14 best LLMs for data analysis: Model overview, comparison, and use cases for business intelligence

Best large language models for data analysis

Choosing the best LLM for data analysis is no easy task. After all, large language models are complex systems with their own strengths and weaknesses. The generated output you see is heavily based on their training methods, including training data, architecture, and the model weights that guide their behavior. Their reasoning ability, structured output reliability, licensing terms, and suitability for analytical workloads can differ a lot from model to model. Some are great at producing clean SQL, while others shine at summarizing lengthy text passages, interpreting CSV files, or supporting analytical tasks.

The following models represent a cross-section of the most capable systems currently available for business intelligence tasks as of November 2025. Each profile outlines general characteristics and explains how the model performs in data analytics contexts, including its reliability, typical use cases, and potential limitations. This section is designed to help teams understand the practical differences between models before testing them in real scenarios. That way, you can discover the best LLM for data analysis for your workflow.

GPT-5.1 (OpenAI) 

GPT-5.1 is OpenAI’s flagship model in the GPT-5 series, built on the architecture behind ChatGPT. It improves instruction following, reduces reasoning errors, and generates more consistent structured outputs like SQL, JSON, and code. These improvements let teams use GPT-5.1 in analytical workflows that depend on predictable formatting and multi-step logic.

In data analysis, GPT-5.1 summarizes long documents, interprets CSV extracts, and turns natural language questions into well-formed SQL queries and automation-ready outputs. It can also operate in a text-to-text format for lightweight transformations. It identifies trends, explains patterns in sample datasets, and supports analysts who need clear step-by-step reasoning.

Earlier GPT-5 models still work well for high-volume or cost-sensitive tasks, but GPT-5.1 offers the strongest performance for new deployments. Its stability and output control make it suitable for BI workflows where organizations rely on consistent analytical behavior from an LLM. GPT-5.1 models also recognise language patterns that help them interpret analytical questions more consistently.

Claude 4 (Anthropic)

Claude 4.x models are Anthropic’s current generation of large language models, known for careful instruction following, long-context reasoning, and predictable handling of complex inputs. They build on earlier Claude 3 models but offer more reliable output structures and stronger performance on analytical tasks that require cross-referencing large volumes of text. Organizations often choose Claude for use cases that depend on factual stability and transparency in reasoning.

In data analysis workflows, Claude 4 summarizes large reports, interprets structured datasets, and generates clear SQL queries that follow defined formatting rules. It produces stable JSON and table-like outputs, which help teams integrate it into BI pipelines and automation systems. Claude’s extended context window allows it to analyze multi-sheet spreadsheets or long BI exports without losing track of relationships between sections. While the model stays cautious when information is incomplete, this behavior supports analytical environments that prioritize clarity, accuracy, and consistent logical structure.

Claude is particularly strong at generating contextually relevant text, which helps teams maintain clarity in analytical explanations. For teams deciding between Anthropic and OpenAI models, this Claude vs ChatGPT guide takes a closer look at how these tools compare.

Gemini 3 Pro (Google)

Gemini 3 Pro is Google’s current long-context, multimodal model and the core of the Gemini 3 series. It processes large analytical inputs, interprets charts and dashboard screenshots, and handles tasks that blend text, numbers, and visual information. This flexibility helps it manage diverse data types commonly used in BI workflows. Gemini’s context capacity allows teams to work with long analytical documents, multi-sheet spreadsheets, and large exports without losing relationships across sections.

For data analysis, Gemini 3 Pro generates SQL queries, summarizes trends, and produces structured outputs that fit well into reporting workflows. Its multimodal capabilities let it interpret graphs or visual dashboards and integrate those insights into written explanations. Teams use it to automate documentation, describe data transformations, and support exploratory analysis that involves both text and visual elements. 

Deployment availability varies by platform, but Gemini 3 Pro is a strong fit for organizations that work with mixed-format analytical data or rely on models that can reason across multiple modalities. Its architecture allows it to analyse human-like text while also interpreting charts and visual inputs.

Llama 3 / Llama 4 (Meta)

The Llama 3 and Llama 4 families continue Meta’s push to make high-quality open models widely accessible. These models come in several sizes, from lightweight versions suited to edge devices to larger models that handle more complex reasoning. Because the weights are openly available, organizations can run them on-premise, customize them, or fine-tune them to match the language and analytical style of their domain. Llama 3 and 4 are some of the best open source LLMs for data analytics, especially for teams needing private deployments.

For data analysis, Llama models handle everyday tasks reliably: summarizing datasets, generating SQL queries, and turning natural language instructions into structured outputs like JSON or table-formatted text. Teams often fine-tune them to recognize industry-specific terminology or reporting patterns, which gives them an advantage in specialized analytical environments. While Llama models may not reach the same ceiling as the largest proprietary systems, their flexibility, transparency, and deployment control make them a strong fit for organizations that want to build private, customizable analytical pipelines without sending sensitive data to external platforms.

Many of these open-weight models benefit from active contributions from the natural language processing community, which helps improve training datasets, evaluation methods, and downstream tooling.

Mistral Large / Mixtral (Mistral AI)

Mistral Large and the Mixtral models form the core of Mistral AI’s lineup, combining strong reasoning with efficient performance. Mistral Large focuses on high-quality output for complex tasks, while the Mixtral series uses a mixture-of-experts design to offer solid analytical performance at lower compute cost. This makes the family appealing to teams that want capable models they can run locally or scale across internal infrastructure.

In analytical workflows, Mistral models generate SQL queries, clean up messy inputs, and summarize patterns in reports or CSV extracts. Their structured outputs integrate smoothly with BI pipelines, and fine-tuning options let teams adapt them to their domain-specific vocabulary or formatting standards. They’re reliable across routine analytical work, from documentation to exploratory analysis. While very complex reasoning can require extra validation, the balance of speed, cost, and customisation makes the Mistral family a practical choice for organizations building scalable or self-hosted data analysis systems.

Grok 4 (xAI)

Grok 4 is part of xAI’s current model lineup and focuses on fast reasoning and low-latency interaction. It responds quickly to multi-step prompts, which makes it a good fit for analysts who iterate through many query variations or need real-time feedback while exploring a dataset. Its structured outputs have also become more consistent across the 4.x releases, improving how it plugs into reporting tools or automation pipelines.

For data analysis, Grok 4 generates SQL queries from natural language questions, summarizes analytical reports, and explains patterns it finds in structured inputs like tables or CSV files. Its speed makes it especially useful for exploratory work where analysts refine questions on the fly. While it doesn’t offer the longest context window on the market, it performs well in interactive BI workflows that benefit from rapid turnaround. organizations often choose Grok 4 when responsiveness and iterative analysis matter more than running very large context sizes.

Falcon 180B (TII UAE)

Falcon 180B is the largest open-weight model from the Technology Innovation Institute (TII) and remains a well-known option for organizations that want full control over their AI stack. Because it’s openly released and easy to host privately, teams often use Falcon as a foundation for custom analytical workflows or environments where sensitive data cannot leave internal systems. Despite its size, it delivers stable reasoning and predictable behavior across a wide range of text-based tasks.

In data analysis, Falcon 180B handles summarization, SQL generation, and structured outputs like JSON with solid consistency. Its scale helps it spot patterns in datasets, describe trends, and interpret analytical reports without losing context. Although it requires more compute than mid-sized models, Falcon’s open-weight design gives teams the freedom to fine-tune it for domain-specific tasks or integrate it into locked-down data pipelines. For organizations that value openness and on-premise deployment, Falcon 180B is a strong and reliable open source LLM option.

DBRX (Databricks)

DBRX is Databricks’ open-weight mixture-of-experts model, built to slot directly into modern data engineering and analytics ecosystems. Because it comes from a company deeply rooted in the data workflow world, DBRX fits naturally into lakehouse environments where analysts, data scientists, and engineers work with shared datasets and pipelines. Its architecture emphasizes efficiency and strong performance on tasks that blend structured and textual data, drawing on modern language modeling principles.

For analysis tasks, DBRX generates SQL queries, documents data transformations, and produces structured outputs that teams can feed into reporting tools or automation systems. It explains dataset characteristics clearly and supports exploratory analysis where users refine prompts as they learn more about the data. 

Since many organizations already operate on Databricks infrastructure, DBRX often becomes the most seamless option for running LLM-driven analytics without introducing new tooling or external dependencies. It’s a practical, open source choice for teams that want an LLM aligned with their existing data stack. DBRX also performs well on code datasets, which support engineering-heavy analytics environments.

Qwen 2 / Qwen 2.5 (Alibaba Cloud / Qwen Team)

The Qwen 2 and Qwen 2.5 families represent Alibaba’s most widely used open-weight models, known for strong multilingual performance and efficient reasoning. These models gained popularity across APAC enterprises because they run well on a variety of hardware setups and support fine-tuning at a lower cost than many large proprietary systems. The 2.5 updates bring more consistent structured outputs and better performance on analytical tasks.

In data analysis, Qwen models generate SQL queries, summarize reports, and interpret structured datasets with steady reliability. Their JSON and table-style outputs integrate easily with BI pipelines, and their multilingual strengths make them valuable for organizations working across regions or handling datasets in multiple languages. Teams often fine-tune Qwen models on internal terminology or reporting standards to improve accuracy. While they may require domain-specific tuning for specialized analytics, the Qwen family offers a flexible open source path for global teams that need efficient, multilingual analytical support.

DeepSeek-V3 / V3.x (DeepSeek)

DeepSeek-V3 and its later 3.x variants are high-efficiency mixture-of-experts models designed to deliver strong performance while keeping compute costs manageable. DeepSeek focuses on scalability, which makes these models attractive for teams that need throughput, stability, and a predictable price–performance ratio across large analytical workloads. Despite the efficiency focus, the V3 family performs competitively on reasoning and code-generation tasks.

In data analysis, DeepSeek-V3 generates SQL, interprets structured inputs, and produces clean JSON outputs for downstream processes. It summarizes analytical reports, identifies simple patterns within datasets, and supports iterative querying in workflows where users experiment with different prompts. Because of its efficiency, many organizations use DeepSeek-V3 as a general-purpose model for routine analytical work without needing the heavy compute requirements of frontier systems. It can also be fine-tuned to align with internal data structures or specialized reporting language, making it a practical option in data-centric environments.

Yi-34B (01.AI)

Yi-34B is a mid-sized open-weight model from 01.AI that balances solid reasoning performance with manageable deployment requirements. With multilingual support and a permissive license, it appeals to organizations that want a capable model they can fine-tune and host privately without committing to the resource demands of more sprawling large language models. Its size makes it flexible enough for both experimentation and production use in controlled environments.

For data analysis, Yi-34B generates SQL queries, summarizes reports, and interprets structured datasets with predictable behavior. Its structured outputs integrate smoothly into BI pipelines, and teams often fine-tune it to match internal terminology or reporting styles. While it doesn’t compete with frontier-scale models in complex reasoning, Yi-34B offers a reliable and efficient option for organizations that want open source analytical capabilities without large infrastructure overhead. It works well as a stable foundation for private analytical assistants or internal data transformation tools.

Vicuna (Community / UC Berkeley)

Vicuna is an older open-weight model fine-tuned from early Llama releases. It’s still useful for lightweight workloads, experimentation, and simple analytical tasks, but not really competitive with modern large language models used in the data science industry.

Teams sometimes use Vicuna for prototyping, education, or small self-hosted tools that require very low compute.

Alpaca (Stanford / Lightweight Model)

Alpaca is a lightweight, instruction-tuned model created as an academic demonstration. It’s relevant for research and experimentation, but not suitable for modern analytical workloads.

It can still handle simple structured outputs in controlled scenarios, but it falls short of the consistency and reasoning required in contemporary data analysis pipelines.

BLOOM / BLOOMZ (BigScience)

BLOOM and BLOOMZ are community-built models from the BigScience project and remain well known for their openness and multilingual support. While the architecture is older, these models still serve teams that prioritize transparency, reproducibility, or multilingual datasets.

They handle summarization, structured data interpretation, and JSON outputs reliably enough for research or public-sector environments, even if they are not competitive with frontier systems.

Comparison of the top LLMs for data analysis tasks

To make the model landscape easier to navigate, here’s a simple breakdown of how the best large language models for data analysis stack up against each other.

LLM in data analytics use cases

Large language models support a wide range of analytical tasks, especially when teams work with unstructured or mixed-format data. They don’t replace traditional analytics engines, but they streamline the steps around analysis — from preparing data to generating insights. 

Data cleaning and transformation

LLMs turn messy text, logs, and semi-structured inputs into clean JSON, tables, or SQL-ready formats. This reduces manual prep work and helps analysts focus on actual problem-solving.

Summarizing datasets and reports

They condense long exports, spreadsheets, and dashboard snapshots into short explanations that highlight trends and outliers. This saves time and improves how insights are shared across teams.

SQL generation and query refinement

LLMs convert text inputs into clean, well-formed SQL that follows your established formatting rules. They also rewrite or debug existing queries, which helps analysts iterate faster and avoid avoidable errors.

Automating documentation

Models create clear explanations of data transformations, pipeline steps, and analytical logic. This closes the documentation gap that often forms in fast-moving environments.

Exploratory analysis

LLMs help analysts test ideas, explore datasets, and identify early patterns before deeper modeling starts. They also support simple sentiment analysis tasks when needed, speeding up the preliminary evaluation phase of “what’s here?”.

Forecasting support

LLMs assist with early forecasting steps by turning historical data into trend descriptions or generating draft models that analysts can refine. This gives teams a faster starting point for projecting future scenarios.

Data visualization and reporting

They generate narrative text for reports, produce leadership-friendly summaries in natural language, and explain changes visible in charts or dashboards. Check our LLM benchmarks overview for more performance insights.

If you want to streamline these workflows, nexos.ai offers AI for data analysis, a space where you can clean data,  reports, generate SQL, and more. You can easily switch between GPT-5.1, Claude, Gemini, and others within the nexos.ai AI workspace — so your team can get insights faster.

How to choose a suitable LLM for your data analysis needs

Choosing the right model depends less on raw benchmark scores and more on how well an LLM fits into your data workflows. Finding the best AI LLM for data analysis for your team starts with knowing what it can do — and what it can’t.

Different teams value different things — consistency, privacy, deployment control, multilingual support, or the ability to handle large, messy inputs. Below are the criteria most businesses look at when evaluating which model works best for their analytical environment.

Structured output reliability

For most analytical tasks, stable JSON, tables, and SQL matter more than creativity. Models that reliably follow formatting rules reduce the time spent fixing broken outputs or re-running prompts.

SQL generation quality

If your team relies heavily on querying, look for models that produce well-formed SQL without hallucinating table names or changing logic. Consistency here directly affects analyst productivity.

Ability to work with large or complex inputs

Some workloads involve long reports, multi-sheet spreadsheets, logs, or dashboard snapshots. Models with strong long-context performance handle these better and retain relationships across sections.

Integration and deployment options

Businesses that handle sensitive data often prefer open-weight models they can host privately. Others choose hosted APIs for convenience and speed. The right choice depends on your security, compliance, and infrastructure requirements.

Performance and cost balance

Frontier models usually deliver the best reasoning, but cost-sensitive teams often get more value from smaller or open-weight models. If you’re comparing models side-by-side, the compare AI models feature on nexos.ai can help evaluate trade-offs. If you’re testing a few models side by side, the compare AI models feature on nexos.ai lets you run the same prompt across multiple LLMs and see how they differ in real workflows

Multilingual or domain-specific needs

Global teams and industry-specific environments benefit from models that understand regional terminology, technical language, or multilingual datasets. Fine-tuning can also improve accuracy on specialised tasks.

Speed and interactivity

For exploratory analysis, responsiveness matters. Faster models help analysts iterate quickly, especially when refining queries or testing ideas in real time.

Ultimately, the best LLM is the one that fits your workflow, security model, and the type of analysis your team performs most often.

Advantages and limitations of using LLMs for data analysis

LLMs are excellent tools for speeding up analytical work. They make complex and data-heavy tasks easier to handle, especially for teams that work with unstructured or mixed-format data. 

Advantages

Faster workflows

LLMs reduce the time spent on cleaning data, drafting SQL, summarizing reports, and preparing documentation.

Ease of use

Analysts and non-technical users can explore data, ask questions, and generate insights through natural language instead of code.

Democratized access to analysis

Teams across the organisation can participate in analytical tasks without relying solely on specialised skills or tooling.

Flexible input handling

Models work with text, logs, tables, and dashboard snapshots that traditional tools struggle to parse.

Limitations

Accuracy and hallucination risks

LLMs can produce confident but incorrect outputs, especially when data is ambiguous or incomplete.

Data sensitivity

Some organisations can’t send internal data to hosted APIs, which limits deployment options or requires self-hosting.

Not a substitute for statistical models

LLMs support forecasting and trend exploration, but they don’t replace rigorous predictive modeling.

Cost and performance trade-offs

Frontier models offer better reasoning but may increase cost or latency depending on workload.

So which LLM is best for data analysis? As you can see, it depends on the type of work your team does! If you’re exploring how different models perform side by side — or want a single place to use them securely — nexos.ai brings GPT, Claude, Gemini, Mistral, Llama, and other models together in one workspace. It’s a simple way to test, compare, and put LLMs to work in your analysis stack.