AI model vision and multimodal claims face skeptical real-world testing
A thread on GPT 5.6 Sol's vision capabilities showed the usual split: one camp says it's the best vision model OpenAI has shipped, another says it's 'still as blind as any other model, with no taste, no attention to any sort of detail.' One developer spent two weeks trying to get Codex to outpaint a generated image after a resolution change and couldn't make it work reliably.
This isn't a new pattern, but it's intensifying. Every frontier model release comes with vision capability claims, and the real-world feedback is consistently more ambivalent than the benchmark numbers suggest. The gap between 'performs well on standardized vision benchmarks' and 'reliably does what a developer needs in production' remains wide.
The Qwen overthinking thread is a related signal: model behavior in controlled evaluations and model behavior in production agent loops diverge in ways that only show up when you're running real workloads at real costs.
So what?
Don't build a product feature on a model capability until you've stress-tested it on your specific task, not a benchmark. Vision in particular is still unreliable enough that any product depending on it needs human fallback paths or should be scoped to a narrow, well-tested use case rather than general image understanding.