My comparison between Deepseek(+vision proxy) and Minimax-M3 / Opencode Go on an image analysis task

I recently finished setting up my workflow using Reasonix with Deepseek V4 Pro, with vision capabilities via MiMo 2.5 that I just set up (credit to u/houston697, see https://www.reddit.com/r/DeepSeek/comments/1umo0bj/hasslefree_way_to_have_vision_capabilities_with/ ). But I spent a few hours fine-tuning the vision model because I found that it was very sensitive to the prompt, so I had to fiddle with it for a few hours until it could give useful interpretations to deepseek.

Then I was worried I wasted my time building a vision skill since models like Minimax M3 is supposed to already have multimodel capabilities while being just as good as deepseek....lol. So I tried to pit my workflow against Minimax M3 ($5 token), as well as Opencode Go ($5/mo) where I tried to use both Qwen 3.7 pro and Minimax M3 in opencode.

I gave the same prompts to all three (it was something like segment holes in 200 images and provide statistical info) and evaluated the results as well as the token costs.

You guys will be happy to know that Reasonix/Deepseek+Mimo was the clear winner. It gave me a lot better answer, was a lot more concise (Minimax was very verbose and slow, an issue documented in other threads as well), and was about 2x cheaper overall.

I did like the versatility of being able to switch models in opencode go - had to do this when Qwen stopped working on me for some reason, so I switched around to Minimax m3 and even mimo 2.5 there.

I honestly thought I would keep Opencode Go for vision-heavy tasks but it looks like deepseek+mimo pipeline works just as well for now!