9+ Best LLMs for Data Analysis for 2026

The best LLM for data analysis can save you hours of manual work.

Instead of writing complex SQL queries, cleaning datasets, building charts, and digging through spreadsheets yourself, you can ask an AI model questions in plain English and get actionable insights in seconds.

The problem is that not every large language model performs well on data analysis tasks.

Some models are excellent at analytical reasoning and statistical analysis. Others shine when generating Python code, writing SQL queries, exploring datasets, creating data visualizations, or working with business intelligence workflows.

With new models launching constantly, figuring out which one is actually worth using has become harder than ever.

I spent time researching and comparing the leading LLMs across real-world data analysis tasks, including data exploration, spreadsheet analysis, code generation, reporting, forecasting, and insight discovery.

In this guide, you'll find the 9+ best LLMs for data analytics for 2026. Many teams use LLMs to leverage these capabilities. This summary covers who they're best for, where they fall short, and the primary use case for each model in your workflow.

Top LLMs for Data Analysis: Comparison

To help you choose the best LLM for data analysis, we compared the top models. We evaluated reasoning, coding, analytics, pricing, and real-world performance.

Best LLMs for Data Analysis

Several large language models now perform well on data analysis tasks. However, their strengths vary significantly depending on the type of work involved.

Below is a detailed comparison of the top LLMs for data analysis for 2026, based on real-world performance across reasoning, code generation, and practical analytics workflows.

Claude Opus 4.8

If you need strong depth in data analysis, Claude Opus 4.8 is one of the top choices right now. It scored 78.34 on the LiveBench Data Analysis task. It performs at a high level on complex reasoning and long-context tasks. In testing on real projects, it delivered more consistent multi-step analysis than most other models.

Key features

• Reaches approximately 80.8% on SWE-bench, verified for coding and agentic tasks
• Handles long context windows effectively for large reports and datasets
• Generates reliable Python and SQL code for data pipelines
• Produces structured outputs that are easy to review and use
• Works well with detailed prompts for iterative data exploration
• Performs strongly on both structured data and unstructured data
• Maintains quality across multi-step analytical workflows

Pricing: $5 per million input tokens and $25 per million output tokens (standard API).

Best for: Data analysts who need depth and reliability on complex data analysis workflows.

GPT-5.5

GPT-5.5 currently leads several data analysis benchmarks when used in Thinking mode. Many analysts consider GPT-5.5 the best LLM for data analysis, particularly for benchmark performance and interactive analytics.

It scored 81.08 on the LiveBench Data Analysis task, the highest among tested models. This makes it one of the strongest options if you want measurable performance on analytical work.

Key features

• Scored 81.08 on LiveBench Data Analysis (highest in recent results)
• Strong integration with code execution and file handling in ChatGPT
• Converts natural language prompts into accurate Python and SQL code
• Handles multi-file analysis and iterative questioning effectively
• Delivers clear actionable insights from large datasets
• Works well with both structured data and unstructured data
• Balances speed and reasoning quality across different data analysis tasks

Pricing: $5 per million input tokens and $30 per million output tokens.

Best for: Data analysts who want strong benchmark performance in interactive data analysis.

Gemini 3.1 Pro

Gemini 3.1 Pro is one of Google’s strongest models for data analysis involving visual and multimodal data. It performs well when working with charts, dashboards, documents, and large datasets, especially within the Google Cloud ecosystem.

Key features

• Supports context windows of up to 1 million tokens, enabling analysis of very large documents and datasets
• Effectively processes and interprets charts, graphs, and visual reports
• Integrates natively with BigQuery, Vertex AI, and other Google Cloud services
• Generates code and structured insights from both text and visual data inputs
• Performs competitively on applied statistics and technical data interpretation tasks
• Handles multimodal inputs, including text, images, PDFs, and structured files

Pricing: Usage-based API pricing through Google AI Studio or Vertex AI (typically around $2–$4 input / $12–$18 output, depending on context length).

Best for: Data analysts who work with visual data, large documents, and Google Cloud tools.

Grok 4.3

Grok 4.3 provides balanced performance on real-world data analysis tasks. It handles messy datasets and maintains context across longer sessions. You get straightforward results without unnecessary complexity.

Key features

• Strong results on reasoning and data interpretation benchmarks
• Supports large context windows for full dataset reviews
• Delivers clear and direct outputs for quick decisions
• Handles charts and documents in the same workflow
• Reliable in multi-step analytical pipelines
• Competitive speed on varied data analysis tasks
• Includes real-time capabilities when data sources change frequently

Pricing: $1.25 per million input tokens and $2.50 per million output tokens.

Best for: Data analysts who want balanced, practical performance in data analysis.

MiniMax M3

MiniMax M3 ranked first in independent real-world testing on Google Analytics data with broken attribution. It achieved 100/100 accuracy while being one of the fastest and lowest-cost options. This makes it very effective when you run high volumes of data analysis.

Key features

• Ranked #1 in real-world GA4 benchmark with 100/100 accuracy
• Delivered results in approximately 70 seconds on average
• Maintained consistency across multiple test runs
• Good at detecting data quality issues and suggesting alternatives
• Supports agentic workflows for automated analysis
• Returns actionable insights at very low cost per query
• Scales well for high-volume daily data analysis work

Pricing: $0.30 per million input tokens and $1.20 per million output tokens (for inputs up to 512K).

Best for: Data analysts running high-volume data analysis where cost and speed matter.

Kimi K2.6

Kimi K2.6 is Moonshot AI’s main multimodal and agentic model (released April 2026). It stands out for long-horizon tasks, strong coding capabilities, and agent swarm features. If your data analysis involves complex workflows, multiple data sources, or multi-step processes, this model performs very well.

Key features

• Strong performance on long-horizon coding and agentic workflows
• Supports multimodal inputs (text + vision)
• Features agent swarm capabilities for coordinating multiple sub-agents
• Produces clean structured outputs suitable for reports and pipelines
• Handles complex, multi-step data analysis tasks effectively
• Good instruction following and consistency on demanding prompts
• Competitive results on agentic and reasoning benchmarks

Pricing: Approximately $0.95 per million input tokens and $4 per million output tokens.

Best for: Data analysts running complex, multi-step data analysis with agentic or long-horizon needs.

Qwen3.7-Max

Qwen3.7-Max delivers strong coding and agentic performance at a competitive price. It generates reliable code and structured outputs while supporting multilingual data. Many teams use it when they need scalable results without high costs.

Key features

• Strong agentic execution across data pipelines and workflows
• Creates accurate Python and SQL code for data transformation
• Supports multilingual datasets and global analytics work
• Consistent structured outputs for business intelligence
• Scales efficiently for high-volume use
• Strong reasoning on data analysis tasks
• Integrates cleanly into existing workflows

Pricing: Cost-efficient usage-based API pricing on Alibaba Cloud.

Best for: Scalable data analysis where coding quality and cost efficiency matter.

DeepSeek-V4-Pro

DeepSeek-V4-Pro combines high reasoning performance with strong coding capabilities. It supports long context and works well for statistical and modeling work. The open-weight option gives you flexibility if you prefer self-hosted setups.

Key features

• High performance on reasoning, coding, and statistical benchmarks
• Supports large context windows for big dataset processing
• Strong agentic features for multi-step analytical workflows
• Reliable Python code generation for data modeling
• Open-weight versions available for self-hosted use
• Competitive results against proprietary models on many tasks
• Handles diverse data types with consistent output quality

Pricing: Approximately $0.435 per million input tokens and $0.87 per million output tokens.

Best for: Data scientists who need strong reasoning with flexible deployment options.

GLM-5.1

GLM-5.1 gives reliable performance on coding and structured data tasks. It works well in self-hosted environments where you need control over data and deployment. You get solid results for analytical pipelines without high complexity.

Key features

• Competitive results on coding and agentic data tasks
• Produces clean structured outputs for reports and systems
• Strong option for self-hosted and sensitive data environments
• Handles data transformation and query generation effectively
• Maintains consistency across repeated analytical runs
• Integrates well into custom pipelines
• Good value when control and cost efficiency are priorities

Pricing: Usage-based API and self-hosted options. See Zhipu AI for current rates.

Best for: Self-hosted data analysis where you want reliable coding support.

Llama 4

Llama 4 provides capable open-weight performance for data analysis. It supports coding, reasoning, and fine-tuning so you can adapt it to your specific needs. This makes it useful when privacy, customization, or long-term infrastructure costs are important.

Key features

• Strong open-weight results on coding and reasoning tasks
• Supports fine-tuning for domain-specific data work
• Full control over data access and deployment
• Reliable Python and query generation capabilities
• Scales for internal analytics platforms and automated pipelines
• Competitive with closed models on many data analysis tasks
• Suitable for building customized data analysis tools

Pricing: Open-weight model. You only pay for your own infrastructure.

Best for: Self-hosted data analysis where customization and control matter most.

Bonus: A Quick Way to Test Multiple LLMs

If you want to test several of these LLMs without managing multiple subscriptions, you can use Aymo AI. It is an all-in-one platform that gives you access to many leading models in a single workspace.

This can be useful when you want to compare the outputs of 2–3 models on the same task. It also includes team features and usually costs less than subscribing to individual model plans separately.

Key Features

• Access to 45+ LLM models (including GPT-5.5, Claude, Gemini, DeepSeek, Grok, and others) in a single workspace
• Team collaboration features with shared workspaces and team memory
• File upload and analysis support (PDFs, documents, code, etc.)
• Ability to compare outputs from multiple models using the same prompt
• Private and secure workspaces
• Bring Your Own Key (BYOK) support in higher plans

Pricing (as of June 2026): Paid plan starts from $4/month

Conclusion

The best LLM for data analysis depends on your needs. Some models excel at deep analysis, while others offer better coding, multimodal features, lower costs, or greater deployment flexibility.

• Claude Opus 4.8 currently delivers the strongest results when complex reasoning and reliable multi-step analysis are required.
• GPT-5.5 provides the most consistent balance across interactive data work and benchmark performance.
• MiniMax M3 offers the strongest value for high-volume analysis at a significantly lower cost.
• Other models, including Gemini 3.1 Pro, Grok 4.3, and Kimi K2.6, each perform well in specific scenarios.

The only dependable way to identify the best option for your work is to test the leading models directly on your own datasets and workflows. The model that produces accurate insights, clean code, and actionable outputs on your data is the one worth adopting.

You can try all the models in Aymo AI, an all-in-one AI platform that gives access to multiple models in a single workspace at a lower cost than individual subscriptions, along with team features. It can help you get more clarity when you need to test 2–3 models to complete your work.