DeepSeek v4 Pro's 1.6T parameters fail to beat smaller open models
DeepSeek v4 Pro lags behind GLM 5.1, Kimi K2.6 despite massive 1.6T parameter count.
DeepSeek v4 Pro, released by DeepSeek, has drawn mixed reactions for its 1.6 trillion parameter architecture—among the largest in open-source AI. Yet across multiple benchmarks, it consistently trails behind models with far fewer parameters. GLM 5.1 at 750B parameters is widely regarded as an 'opus,' while Kimi K2.6 (1T), MiniMax M3 (~450B), and MiMo v2.5 Pro (1T) all deliver superior performance per parameter. This has led the community to question whether raw scale is being prioritized over efficiency.
Several explanations have emerged: DeepSeek v4 Pro is still a 'preview,' meaning optimizations could improve scores. Others argue the real breakthrough might be the inference hardware—DeepSeek reportedly relies on Huawei chips—which could reduce costs but not boost raw accuracy. The debate underscores a broader industry shift from parameter-count hype to practical performance and deployment efficiency. For professionals, it's a clear signal that bigger isn't always better when choosing an AI model for real-world tasks.
- DeepSeek v4 Pro has 1.6T parameters but fails to achieve top benchmark scores.
- Smaller open models like GLM 5.1 (750B), Kimi K2.6 (1T), and MiniMax M3 (~450B) all outperform it.
- The model's 'preview' status and Huawei-based inference may be more significant than raw parameter count.
Why It Matters
Parameter count alone doesn't guarantee performance; efficiency and inference hardware are becoming the real differentiators.