Muse Spark Leads LLM Calorie Benchmark
π‘A small benchmark shows why task-specific testing can overturn size-based LLM rankings.
β‘ 30-Second TL;DR
What Changed
Muse Spark 1.3 had the highest accuracy, with 48% of meals within 20% error.
Why It Matters
The results suggest that practitioners should benchmark models on their actual multimodal task rather than select by parameter count or general reputation. However, the small sample size means the rankings should be treated as directional rather than definitive.
What To Do Next
Reproduce the test with at least 100 meals from Nutrition5k and your target cuisine before choosing a local vision-language model for calorie estimation.
Key Points
- β’Muse Spark 1.3 had the highest accuracy, with 48% of meals within 20% error.
- β’DeepSeek v4 Flash Vision reached 40% within the target error range, while Qwen 3.8 27b reached only 16%.
- β’Muse Glimmer 30b outperformed Qwen 3.8 27b despite a similar consumer-hardware footprint.
- β’The evaluation used 25 randomly selected Nutrition5k meals and nutrition data from USDA FoodData Central and MEXT.
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Original source: Reddit r/LocalLLaMA β
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