Oof 800 by 800 kills a lot of use cases

I’ll keep that in mind next time I need to tell what time it is by asking an llm to read an analog clock.

Snark aside, I’m not sure that these gotcha tests are any more useful than asking politicians gotcha questions. Sure, the model can’t tell me what time it is, but it can code the Wang algorithm for noisy audio matching in one shot. Maybe this is just me being an optimist, but this is my hiring philosophy and I guess maybe now my llm philosophy: I’m not interested in seeing how dumb I can make you look, I’m more interested in how smart you can be.

I would say, try without thinking on. I find reasoning on any rag type request seems to increase hallucinations, probably due to the thinking tokens taking attention away from the, in this case, vision tokens.

I'd recommend non-thinking for any non-prompt input, and leave the thinking where it has to actually reason.

This is not a normal looking clock - most clocks have either one color for all hands (second hand is thinnest and maybe also longest) or one color for hour/minute and one for second. I know that the hand lengths and thicknesses on this image are correct but for some reason I, a totally human person who grew up when analog clocks were still common, see this and think the hand on the 5 is the minute hand. How does the AI do if you just make all the hands black?

A good share of humanity would have also gotten this question wrong!

I was wasting hours yesterday trying to get DeepSeek V4 Flash (with Qwen 3.8 27b as the vision agent, actually) to read sheet music to pass a Terminal Bench 3 benchmark and none of it was working... nothing... I changed models to gemma 31b, I tried OCR models... nothing could get it...

And then I realized, wait a second... you're testing the harness not only against a difficult benchmarking problem, but it's one you're literally never going to use the coding harness for either, lol. I don't write programs that read or interact with sheet music and I never will.

tl;dr Being frustrated that a "state of the art" vision model doesn't have perfect vision is a fools errand.

It can read and extract information from screenshots and PDFs just fine (my setup). No need to worry about edge cases.

Gemini 3.7 Flash and 5.6-Sol (on all reasoning levels) also answer 8:10:25. The new "stealth" Ox Alpha also replies with the same. Opus 5 replies with 8:10 (no seconds). Not sure why this is so hard for them; Gemini is especially good at vision and I would have expected better from it.

most likely a preview. they often release the preview via API, get more training data, post train some more then release the weight. i would expect to see it perform better in a few weeks or a month.

welp, damning indictment. not sure if that means DS is super crap, or qwen is super good

It tried to recreate vision by analyzing pixels on 3 separate projects I had it working on.

I've mitigated this by giving it a "skill" that just means the harness using a different model.

Yeah I've seen it a lot. It goes through the effort, unasked, of pulling screenshots off a connected device and then it's like... Oh shit yeah I can't see.

Or about to start. Depending on which life philosophy you desire to believe.

Can split and feed?

This is a fairly small model for coding and agentic work.

Training it on images like yours would just make it worse in other areas.

The closer comparison would be 5.6-Luna. On DeepSWE at Xhigh it's 57% at 1/6 the cost of Sol M, on Max it's 67% at 1/3rd the cost.

Still an advance, I just thought it worthy to note Sol isn't nearly as impressive on the cost/performance frontier as discounted Luna.

really? i vividly remember the 800x600 screens from back in the day, defintely into the 2000s. pretty sure they only went away around ~05 or so

and games like the original diablo 2 only supported 640x480; going up to ... i think 800x600 with the xpac?

seriously, i think youre either a zoomer and didnt actually live through that time period or youre ... idk, having an episode? ^_~

You'd expect a tool-enabled model to leverage crop and zoom tools to inspect and validate what it thinks it's seeing, though.

I typically provide small screenshots to llms so this seems fine for that usecase, providing an entire screens context seems cause confusion with a lot of llms.

Nope. Handles vision differently.

You can use the playground on openrouter. Still needs an account and some money, but it's one of the more useful accounts to have sitting around with a $5 of balance. Great for one-off experiments with various models

https://openrouter.ai/deepseek/deepseek-v4-flash-vision-exp

It was explicitly said that they are pursuing multimodal support. A quote from the meeting transcript: https://github.com/demo-zexuan/liang-wenfeng-investor-meetin...

    Nevertheless, as a component, we will undoubtedly implement multimodal support — and we are already doing so. We plan to develop relevant models, ensuring that versions like V4 and subsequent iterations will natively support multimodal functionality.

    Achieving excellence in AI training does not require a global model or even multimodal approaches—by narrowing the scope of AI training and eliminating multimodality, certain tasks may remain unachievable without compromising the algorithm's validity.

    Multimodal approaches ultimately need to be implemented.

Worth noting that deepseek has had a separate vision-capable model for some time, which also powers their chat interface's vision mode

I think you're thinking of Dario saying this about image generation.

I use research agents to attribute methane emissions plumes detected by satellites to oil and gas infrastructure on the ground, using a pre-baked database of geospatial data and web research.

Had a tool that called out from DeepSeek to Gemini 3.5 Flash for viewing the spatial features in the context of high-resolution satellite imagery of each site, but will be trialling this model for the whole thing now.

The obvious use case, especially on HN, is frontend dev of any kind at all. The second most obvious one is OCR of paper documents.

https://stencil.so/blog/snapcompact - some agents (notably oh my pi, i forget which others) come with snapcompact as a primary means of compaction. Take the entire context, stick it in a small font in a PNG, and vision capable models can summarize and pull out the most useful information in many fewer vision tokens than the original context used.

I've not used it myself, but it's there.

Having vision is very handy for getting it to make plots/figures with matplotlib. A model with vision can be much more autonomous with catching visual glitches/misalignments and correcting itself.

Also used it for 3d printer control once, had it diagnosing issues, calibrating my Tradrack MMU and canceling failed prints autonomously from a couple of cameras placed around the printer.

Allowing it to analyse a system under test (usually in an emulator, web browser, Electronic app container, etc. - something that can be reasonable captured).

It makes running much, much longer feedback loops possible. Although you can mix and match non-vision and vision models simply by invoking a vision model when you need one, as I like to use non-vision models like glm-5.3.

Any kind of spatial/graphical task is likely going to go better with a vision-capable model. Feed it a napkin-sketch of what your app should look like. Have it verify screenshots of the UI it just built. All of these one-shot-a-video-game evaluations that have suddenly become popular only work if the model can interpret screenshots...

In the feedback loop when working on anything UI or graphical output related.

My product is connecting employers and workers with conversational agents. They love to communicate with images — CVs, documents, photos of worksites. Even CV-as-photo or offer-as-photo format is very popular. My daily driver Deepseek Flash can't see those photos. So I use image models to let agents understand the context.

I've been working on an agentic graphic design tool, so vision is quite useful for having the model check its own work. I'm already seeing improvements with this model vs the text-only one.

No one’s mentioned robots, so… robots. VLA models, etc.

Generating alt text for images in social media posts.

Going straight to surveillance and unable to think "nicer" ways... is strange.

1. process graphs and charts

2. process handwritten math formula, also chinese characters writings

3. process design sketch and wireframe

4. process scanned documents

... etc

in fact these transformer models currently suck for surveillance, too slow and expensive. There are already faster and better facial/gait/object recognition models out there.

when i am learning i draw what i undestand in a picture and ask ai to correct me. i want ai to watch over me while i am learning.

this is such good way to learn something for me.

That would've been a very strange arrangement.

Correct link

https://xcancel.com/deepseek_ai/status/2090730032574631962

I believe so. Openness has always been a consistent tradition of DeepSeek

I imagine this is based on their 'Thinking with Visual Primitives' paper, and they had mentioned that the weights would be released for that

This is something I would like to know as well.

But if not, does anybody know a recommended way to attach vision to deepseek flash (on a self-hosted infrastructure)?