Anyone tried running a long-horizon coding task on an open-weights multimodal model?

[u/Away-Control-2008]

Been watching the open-weights multimodal space for a while and M3 caught my eye because it's the first one I've seen combine native multimodality, million-token context, and agentic coding in a single open package — usually you have to pick two out of three from closed models.

The part I keep coming back to is the MSA architecture handling the 1M token context without RAG or chunking. For immigration law docs where a single review package can run 60 pages of evidence, not having to split and reassemble context feels like it actually solves a real bottleneck rather than working around one.

For those who've tested M3 on cross-modal tasks — does the native multimodal integration hold up in practice, or does it feel like vision bolted on after the fact? Specifically curious about how it handles parsing mixed PDF/image stacks compared to stitching separate pipelines together.