Benchmark Almanac

SpreadsheetBench 2

arito 54.5% tops its leaderboard Claude Opus 4.6 34.9% leads the models we track In use

Agents complete 321 expert-annotated business spreadsheet workflows, such as financial modeling, debugging, and charting, in multi-sheet workbooks. Scoring is task accuracy.

In the authors’ words

Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edits, and therefore fail to capture end-to-end workflows in realistic business settings. We introduce SpreadsheetBench 2, a workflow-level benchmark for spreadsheet agents that covers three task categories: generation, debugging, and visualization. The benchmark is constructed from authentic business data, including financial reports and corporate filings, and is annotated and validated by domain experts. The benchmark contains 321 tasks; each instance averages 11.8 worksheets and requires 593.5 cell modifications, reflecting large multi-sheet workbooks with cross-sheet dependencies. We evaluate eight frontier large language models under a unified multi-turn agent scaffold, and additionally include several LLM-based spreadsheet products as complementary baselines. Results show that current systems remain far from reliable on real-world workflows: the best model achieves 34.89% overall task accuracy, and debugging accuracy is as low as 12.00%. Trajectory analysis and a failure taxonomy further indicate that insufficient spreadsheet inspection and incorrect target-cell selection are the dominant bottlenecks. Together, these findings position SpreadsheetBench 2 as a challenging testbed for advancing reliable spreadsheet automation. Project page: https://spreadsheetbench.github.io/

Abstract of SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows (arXiv:2606.29955, CC0)
Made by Renmin University of China (RUCKBReasoning) Released Jun 2026 Documents and spreadsheets All tasks public Last in a launch post Sep 2026 Last model added Sep 2026 Scores refreshed 2 Nov 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
11
Entries on its own leaderboard, 8 models we track
2
Launch posts list it, from 2 labs
1
System card reports it, from 1 lab

arito tops the leaderboard with 54.5%

Best score per entry from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders. The best model we track is Claude Opus 4.6, at 34.9%.

The newest model with a score here came out in Feb 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 arito 54.5%
  2. 2 WPS AI 50.9%
  3. 3 Claude Opus 4.6 34.9%
  4. 4 Decide Agent 2.0 33.8%
  5. 5 GPT-5.2 26.8%
  6. 6 Gemini 3.1 Pro 23.7%
  7. 7 GLM-5 17.1%
  8. 8 DeepSeek-V3.2 15.6%
  9. 9 Kimi K2.5 14.6%
  10. 10 Qwen3.5 397B-A17B 11.2%
  11. 11 MiniMax-M2.5 7.17%
#EntryScoreSettingDateSource
1 arito 54.5% Sep 2026 Official leaderboard
2 WPS AI 50.9% Aug 2026 Official leaderboard
3 Claude Opus 4.6 34.9% SWE-agent Feb 2026 Official leaderboard
4 Decide Agent 2.0 33.8% Aug 2026 Official leaderboard
5 GPT-5.2 26.8% SWE-agent Dec 2025 Official leaderboard
6 Gemini 3.1 Pro 23.7% SWE-agent Feb 2026 Official leaderboard
7 GLM-5 17.1% SWE-agent Feb 2026 Official leaderboard
8 DeepSeek-V3.2 15.6% SWE-agent Dec 2025 Official leaderboard
9 Kimi K2.5 14.6% SWE-agent Jan 2026 Official leaderboard
10 Qwen3.5 397B-A17B 11.2% SWE-agent Feb 2026 Official leaderboard
11 MiniMax-M2.5 7.17% SWE-agent Feb 2026 Official leaderboard

Labs report scores for 2 models in their cards. We plot every source in the charts below.

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.

Anthropic Moonshot AI
Own leaderboard Not tested since Jun 2026
Lab claims 1 leader
Kimi K3 34.8%

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 4.6 GPT-5.2 DeepSeek-V3.2 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 4.6 GPT-5.2

2 labs put it in a launch post in the last six months

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Moonshot AI
StepFun
DateLaunch postScoreAs the post writes it
20 Sep 2026 Step 5 Preview 29.4 SpeadSheet v2
16 Jul 2026 Kimi K3 34.8 SpreadsheetBench 2

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
27 Jul 2026 Kimi K3 technical report 34.8 SpreadsheetBench 2 (Table 2) Capabilities

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