DeepSeek-V4-Flash-Vision-Exp: pricing, features, alternatives

DeepSeek-V4-Flash-Vision-Exp is DeepSeek's first experimental multimodal model in the V4 family. The Hugging Face card says it takes the DeepSeek V4 Flash architecture, adds visual modules, and continues training so the same agent stack can read images. License is MIT. Hugging Face created the repo on 2026-08-31. On 2026-09-08 the card showed about 800 likes and 251,611 downloads last month. r/LocalLLaMA was using it for game-world screenshots. Hugging Face lists the model size as 305B params (BF16 / FP8 shards).

Compare DeepSeek V4 Flash if you wanted the text-only Flash SKU, Qwen3.8-27B if you wanted a smaller dense multimodal local model, or MiniCPM5-2B if you wanted on-device 2B.

Model Specifications

Spec	DeepSeek-V4-Flash-Vision-Exp
Role	Experimental V4 vision + text agent
Base	DeepSeek-V4-Flash with visual modules
HF size	305B params (card, 2026-09-08)
License	MIT
Pipeline	image-text-to-text
Card date	2026-08-31

Limitation: the name ends in Exp. This is not a drop-in replacement for Flash-0731 in a text-only fleet until you measure vision VRAM and the extra encoder. Serving examples use a 4-GPU GB300-class node, not a laptop.

Key Features

Multimodal agent scores on the card: ApexBench Pass@1 36.5 vs Flash-0731 26.2 (text model ignoring images). Agents' Last Exam 27.3 vs 25.2. Chartography 64.3. ZeroBench Pass@5 35.0. Vendor numbers.
Text agents stay close: Terminal Bench 2.1 83.9 vs Flash 82.7. Toolathlon-Verified 75.9 vs 70.3. Cybergym is slightly worse (75.3 vs 76.7).
DSpark in the same checkpoint: SGLang --speculative-algorithm DSPARK with no separate draft path.
Prompt encoding: OpenAI-style JSON image blocks or compact <image>path</image> TXT. Both encode to the same token IDs in the examples.
vLLM recipe: vllm/vllm-openai:deepseekv4-flash-vision image, tool-call parser deepseek_v4, FP8 KV cache.

Use Cases

Agent harnesses that already run Flash and now need screenshots, charts, or UI dumps in the same session.
Local multimodal research on MIT weights, not a closed vision API.
Game and world-gen experiments like the r/LocalLLaMA Flash-Vision-Exp thread.

Pricing

Weights are free under MIT. Hosted chat is on DeepSeek's site; this card does not print a vision token row. Do not copy Flash text prices onto vision.

Piece	Price	Source 2026-09-08
Weights	$0	MIT on Hugging Face
Self-host	Your GPUs	Card example: 4-way tensor parallel
deepseek.com chat	Confirm the product page	Not a vision rate card

Getting Started

Open huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp.
Follow the vLLM recipe or SGLang cookbook with --speculative-algorithm DSPARK.
Encode images with the repo encoding/ helpers, not a random VL chat template.
Confirm DeepSeek V4 Flash if the job is text-only.

First-party resource: DeepSeek-V4-Flash-Vision-Exp model card.

Frequently Asked Questions

Same as V4 Flash?

No. Flash-0731 is text. Vision-Exp adds visual modules and continued training. Text-agent scores stay in the same band; multimodal scores are the reason this SKU exists.

Can I run it on one consumer GPU?

The published vLLM example is a 4-GPU node with FP8 KV cache. Plan cluster hardware, not a 24GB card.

Is it MIT?

Yes. The card badge and license section say MIT.

Alternatives

DeepSeek V4 Flash: text-only Flash agent SKU.
Qwen3.8-27B: smaller dense multimodal local model.
MiniCPM5-2B: on-device 2B, not 305B.

Tips

Quote MIT, 800 HF likes, 251,611 monthly downloads, and 305B size from the card checked 2026-09-08.
Keep DSpark on the same checkpoint. Do not invent a separate draft repo.
If ApexBench is the goal, do not evaluate Flash-0731 by dropping the images.

Conclusion

DeepSeek-V4-Flash-Vision-Exp is the MIT V4 experiment that keeps Flash-class text agents and adds screenshots. Start at the Hugging Face card, then decide whether DeepSeek V4 Flash already covers a text-only path.