When you type "show me monthly revenue by region" into an AI dashboard tool, something complex happens behind the scenes. The AI model must understand your business intent, identify the correct data columns, select an appropriate chart type, and generate valid configuration. Different models handle these tasks very differently. Some get the chart type right but use wrong columns. Others understand the intent but produce invalid output. We wanted real answers. So we tested 12 popular models across 32 real-world business scenarios. We designed 32 test scenarios covering common business analytics requests: Basic charts: Bar, line, pie, area for standard KPIs Multi-dimensional analysis: Grouped comparisons, stacked charts Time-series analysis: Trends over time, period comparisons Conditional formatting: Thresholds, color rules Multilingual prompts: Requests in Turkish and English Complex queries: Multi-step analysis with follow-up questions Each model was scored on correctness (right chart type and data mapping), completeness (all requested elements included), speed (response time), and stability (consistent results across similar prompts). Response time matters for interactive use. If users wait more than 10 seconds, engagement drops. Model CPU (no GPU) GPU / Apple Silicon Gemma 4 E2B ~5s ~1.5s Mistral 7B ~6s ~2s Qwen 2.5 7B ~7s ~2s Llama 3.1 8B ~8s ~2s Qwen 3 8B ~10s ~3s Gemma 4 E2B is clearly the fastest — ideal for interactive chart creation where speed matters more than the last few percent of accuracy. Model Correct Wrong Partial Llama 3.1 8B 28/32 2 2 Qwen 2.5 7B 27/32 3 2 Qwen 3 8B 26/32 3 3 Gemma 4 E2B 25/32 4 3 Mistral 7B 24/32 5 3 Llama 3.1 8B leads in accuracy. But the gap between top models is narrowing with each release. For teams working in multiple languages, this matters a lot. Model Turkish Prompts English Prompts Overall Qwen 2.5 7B 26/32 27/32 Best multilingual Qwen 3 8B 25/32 26/32 Strong multilingual Llama 3.1 8B 22/32 28/32 English-first Gemma 4 E2B 20/32 25/32 Weaker Turkish Mistral 7B 19/32 24/32 Weaker Turkish Qwen models clearly dominate multilingual scenarios. If your team works in Turkish, Arabic, or other non-English languages, Qwen should be your first choice. "Show me revenue by region" could be a bar chart, pie chart, or treemap. The best models infer the most appropriate visualization type from context. Some models generate conversational text when chart configuration is expected. Others produce partial configurations that break rendering. This is the most common failure mode. Common patterns: Misidentifying date columns as categorical data Generating chart types that don't match data structure Failing to handle empty or null values Confusing similar column names Better models handle these edge cases gracefully. Some models produce different charts for identical prompts across sessions. For production dashboard generation, consistency is essential. Use Case Recommended Model Why Turkish/multilingual dashboards Qwen 2.5 7B Best multilingual support Fast interactive use Gemma 4 E2B Quickest response time Maximum accuracy Llama 3.1 8B Highest chart correctness Balanced performance Qwen 3 8B Good accuracy + multilingual Lightweight deployment Mistral 7B Low hardware requirements Models that struggled with chart generation six months ago now produce acceptable results. Within the next year, most mainstream models will handle business chart generation competently. For now, selecting the right model for your specific use case still matters. We built LivChart to make local AI dashboards accessible. Try different models and see which works best for your data.