OSS vs Black Box: The Open-Source Push in the Next Wave of LLMs

OSS is the buzzword shaping the next wave of LLMs. In a heated discussion about what’s coming next, open-source badges pop up for vision-first models and MOE-backed engines, signaling a tilt toward transparency and customization ^[1].

OSS Vision Models A thread calls out Qwen3 VL as potentially the best OSS vision model, while Magistral 24B’s vision chops are praised as top-tier. It even notes that Google hasn’t released any MOE models yet, hinting at a coming race in this space ^[1].

• Qwen3 VL – touted as potentially the best OSS vision model ^[1] • Magistral 24B – vision capabilities praised as top-notch ^[1] • Gemma – MOE approach; mentions Gemma 4 coming with 30B MOE, like Qwen3 ^[1]

MOE, Size, and Efficiency The discussion surveys MOE’s appeal at larger sizes and 8GB VRAM footprints. It points to Qwen3-30B-A3B and its variants, alongside GPT-OSS-20B, as the likely picks in that niche ^[1].

• Qwen3-30B-A3B – 30B variant highlighted among “the only ones worth using around that size” ^[1] • GPT-OSS-20B – named in the same context ^[1] • More MOE models in 15-30B size for 8GB VRAM ^[1]

The thread maps a crowded OSS landscape where open designs, MOE scaling, and on-device potential are the talking points as people compare openness to traditional proprietary options ^[1].

Closing thought: If OSS continues to surface in multimodal and efficiency-focused ways, expect more real-world tests that could tilt tool use and governance around future LLMs.

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