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From Benchmarks to Cognitive Landscapes: Building ReasonScape
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Let's talk about one of the hidden gems in the ReasonScape evaluation results, lucky #13: https://huggingface.co/aquif-ai/aquif-3.5-8B-Think
Built on top of the solid Qwen3-8B foundation, aquif-3.5-8B-Think successfully preserves the high performance of the original model while consuming 30-50% less reasoning tokens.
The most notable regression vs the base model here is in arithmetic - if your workload is math heavy this model demonstrates an unfortunate collapse with performance under growing complexity.
The interesting combination of awesome overall performance on SVG simple shapes identification coupled with a total inability to recognize more complex shapes like 'House' or 'Arrow' is a behavior directly inherited from the base model (but with a ~20% improvement in token utilization).
If you like your reasoning models token-efficient, Aquif-3.5-8B-Think is well worth a spin.
Higher resolution, more detailed, interactive plots are available at the m12X explorer: https://reasonscape.com/m12x/explorer/
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From Benchmarks to Cognitive Landscapes: Building ReasonScape
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