DeepSeek Releases V4 Flash Vision Exp, an Experimental Multimodal MoE Model with 1M Context

DeepSeek Adds Vision to V4 Flash Line

DeepSeek has released DeepSeek V4 Flash Vision Exp, an experimental vision-enabled version of its DeepSeek V4 Flash 0731 model. The new model adds image understanding capabilities while, according to DeepSeek, matching the base text model's performance on agents, reasoning, and world knowledge tasks.

Architecture and Specs

V4 Flash Vision Exp is a sparse mixture-of-experts (MoE) model with 13B active parameters out of 284B total parameters. This active/total ratio is consistent with DeepSeek's established approach of using large parameter pools while keeping inference costs low through sparse activation.

The model supports a 1M token context window, positioning it for long-document and multi-image workflows. It is listed as available via OpenRouter, with a release date of August 21, 2026.

Pricing

OpenRouter lists pricing at:

• Input: $0.22 per 1M tokens
• Output: $0.66 per 1M tokens
• Cache read: $0.007 per 1M tokens

No separate benchmark scores have been published for this release. DeepSeek has not disclosed formal evaluation results comparing V4 Flash Vision Exp against other multimodal models on standard benchmarks like MMMU or DocVQA.

Intended Use Cases

DeepSeek positions the model for:

• Document and chart understanding
• Visual question answering
• Multimodal agent workflows that interleave text and images

The "Exp" designation in the model name signals this is an experimental release, suggesting DeepSeek is testing the vision capability ahead of a potential stable version. The model builds directly on DeepSeek V4 Flash 0731, extending it rather than representing a ground-up architecture change.

What this means

This release extends DeepSeek's Flash line into multimodal territory without, according to the company, sacrificing the text-only model's reasoning and agentic capabilities — a claim that has not been independently verified through published benchmarks. The 13B active-parameter design keeps inference costs comparable to DeepSeek's other efficient models, and the sub-$1 per-million-token pricing on both input and output continues the aggressive cost positioning that has characterized DeepSeek's strategy against Western frontier labs.

The 1M token context window is notable for a vision-capable model at this price point, potentially enabling use cases like analyzing lengthy multi-page documents with embedded charts and images in a single pass. However, the experimental tag means enterprises should expect further iteration before this becomes a production-recommended model. Real-world performance on multimodal benchmarks will determine whether it can compete with established vision models from OpenAI, Google, and Anthropic, none of which have published comparable figures yet for this specific release.