Best LLMs for Data Analysis in 2026
As of November 13, 2026, the top model among LLMs for data analysis on the BenchLM leaderboard is OrcaCyber-Nano-1.0 with a score of 98.
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.
Full Rankings (26 ranked)
How to choose
Key Takeaways
The top model on this sourced reporting-family slice is OrcaCyber-Nano-1.0 by OrcaRouter with an average of 98.
The best open-weight model is OrcaCyber-Nano-1.0 at position #1.
26 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.
About this ranking
Ranking data as of November 13, 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.
OrcaCyber-Nano-1.0 leads this ranking with a score of 98, followed by Ternary Bonsai 2 27B (97.9) and Sarvam 105B (96). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is OrcaCyber-Nano-1.0 (ranked #1 with a score of 98). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking uses provisional overall weighted scores from the active scoring formula. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.
Questions
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.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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