DeepSeekV4 Flash Vision Exp

External Link

DeepSeek-V4-Flash-Vision-Exp is DeepSeek's first experimental multimodal model in the DeepSeek-V4 family, adding visual modules and continued training to DeepSeek-V4-Flash.

Token context
1,049K tokens
Inputs
Text, Image
Outputs
Text
Released
Aug 21, 2026

Providers & Pricing

Estimate workload cost →
ProviderInput / 1MOutput / 1MCache read / 1MCache write / 1MObservedEvidence
$0.44$1.32$0.014—Oct 19, 2026Provider-reported
$0.22$0.66$0.007—Sep 2, 2026Official fact
LiveBench2026-06-25-90f8d44da96579.67percent52,6441,270deepseek-v4-flash-vision-expaverage per caseRecomputedwinner eligibleThird-party benchmark

At a Glance

Model Facts

Identity

Status
retired
Developer ID
deepseek-ai/DeepSeek-V4-Flash-Vision-Exp
Released
Aug 21, 2026
Version
DeepSeek-V4-Flash-Vision-Exp
License
mit

Capacity

Token context
1,049K tokens
Maximum output
393K tokens
Total parameters
304.6B
Active parameters
Unknown
Knowledge cutoff
Not reported

Interface and Access

Inputs
Text, Image
Outputs
Text
API available
Yes
Open weights
Yes
Self-hostable
Yes
Reasoning
Yes
Vision
Yes
Tool calling
Yes
Capabilities
chat, generation, reasoning, structured outputs, tools

This page represents one developer model product. Dated API snapshots, serving endpoints, pricing, regions, quantizations, and service tiers attach as versioned aliases or provider details and never create duplicate public model records.

Version Lineage

PredecessorUnknown
SuccessorsUnknown
Aliasesdeepseek-ai/DeepSeek-V4-Flash-Vision-Exp, deepseek-v4-flash-vision-exp, DeepSeek-V4-Flash-Vision-Exp

Explicit Unknowns

  • Knowledge cutoff

Primary Evidence

Sources and Observation Date

Comparable Peers

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Questions

DeepSeek V4 Flash Vision Exp FAQs

What is DeepSeek V4 Flash Vision Exp?+

DeepSeek-V4-Flash-Vision-Exp is DeepSeek's first experimental multimodal model in the DeepSeek-V4 family, adding visual modules and continued training to DeepSeek-V4-Flash. It is developed by DeepSeek and its current sourced lifecycle status is retired.

What inputs and outputs does DeepSeek V4 Flash Vision Exp support?+

DeepSeek V4 Flash Vision Exp's current record lists Text and Image as input and Text as output.

How much context does DeepSeek V4 Flash Vision Exp support?+

The current record lists a 1,049K-token context window and a maximum output of 393K tokens.

Is DeepSeek V4 Flash Vision Exp available through an API?+

DeepSeek V4 Flash Vision Exp is marked as API-available. The directly sourced provider records currently include Deepinfra, DeepSeek, and Fireworks Ai.

How large is DeepSeek V4 Flash Vision Exp?+

The current record lists 304.6B total parameters and an unknown active parameter count. Its architecture is DeepseekV4ForCausalLM.

What capabilities and tasks does DeepSeek V4 Flash Vision Exp support?+

DeepSeek V4 Flash Vision Exp's recorded capabilities are chat, generation, reasoning, structured outputs, and tools. Its supported tasks are image, reasoning, and text.

How much does DeepSeek V4 Flash Vision Exp cost through an API?+

The lowest directly sourced prices currently attached to DeepSeek V4 Flash Vision Exp are $0.22 per million input tokens through DeepSeek and $0.66 per million output tokens through DeepSeek. Prices are provider-specific and should be checked against each cited observation date.

Can DeepSeek V4 Flash Vision Exp be self-hosted?+

DeepSeek V4 Flash Vision Exp is an open-weight model. Self-hosting is supported, and the recorded license is mit.

Does DeepSeek V4 Flash Vision Exp have related versions or aliases?+

No predecessor is recorded. No successor is recorded. Recorded aliases are deepseek-ai/DeepSeek-V4-Flash-Vision-Exp, deepseek-v4-flash-vision-exp, and DeepSeek-V4-Flash-Vision-Exp.

Which models can DeepSeek V4 Flash Vision Exp be compared with?+

The current compatible peer set includes Claude Fable 5.1, Gemini 3.1 Pro, DeepSeek V4.1 Flash, Qwen3.8 Max, and Llama 4 Maverick 17B 128E Instruct. Compatibility requires shared sourced modalities and tasks; it does not imply that one model is better. Browse model comparisons →

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