Qwen3.5-122B-A10B vs DeepSeek V3.2: Efficiency vs Raw Power in Open-Weight AI
A benchmark-by-benchmark comparison of Qwen3.5-122B-A10B and DeepSeek V3.2 - the efficiency-optimized underdog versus the brute-force open-source heavyweight.
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Qwen3.5-122B-A10B vs Llama 4 Maverick: The Efficiency Gap Nobody Expected
A data-driven comparison of Alibaba's Qwen3.5-122B-A10B and Meta's Llama 4 Maverick - two open-weight MoE models with radically different approaches to parameter efficiency and benchmark performance.
Qwen3.5-122B-A10B vs Mistral Large 3: When 4x More Parameters Buys You Less
A data-driven comparison of Qwen3.5-122B-A10B and Mistral Large 3 - two Apache 2.0 MoE models where the smaller one dominates text benchmarks despite a 4x active parameter disadvantage.
Qwen3.5-35B-A3B vs GLM-4.7-Flash: Two Chinese MoE Models, Very Different Strengths
Head-to-head comparison of Qwen3.5-35B-A3B and GLM-4.7-Flash - two Chinese-origin 30B-A3B MoE models with Apache 2.0/MIT licenses that dominate different benchmarks despite near-identical parameter budgets.
Qwen3.5-35B-A3B vs Llama 4 Scout: 3B Active Parameters vs 17B - Does 5.7x More Compute Actually Win?
David vs Goliath: Qwen3.5-35B-A3B activates 3B parameters and beats Llama 4 Scout's 17B active on MMLU-Pro, GPQA, and coding benchmarks - but Scout's 10M context window and native multimodal support tell a different story.
Qwen3.5-35B-A3B vs Nemotron 3 Nano 30B-A3B: Benchmarks vs Throughput in the 3B Active Parameter Class
A data-driven comparison of Alibaba's Qwen3.5-35B-A3B and NVIDIA's Nemotron 3 Nano 30B-A3B - two ~30B MoE models activating ~3B parameters that take fundamentally different architectural approaches to the same problem.