Excel Modeling Benchmark v1.1
Agents build the same 103 Excel financial modeling tasks as EMB v1.0, with the Scratch rubric judge changed to GPT-6 Luna.
Vals says v1.1 scores are not directly comparable with v1.0. Part of Vals Index v2.2 from 2026-10-19. Claude Fable 5.1 led 37 models at 76.73% on 2026-10-29.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
Claude Fable 5.1 leads with 76.7%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
- 76.7 Claude Fable 5.1
- 76.5
Gemini 4 Carbon
- 75.9 Claude Sonnet 5.5
- 75.8 Claude Opus 5.5
- 75.0
Muse Wonder Full
- 74.8
Gemini 4 Argon
- 73.3 GPT-6.1 Terra
- 72.9
Gemini 3.8 Flash
- 72.7 GPT-6 Astra
- 72.5
Muse Wonder
- 72.0
Qwen3.8 Max (0902)
- 71.5 Grok 4.7
- 71.3 Kimi K3
- 71.1 GPT-6.1 Sol
- 70.0 Kimi K3.1 Pro
- 70.0 GPT-6 Luna
- 69.5
Step 5 Preview
- 69.2
DeepSeek V4.1 Flash 1017
- 69.1 GLM-5.3
- 69.0
Mistral Large 4
- 69.0
Tencent Hy4 preview
- 68.9 MiMo-V2.6 Flash
- 68.7
Mistral Large 4 (Le Chonk, open weights)
- 67.8
MiniMax-M3.1-Flash
- 67.8
DeepSeek V4.1 Flash
- 67.3 MiMo-V2.6 Pro
- 66.3 GLM-5.3-Flash
- 66.0
Qwen 3.8 Max
- 65.5 Claude Haiku 5.5
- 65.1
DeepSeek V4 Pro 0813
- 64.7 Beam 1.0
- 62.0
Muse Spark Open
- 59.8
MiniMax-M3
- 57.9 Mercury 3
- 52.8
Nemotron 3.5 Super VL
- 41.0
Inkling
- 40.8
Inkling-Small
- 1 Claude Fable 5.1 76.7%
- 2
Gemini 4 Carbon 76.5%
- 3 Claude Sonnet 5.5 75.9%
- 4 Claude Opus 5.5 75.8%
- 5
Muse Wonder Full 75%
- 6
Gemini 4 Argon 74.8%
- 7 GPT-6.1 Terra 73.3%
- 8
Gemini 3.8 Flash 72.9%
- 9 GPT-6 Astra 72.7%
- 10
Muse Wonder 72.5%
- 11
Qwen3.8 Max (0902) 72%
- 12 Grok 4.7 71.5%
- 13 Kimi K3 71.3%
- 14 GPT-6.1 Sol 71.1%
- 15 Kimi K3.1 Pro 70%
- 16 GPT-6 Luna 70%
- 17
Step 5 Preview 69.5%
- 18
DeepSeek V4.1 Flash 1017 69.2%
- 19 GLM-5.3 69.1%
- 20
Mistral Large 4 69%
- 21
Tencent Hy4 preview 69%
- 22 MiMo-V2.6 Flash 68.9%
- 23
Mistral Large 4 (Le Chonk, open weights) 68.7%
- 24
MiniMax-M3.1-Flash 67.8%
- 25
DeepSeek V4.1 Flash 67.8%
- 26 MiMo-V2.6 Pro 67.3%
- 27 GLM-5.3-Flash 66.3%
- 28
Qwen 3.8 Max 66%
- 29 Claude Haiku 5.5 65.5%
- 30
DeepSeek V4 Pro 0813 65.1%
- 31 Beam 1.0 64.7%
- 32
Muse Spark Open 62%
- 33
MiniMax-M3 59.8%
- 34 Mercury 3 57.9%
- 35
Nemotron 3.5 Super VL 52.8%
- 36
Inkling 41%
- 37
Inkling-Small 40.8%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Fable 5.1 | 76.7% | Sep 2026 | Official leaderboard | |
| 2 | | 76.5% | Oct 2026 | Official leaderboard | |
| 3 | Claude Sonnet 5.5 | 75.9% | Sep 2026 | Official leaderboard | |
| 4 | Claude Opus 5.5 | 75.8% | Sep 2026 | Official leaderboard | |
| 5 | | 75% | Oct 2026 | Official leaderboard | |
| 6 | | 74.8% | Sep 2026 | Official leaderboard | |
| 7 | GPT-6.1 Terra | 73.3% | Oct 2026 | Official leaderboard | |
| 8 | | 72.9% | Sep 2026 | Official leaderboard | |
| 9 | GPT-6 Astra | 72.7% | Sep 2026 | Official leaderboard | |
| 10 | | 72.5% | Oct 2026 | Official leaderboard | |
| 11 | | 72% | Sep 2026 | Official leaderboard | |
| 12 | Grok 4.7 | 71.5% | Sep 2026 | Official leaderboard | |
| 13 | Kimi K3 | 71.3% | Jul 2026 | Official leaderboard | |
| 14 | GPT-6.1 Sol | 71.1% | Sep 2026 | Official leaderboard | |
| 15 | Kimi K3.1 Pro | 70% | Oct 2026 | Official leaderboard | |
| 16 | GPT-6 Luna | 70% | Sep 2026 | Official leaderboard | |
| 17 | | 69.5% | Sep 2026 | Official leaderboard | |
| 18 | | 69.2% | Oct 2026 | Official leaderboard | |
| 19 | GLM-5.3 | 69.1% | Aug 2026 | Official leaderboard | |
| 20 | | 69% | Oct 2026 | Official leaderboard | |
| 21 | | 69% | Aug 2026 | Official leaderboard | |
| 22 | MiMo-V2.6 Flash | 68.9% | Sep 2026 | Official leaderboard | |
| 23 | | 68.7% | Oct 2026 | Official leaderboard | |
| 24 | | 67.8% | Oct 2026 | Official leaderboard | |
| 25 | | 67.8% | Sep 2026 | Official leaderboard | |
| 26 | MiMo-V2.6 Pro | 67.3% | Sep 2026 | Official leaderboard | |
| 27 | GLM-5.3-Flash | 66.3% | Aug 2026 | Official leaderboard | |
| 28 | | 66% | Aug 2026 | Official leaderboard | |
| 29 | Claude Haiku 5.5 | 65.5% | Oct 2026 | Official leaderboard | |
| 30 | | 65.1% | Aug 2026 | Official leaderboard | |
| 31 | Beam 1.0 | 64.7% | Oct 2026 | Official leaderboard | |
| 32 | | 62% | Oct 2026 | Official leaderboard | |
| 33 | | 59.8% | Jun 2026 | Official leaderboard | |
| 34 | Mercury 3 | 57.9% | Oct 2026 | Official leaderboard | |
| 35 | | 52.8% | Oct 2026 | Official leaderboard | |
| 36 | | 41% | Jul 2026 | Official leaderboard | |
| 37 | | 40.8% | Jul 2026 | Official leaderboard |
The best score, 76.7%, costs $16 a task
Runs on the cost frontier score higher than any run that's cheaper. GPT-6.1 Terra comes within 3.4 points of the best for $0.56 a task, 28 times cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
Versions
We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.
More in Law, finance and medicine
- HealthBench Professional In use 525 physician-authored tasks from real clinician chats (care consult, documentation, research) with physician-adjudicated rubrics. Claude Sonnet 5.5 69.2%
- MedXpertQA-MM In use Models answer expert-level medical exam questions that combine images with clinical records and test results. Scoring is accuracy.
Qwen 3.8 Max 80.4%
- HealthBench In use 5,000 realistic health conversations graded against physician-written rubrics. A length-adjusted variant penalizes verbosity. GPT-5 67.2%
- KBL In use Models answer Korean legal knowledge and reasoning tasks and Korean bar exam questions. It has 7 knowledge tasks, 4 reasoning tasks and 2,510 bar exam items.
Solar Pro 4 77.5%
- KorMedMCQA In use Models answer 7,469 multiple-choice questions from Korean licensing exams for doctors, nurses, pharmacists and dentists, held from 2012 to 2024.
Solar Pro 4 93.2%
- PRBench In use Model answers 1,100 expert-written law and finance questions. LLM judge grades against weighted expert rubrics of 10-30 criteria each.
Muse Spark 1.3 61.6%