Best AI for Data Analysis (2026): Models Ranked for Excel, CSV, and Research

The best AI model for data analysis depends on what "data analysis" means for you. Summarizing a CSV? Any model works. Building regression models from messy data? You need frontier intelligence and large context windows.

Update (September 26, 2026): The scores below are measured at each model's maximum reasoning effort. At the settings people actually run, Claude Opus 5.5 still leads (53.6), MiMo-V2.6-Pro is the best value, and Grok 4.7 answers fastest. Our live research and data analysis ranking shows those picks with real costs per task, thinking tokens included.

We ranked the major models for four data analysis scenarios on intelligence, cost and context window. Quality scores come from the Artificial Analysis (AA) Intelligence Index v4.3.2, which weights agentic tasks, coding, general knowledge work, and scientific reasoning. AA rebased the index in September 2026, so every score here runs lower than the ones you saw earlier in the year. Here's what works in September 2026.

Quick Picks

Monthly costs assume 100 tasks at roughly 15K input and 20K output tokens each, reasoning included.

Full Rankings for Data Analysis

We left out superseded models. Opus 5 (51) and Fable 5 (50) cost more than Opus 5.5 and score lower. GPT-5.6 Sol (47) and Luna (37) match their GPT-6 replacements within a point at twice the price or more.

The top of the table isn't where the value is. Opus 5.5 beats Fable 5.1 and GPT-6 Astra by five points at 40% of their price, and below it, MiMo-V2.6-Pro scores 46 for less than a tenth of Opus 5.5's price.

Scenario 1: CSV and Excel Analysis

Most data analysis starts with a CSV or spreadsheet. You need a model that can:

• Parse tabular data accurately
• Calculate aggregates (sums, averages, percentiles)
• Identify patterns and outliers
• Generate charts or visualization code

Best pick: GPT-6 Luna ($0.60/1M total)

For structured data tasks, you rarely need frontier intelligence. Luna handles CSV parsing, basic statistics, and pandas code generation at $0.10/$0.50 per million tokens. It scores the same 37 as GPT-5.6 Luna at less than half the price. The 1.05M context window (OpenAI's model docs list the same limit across GPT-6) means you can feed it large datasets directly.

For bigger datasets, use our JSON to CSV converter to pre-process API data, then feed the CSV to the model.

Budget alternative: GLM-5.3 Flash ($0.65/1M total)

GLM-5.3 Flash scores five points higher than Luna (42 vs 37) for about the same price ($0.15/$0.50). It's slow, though. At ~43 tok/s it runs at a third of Luna's speed. Good for batch analysis where you're running the same query across many files overnight, not for interactive work.

Scenario 2: Statistical Analysis and Modeling

Multi-step reasoning, regression analysis, hypothesis testing, and building models from data.

Best pick: Claude Opus 5.5 ($24/1M total)

Statistical analysis requires the model to keep calculations accurate across many steps. Opus 5.5 is #1 on the AA Intelligence Index at 58, and it costs less than Opus 5 did ($4/$20 vs $5/$25). It's the strongest choice for:

• Building regression models
• A/B test analysis with significance testing
• Time series forecasting
• Complex multi-variable analysis

Watch the effort setting. The 58 is at max effort. The default is medium, which scores 51, and high scores 54. For stats work where one wrong intermediate number ruins the answer, set it to high or max.

Value alternative: GPT-6 Sol ($12/1M total)

GPT-6 Sol scores 48 at half of Opus 5.5's price, and runs faster (~110 vs ~90 tok/s). For most statistical work that doesn't need the absolute top model, Sol delivers. Grok 4.7 (46) is cheaper again at $2/$6, but slow at ~40 tok/s.

Scenario 3: Research and Document Synthesis

Reading multiple PDFs, synthesizing findings, extracting data points across sources.

Best pick: Muse Spark 1.3 ($5.50/1M total)

Meta's Muse Spark 1.3 matches GPT-6 Sol's 48 at less than half the price, and at ~219 tok/s it's one of the fastest frontier-class models on AA. It takes text, image, and video input with a 1M context window, so charts and figures inside scanned papers come along with the text.

When accuracy matters most: Claude Opus 5.5 ($24/1M total)

If the synthesis has to hold up in front of a reviewer, step up to Opus 5.5. Ten points higher on the index, 1M context, and 128K max output for long write-ups.

Every model in the top 10 except Grok 4.7 (500K) takes at least 1M tokens. Past that, chunk the documents and synthesize in passes.

Scenario 4: Data Visualization and Reporting

Generating charts, dashboards, and formatted reports from raw data.

Best pick: GPT-6 Sol or Muse Spark 1.3

Both generate clean Python (matplotlib, plotly) and JavaScript (D3, Chart.js) visualization code, and they sit close on AA's Coding Agent Index (57 and 54). Muse Spark is less than half the price ($5.50 vs $12/1M total) and about twice as fast. It also accepts images, so you can paste a screenshot of a chart you want to reproduce.

How to Use AI for Data Analysis (Workflow)

1. Prepare data. Clean your CSV/JSON using our data tools. Remove duplicates, handle missing values.

2. Upload to AI. Paste the data directly (for small datasets) or describe the schema and provide a sample (for large ones).

3. Ask specific questions. "What is the correlation between columns X and Y?" works better than "analyze this data."

4. Iterate. Start with exploration, then drill into specific findings.

5. Generate code. Ask the model to produce Python/R code for reproducible analysis rather than just answers.

Context Window: Why It Matters for Data

For datasets larger than your model's context window, use chunking: process in batches and aggregate results. Or ask the model to write pandas/SQL code that processes the full dataset locally.

Free AI for Data Analysis

DeepSeek R1 (Free via OpenRouter)

Has reasoning capabilities, handles basic statistical questions, and generates Python code. Limitations:

• 64K context (small datasets only)
• Slower response times
• Less reliable on complex multi-step calculations

For students, personal projects, or prototyping analysis pipelines, R1 is surprisingly capable.

Google AI Studio (Free tier of Gemini)

Rate-limited but free access to Gemini models. Good for interactive data exploration during development.

Our Recommendation

For most data analysis work in 2026:

• Daily analysis: GPT-6 Sol or Muse Spark 1.3. Both score 48 at a reasonable cost.
• Simple CSV tasks: GPT-6 Luna. Cheapest capable option.
• Complex research: Claude Opus 5.5 at high or max effort. When accuracy matters most.
• High volume: GLM-5.3 Flash or MiMo-V2.6-Pro. Pennies per query. If you need speed too, DeepSeek V4.1 Flash scores 39 at ~232 tok/s for $0.30/$1.20.

Compare all models for your specific data workflow in our LLM Leaderboard or AI Model Comparison Chart, filter by "Analysis" task type to see relevant rankings. For the full "which model is best" question beyond data analysis, see Best LLM in 2026. To cut the bill further, read our guide to reducing AI API costs.