$ 0.0049

Estimated cost per request

10.0K × $ 0.27
+ 2.0K × $ 1.08

≈ $ 4.86 per 1,000 requests

Capabilities and limits

What CrossModel guarantees across every route this model can take right now.

Context window: 1.0M tokens
Max output: 384.0K tokens
Input / output modalities: Text + Image → Text
Streaming: Supported
Tool calling: Supported
Structured output (JSON): Supported
Image input: Supported
Thinking mode: Supported· can be turned off
Reasoning effort: low · medium · high · xhigh · max
Available endpoints: /v1/chat/completions · /v1/responses · /v1/messages

First-party sources:Specshuggingface.co 1 api-docs.deepseek.com 2 api-docs.deepseek.com 3 api-docs.deepseek.com 4Capabilitiesapi-docs.deepseek.com 1 api-docs.deepseek.com 2 api-docs.deepseek.com 3 api-docs.deepseek.com 4 api-docs.deepseek.com 5

Use it in your tools

Point the base URL at CrossModel and paste this model ID — every tool below has a setup guide.

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See all integration guides →

Frequently asked questions

What is DeepSeek V4 Flash Vision Exp?

DeepSeek V4 Flash Vision Exp is available on CrossModel as deepseek/deepseek-v4-flash-vision-exp. It has a 1M-token context window and can return up to 384K tokens per request.

How much does DeepSeek V4 Flash Vision Exp cost?

Currently $ 0.27 per 1M input tokens and $ 1.08 per 1M output tokens. Cached input is billed at $ 0.0054 per 1M.

Does DeepSeek V4 Flash Vision Exp support tool calling and structured output?

Tool calling: Supported · Structured output (JSON): Supported · Image input: Supported · Streaming: Supported · Thinking mode: Supported

Which endpoint do I call?

For text models, OpenAI Chat Completions (/v1/chat/completions), OpenAI Responses (/v1/responses) and Anthropic Messages (/v1/messages) all work — pick whichever your SDK already speaks.

Can I try it without writing code?

Yes. Sign in and open the Playground on this page to chat with the model directly — no API key needed. Usage is billed from your wallet at the normal rate.

Chat first, integrate later

Chat first, then wire it in once you like the answers.