Llama 4 Maverick can write SQL queries, generate pandas code, and interpret datasets. Here's how it performs on real data analysis tasks and when you should consider alternatives via LLMWise.
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Llama 4 Maverick is a capable data analysis assistant for routine tasks like SQL generation, pandas transformations, and basic statistical analysis. Its self-hosting capability is a significant advantage for teams working with sensitive datasets that cannot leave their infrastructure. For complex statistical reasoning, insight generation, and nuanced data interpretation, GPT-5.2 and Claude Sonnet 4.5 produce more reliable and insightful results.
Self-host Maverick to run analysis on confidential financial, healthcare, or customer data without sending it to external APIs. This satisfies compliance requirements that make cloud LLM APIs a non-starter.
Maverick reliably generates correct SQL queries and pandas transformation code for common data manipulation tasks including joins, aggregations, pivots, window functions, and data cleaning operations.
Train Maverick on your specific database schemas, table relationships, and common query patterns. This eliminates the need to include lengthy schema descriptions in every prompt and improves query accuracy.
When processing thousands of data analysis requests daily, such as automated report generation or data quality checks, self-hosted Maverick keeps costs fixed regardless of volume.
Maverick sometimes applies incorrect statistical tests, misinterprets p-values, or draws unsupported causal conclusions from correlational data. GPT-5.2 and Claude are more reliable for statistical interpretation.
When asked to interpret results and generate business insights, Maverick tends to restate numbers rather than surface meaningful patterns. Claude and GPT-5.2 produce more actionable analytical narratives.
For highly nested subqueries, recursive CTEs, or queries spanning many tables with complex join conditions, Maverick produces more syntax errors and logic mistakes than frontier closed models.
Include your database schema and sample data in the system prompt so Maverick generates accurate queries against your actual tables.
Use LLMWise Compare mode to test Maverick against GPT-5.2 on your most common analysis queries to identify where quality differences matter.
For automated pipelines, add SQL validation and dry-run steps to catch syntax errors before executing Maverick-generated queries against production databases.
Fine-tune on your team's historical SQL queries and analysis notebooks to teach Maverick your conventions and common patterns.
Route insight generation and executive summary tasks to Claude Sonnet 4.5 while using Maverick for high-volume data transformation code.
How Llama 4 Maverick stacks up for data analysis workloads based on practical evaluation.
GPT-5.2
Compare both models for data analysis on LLMWise
You only pay credits per request. No monthly subscription. Paid credits never expire.
Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.