Top picks for ETL Scripting (2026)
Writing extract-transform-load pipelines. Ranked from 340 live models on the OpenRouter catalog, weighted for reasoning quality, context window, structured output.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 162 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | OpenAI: GPT-5openai/gpt-5 | 161 | $1.25 | $10.00 | 400,000 | Details → |
| 3 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 160 | $5.00 | $25.00 | 1,000,000 | Details → |
| 4 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 160 | $5.00 | $25.00 | 1,000,000 | Details → |
| 5 | OpenAI: o3openai/o3 | 149 | $2.00 | $8.00 | 200,000 | Details → |
| 6 | OpenAI: GPT-4.1openai/gpt-4.1 | 139 | $2.00 | $8.00 | 1,047,576 | Details → |
| 7 | Google: Gemini 2.5 Progoogle/gemini-2.5-pro | 138 | $1.25 | $10.00 | 1,048,576 | Details → |
| 8 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 135 | $0.30 | $2.50 | 1,048,576 | Details → |
| 9 | DeepSeek: DeepSeek V3deepseek/deepseek-chat | 130 | $0.20 | $0.80 | 131,072 | Details → |
| 10 | OpenAI: o4 Mini Highopenai/o4-mini-high | 129 | $1.10 | $4.40 | 200,000 | Details → |
| 11 | Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 129 | $0.15 | $0.60 | 1,048,576 | Details → |
| 12 | Anthropic: Claude Sonnet 4anthropic/claude-sonnet-4 | 128 | $3.00 | $15.00 | 1,000,000 | Details → |
| 13 | Qwen: Qwen3.7 Plusqwen/qwen3.7-plus | 128 | $0.40 | $1.60 | 1,000,000 | Details → |
| 14 | MiniMax: MiniMax M3minimax/minimax-m3 | 128 | $0.30 | $1.20 | 1,048,576 | Details → |
| 15 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 128 | $1.50 | $9.00 | 1,048,576 | Details → |
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How we ranked these
For ETL Scripting, we weight models on reasoning quality, context window, structured output. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About ETL Scripting
ETL scripting is writing code that extracts data from source systems, transforms it according to business logic, and loads it into target databases or data warehouses. You need this whenever you're building automated data pipelines that run on schedules or real-time triggers. Good models generate syntactically correct code with proper error handling, connection pooling, and schema validation. Bad models produce scripts that ignore data type mismatches, skip null checks, or create inefficient joins that timeout on large datasets. A practical concern: models often generate verbose logging code that doubles pipeline runtime, so you need to audit generated code for performance bottlenecks before deployment.
When to use: Use this when you need to move data between systems automatically-like syncing customer records from your CRM into your analytics database, or preparing raw logs for reporting.
Common questions
What is the best AI model for writing production-grade ETL pipelines?
Claude 3.5 Sonnet and GPT-4 both handle ETL scripting well, but Sonnet is faster for boilerplate generation and costs less. For complex transformation logic with multiple data sources, GPT-4 produces more robust error handling and edge-case coverage, though with higher latency and cost.
How much faster can an AI model write ETL code versus doing it manually?
Models typically generate working scaffolding 5-10x faster than manual coding, cutting initial development from hours to minutes. However, you should budget 20-40% additional time for security review, performance testing, and credential handling adjustments before production deployment.