Gemma 4 31B outperforms larger models on FoodTruck Bench

Benchmark results and analysis

Gemma 4 31B achieved 3rd place on the FoodTruck Bench benchmark, outperforming several larger and more established models. According to the Reddit discussion, the model beat GLM 5, Qwen 3.5 397B, and all Claude Sonnet variants.

The FoodTruck Bench is a benchmark that tests language models on complex, multi-step planning tasks. The original poster speculates that Gemma 4's performance suggests it handles long-horizon tasks better than previous models that failed to complete the benchmark. Specifically, the model appears to effectively listen to its own advice when planning for subsequent steps in the task sequence.

This result is notable because Gemma 4 31B is significantly smaller than some of the models it outperformed. Qwen 3.5 397B, for example, has approximately 12.8 times more parameters than Gemma 4 31B. The performance suggests that model architecture and training approaches may be as important as parameter count for certain types of reasoning tasks.

FoodTruck Bench tests models on practical planning scenarios that require maintaining context over extended sequences of actions. The benchmark's design makes it particularly relevant for developers working with AI agents that need to execute multi-step tasks in real-world applications.