Qwen3.6-35B-A3B Exposes Visual Reasoning Brittleness
This Reddit discussion focuses on a deceptively simple visual physics/geometry puzzle that Qwen3.6-35B-A3B reportedly gets wrong unless the image is rendered at very high resolution. The poster compares its behavior with Gemma 4, Gemini 3.1, and Claude Opus, arguing that the model can swing between incorrect answers and later self-corrections deep into its reasoning trace. The broader takeaway is that even a strong open-weight multimodal model can still be surprisingly fragile when the visual input is ambiguous or low-resolution.
Hot take: this is less about raw intelligence and more about how easily vision-language models can be derailed by image fidelity and internal reasoning drift.
- –The post is useful as an informal benchmark because it shows inconsistent behavior on a task humans consider trivial, which is exactly the kind of edge case that reveals multimodal brittleness.
- –The reported failure mode is not just “wrong answer,” but unstable intermediate reasoning: the model appears to misread slopes/geometry, then later recover, then regress again.
- –The comparison set matters: some models fail more often, some only at lower resolution, and some remain stable, suggesting this is a meaningful differentiator for visual reasoning quality.
- –The official model positioning is strong: Qwen3.6-35B-A3B is an open-weight 35B total / 3B active MoE model aimed at agentic coding and multimodal reasoning, so benchmark-style scrutiny is appropriate.
- –As a community signal, the thread is interesting precisely because it mixes praise for Qwen’s consistency on harder tasks with a clear counterexample on a very simple visual problem.
DISCOVERED
3h ago
2026-04-18
PUBLISHED
5h ago
2026-04-18
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