Benchmark Battle: Qwen 3.6 vs. Claude Opus 4.7 for Creative Generative Tasks
Qwen3.6-35B-A3B
Claude Opus 4.7
pelican benchmark
generative AI
large language models
SVG generation
5
Efficient Scale Wins Over Raw Power
Media Hype
3/10
Real Impact
5/10
What is the Viqus Verdict?
We evaluate each news story based on its real impact versus its media hype to offer a clear and objective perspective.
AI Analysis:
Moderate signal value regarding the growing viability of local, optimized models, though the benchmark itself remains niche and the comparisons are anecdotal.
Article Summary
Using the idiosyncratic 'pelican on a bicycle' benchmark, Simon Willison compared the output of Qwen3.6-35B-A3B and Anthropic's Claude Opus 4.7. While noting that the benchmark is intentionally absurd, the author finds a direct, albeit loose, correlation between generative quality and general usefulness. The comparison shows that while Opus 4.7 is state-of-the-art, the smaller, quantized Qwen model currently wins on specific SVG generation tasks, suggesting that resource-constrained local models might outperform massive, proprietary cloud APIs for certain types of creative output. The piece concludes with a reflection on the difficulty of benchmarking LLMs, asserting that the utility of these models is no longer simply tied to the absurdity of the comparison task.Key Points
- Qwen 3.6-35B-A3B, run locally on consumer hardware, currently produced better results for SVG illustration than Anthropic's powerful Claude Opus 4.7.
- The article underscores the difficulty in establishing a reliable 'utility' metric for modern LLMs, as the connection between benchmark quality and real-world application is weakening.
- While proprietary cloud models (like Opus 4.7) set the high bar, smaller, quantized models are proving surprisingly effective for specific, creative, resource-bound outputs.