BenchLM recommendation
Best LLMs for Data Analysis in 2026
As of July 23, 2026, the top model in best llms for data analysis on the BenchLM leaderboard is GLM-4.7 with a score of 88.5.
Last verified: July 23, 2026
Data analysis stresses a specific blend: quantitative accuracy (MATH-500, AIME), discrete reasoning over messy source material (DROP, BBH), and writing correct analysis code (LiveCodeBench). This reporting family weights those benchmarks to rank models for spreadsheet work, statistics, SQL and pandas generation, and interpreting results without arithmetic slips.
This page ranks models by a sourced blend of quantitative, discrete-reasoning, and analysis-coding benchmarks rather than the full provisional leaderboard.
Bottom line: the models that combine near-perfect MATH-500 with strong LiveCodeBench are the safe picks for analysis pipelines — they compute correctly and write the code to prove it.
GLM-4.7 leads this ranking with a score of 88.5, followed by DeepSeek V4 Pro Base (88.3) and DeepSeek V4 Flash Base (88). The top three are separated by just a few points — any of them would perform well for this use case.
All models in this ranking are open-weight, meaning they can be self-hosted for maximum control and cost efficiency.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula tracked by BenchLM.ai. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
How to choose
Statistics and numeric accuracy?
Weight MATH-500 — arithmetic slips are the silent killer
Writing pandas/SQL analysis code?
LiveCodeBench leaders generate the most reliable analysis code
Extracting numbers from documents?
DROP tests discrete reasoning over text directly
Charts, tables, and PDFs?
Pair this ranking with multimodal understanding scores
Full Rankings (6 models)
Key Takeaways
The top model on this sourced reporting-family slice is GLM-4.7 by Z.AI with an average of 88.5.
The best open-weight model is GLM-4.7 at position #1.
6 models are listed with sourced benchmark coverage in this reporting family.
Score in Context
What these scores mean
A reporting-family blend of quantitative benchmarks (MATH-500, AIME), discrete reasoning over text (DROP, BBH), and analysis-code generation (LiveCodeBench) — the capabilities real data work exercises.
Known limitations
No benchmark covers end-to-end analysis workflows (loading messy CSVs, judging statistical validity, choosing the right chart). Long-context and multimodal scores matter too when your data arrives as documents — check those rankings alongside this one.
Best LLMs for Data Analysis FAQ
What is the best LLM for data analysis?
The top rows of this table lead the blend that analysis work stresses — quantitative accuracy, discrete reasoning, and analysis-code generation. For most teams the practical pick is the highest-ranked model whose price fits pipeline volume; check the provider pricing hubs for per-token rates.
Can LLMs do statistics reliably?
The MATH-500 leaders handle standard statistical computation well, and the best practice is to have the model write and run code rather than compute in-context — generated pandas or R is checkable, mental arithmetic is not. Judgment calls like test selection and validity threats still need human review.
What is the best LLM for Excel and spreadsheets?
Spreadsheet work combines formula generation (tracks the coding benchmarks here) with reading tabular layouts (tracks multimodal understanding for screenshots and document understanding for files). Use this table for the reasoning core and cross-check the multimodal rankings if your workflow feeds the model images of sheets.
What is the best LLM for SQL?
SQL generation tracks the LiveCodeBench and general coding leaders closely — see the coding leaderboard for the current top rows. For text-to-SQL over your own schema, prompt quality (including the schema and sample rows in context) usually moves accuracy more than switching between adjacent top models.
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