DS being unable to precisely view Playwright screenshots is the only thing I really miss from Sonnet. This is promising.

> Images are converted into tokens based on their dimensions, and these tokens are billed together with your text tokens.

> Before inference, every image is automatically resized:

> - Images with a total pixel count below roughly 384×384 are scaled up while preserving their aspect ratio.

> - Larger images are scaled down while preserving their aspect ratio so that the total pixel count after resizing is roughly that of an 800×800 image.

> As a result, there is an upper bound of 384 tokens per image: for example, a 2000×2000 image and a 5000×5000 image consume the same number of tokens after resizing. When a request contains multiple images, each image is counted independently under the same rule—there is no separate calculation for multi-image requests.

400 tokens per image results in 2,500 images per dollar, if I’m not mistaken.

edit: format.

> Larger images are scaled down while preserving their aspect ratio, so that the total pixel count after resizing is roughly that of an 800×800 image.

It's useful but for OCR and a lot of other applications it needs to be a bit higher (eg: putting in a full A4 / Letter sized page)

Benchmarks got a little bump from this: https://xcancel.com/deepseek_ai/status/2087864585504305397?s...

For what do you guys use vision in those models? surveillance is the obvious use case... but are there some "nicer" ways to use it?

800x800 is 640,000 pixels, or 0.64 Megapixels. That is less than the resolution of computer screens from 1995, Super VGA which has around 0.79 MPs.

This is useful for a reasonable amount of use-cases, but I think the watershed rez will be around triple that, ~1080p, which is enough for almost anything, except small text and subtle details.

I main V4 Pro at work now, and at home I route between Pro and Flash based on task. Switched to Opus 4.6 for some tasks at work because I needed image input - horrible. So nice to get image input with DS.

Edit: I see it has limited resolution. Luckily I just built a vision worker plugin for DSH that routes image input to Kimi K2.6 on Cloudflare.

I've heard that DeepSeek v4 Flash 0731 has frequently assumed that it has vision capabilities and then resorts to inventing text-based image analysis tools when it finds that it actually can't see. In that case, this is a great upgrade for the model.

Anecdotally, I had to tell 0731 to refrain from viewing screenshots since it kept breaking its sessions by trying to read images.

Congratulations! DeepSeek has finally gained eyes — the dark days are about to be behind us.

Interesting. Wasn't Deepseek's founder saying that they had explicitly decided not to focus on multimodal models at all and were going text-only because they believed it was enough to achieve AGI?

Hello Ox Alpha?

will this be open weights?

[flagged]

[dead]

[dead]

[dead]