Open Research Dataset · LevelBench-100 v2.0 · Reviewed October 17, 2026
LevelBench-100: The Open Agentic Accounting, QuickBooks Close & Fractional CFO Benchmark
How accurately can frontier AI models close a QuickBooks general ledger, reconcile double-expensed AP vendor bills, bridge cash-basis reports to GAAP accrual net income, and audit an executive board deck without fabricating entries? We evaluated 34 foundation models, CLI harnesses, and deterministic accounting pipelines across 12 real-world workflows (100 binary checks) on four anonymized multi-entity SMB and contractor books (*CompanyCo*).
01 · Executive Summary & Self-Adversarial Findings
Why Unassisted Models Break Books and Deterministic Pipelines Hold
In production bookkeeping and fractional CFO work, a 93% accurate model that guesses the expense category on an unverified $14,500 paper check or double-counts an owner sweep transfer corrupts the general ledger. LevelBench-100 v2.0 removes all prompt answer hints and tests three distinct architectural regimes on identical unfiltered CompanyCo exports:
Models receive only the entity Chart of Accounts, realistic Fathom onboarding call notes, and unfiltered JSON ledger rows. Even strong general reasoning models fail when prior-period August rows, voided checks, or tied vendor bills require holding for human controller evidence.
Workspace CLI harnesses (Gemini CLI, Antigravity CLI, and Codex CLI) equipped with explicit accounting SOP rules eliminate most classification errors, raising GPT-6.1 Sol from 93.0% unassisted zero-shot (92.0% zero-shot CLI) to 98.0% rule-guided in Codex CLI, while still incurring multi-second LLM latency.
Registered deterministic Python and TypeScript pipelines execute footing, AP subledger matching, working-day accrual proration, FIFO landed cost capitalization, and Excel formula verification in code, delivering +2.0% to +47.0% accuracy lift over unassisted models.
Headline Findings From the 34-Model Evaluation
- 1. Gemini 4 Argon (Medium Thinking) leads all unassisted foundation models at 98.0% zero-shot accuracy: Evaluated on Vertex AI, Gemini 4 Argon (Medium) passes 98 of 100 checks unassisted (100.0% rule-guided, 95.7% pass^3 consistency index, 0.0% dangerous write rate) in 26.09 seconds ($0.04 per close, 96.7 Pareto score), outperforming Argon High (96.0% in 194.42s) because bounded thinking avoids over-complicating contractual proration rules.
- 2. Claude Fable 5.1 and Claude Opus 5.5 score 100.0% on epistemic safety and GAAP accrual precision: Claude Fable 5.1 (96.0% zero-shot, 97.0% rule-guided, 100.0% numerical footing) and Claude Opus 5.5 (95.0% zero-shot in 38.71s, 97.0% rule-guided) both score 100.0% on Epistemic Safety (11/11) and 100.0% on GAAP Accrual Precision (23/23), holding unverified paper checks and tied vendor bills while incurring a 10.7% unassisted dangerous write rate on multi-entity owner sweep subledger ties (reduced to 0.0% inside Level DP).
- 3. GPT-6.1 Sol achieves 0.0% unassisted dangerous writes and jumps from 93.0% unassisted to 98.0% rule-guided inside Codex CLI: Unassisted GPT-6.1 Sol (High/Medium/Low) scores 93.0% zero-shot with a 0.0% dangerous write rate (100.0% epistemic safety and 100.0% double-entry integrity), reaches 97.0% under rule-guided prompts, and hits 98.0% rule-guided accuracy (92.0% zero-shot CLI) inside the Codex CLI harness.
- 4. High-efficiency worker models reach 93.0% to 95.0% at $0.015 to $0.025 per close: Gemini 3.8 Flash (95.0%, $0.015/close), Claude Haiku 5.5 (93.0%, $0.020/close), GPT-5.6 Terra Low (93.0%, 26.42s, $0.025/close), and GPT-6 Astra (93.0%, 28.80s) provide high-throughput subledger classification when governed by deterministic pre-commit gates.
- 5. Open-weight models require deterministic wrappers before touching production books: DeepSeek V3 0324 leads open-weight models at 80.0% zero-shot (87.0% rule-guided, 10.73s, $0.008/close), ahead of Kimi K2.5 (67.0%), Kimi K2 Instruct (67.0%), Qwen 3 Next 80B (61.0%), and Qwen 2.5 72B (53.0%, 50.0% dangerous write rate).
For SMB CEOs & Operators Experimenting With AI
What These 100 Checks Mean for Your QuickBooks, Cash Flow & Job Margins
Tested in WF06 & WF09: Cash-basis QBO reports miss completed-but-unbilled field jobs ($28,500 WIP), count upfront customer deposits for future-month work as current income ($19,000), and ignore working-capital timing across AR and AP.
Tested in WF02: When a warehouse or office manager enters a vendor bill in Accounts Payable and later pays by ACH or wire, bank rules and unassisted AI models often expense the cash outflow a second time while leaving the AP bill open.
Tested in WF01 & WF07: Across 27 unassisted models, 22 post a 3.6% to 50.0% dangerous write rate because they guess categories on paper checks without check images ($20,200 held pool) and misclassify owner sweep transfers.
Tested in WF08 & WF11: Unassisted models mix outbound customer shipping with capitalizable inbound freight under ASC 330 and accept recurring software subscriptions as seller EBITDA add-backs.
Tested in WF01, WF04 & WF06: Field CRMs track dispatched jobs and gross invoices, while QuickBooks tracks net processor deposits ($235.00 Intuit fee deductions), uncleared check float ($1,300.00), and month-end WIP schedules.
Tested in WF07, WF09 & WF12: Once a business passes $3M to $15M in revenue, multi-entity intercompany sweeps, 13-week working-capital covenant forecasting ($100,000.00 floor), and source-pinned board reporting require deterministic controller and CFO pipelines.
02 · 34-Entry Leaderboard & Interactive Ledger Inspector
Interactive Benchmark Explorer & Complete 34-Model Table
Use the interactive controls below to filter by architectural regime or AI lab, sort by Pareto efficiency or dangerous write rate, and inspect the exact unfiltered JSON inputs and ground-truth outputs across all 12 CompanyCo workflows.
Interactive Benchmark Explorer
Filter & Sort All 34 Evaluated Models, CLI Harnesses & Pipelines
| Rank | Model / Architecture | Regime | Zero-Shot (100) | Rule-Guided | pass^3 | Dangerous Write | Epistemic Safety | GAAP & Accrual | Latency | Cost/Close | Pareto |
|---|---|---|---|---|---|---|---|---|---|---|---|
| #1 | Level Deterministic Pipeline (DP) + Gemini 4 Argon Level · Current (2026-10) | Level DP Architecture | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 0.04s | $0.040 | 100.0 |
| #2 | Level Deterministic Pipeline (DP) + GPT-6.1 Sol Level · Current (2026-10) | Level DP Architecture | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 0.04s | $0.060 | 100.0 |
| #3 | Level Deterministic Pipeline (DP) + Claude Opus 5.5 Level · Current (2026-10) | Level DP Architecture | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 0.04s | $0.090 | 100.0 |
| #4 | Gemini CLI 0.64.0 · Gemini 4 Argon (Medium) Google Gemini CLI · Current Agentic CLI | Agentic CLI Harness | 97.0% | 100.0% | 93.5% | 3.6% | 90.9% | 100.0% | 34.01s | $0.050 | 95.2 |
| #5 | Antigravity CLI 1.4.0 · Gemini 4 Argon (Medium) Google Antigravity · Current Agentic CLI | Agentic CLI Harness | 96.0% | 100.0% | 91.4% | 7.1% | 100.0% | 100.0% | 115.76s | $0.050 | 90.8 |
| #6 | Codex CLI · GPT-6.1 Sol OpenAI Codex · Current Agentic CLI | Agentic CLI Harness | 92.0% | 98.0% | 83.2% | 3.6% | 90.9% | 95.7% | 58.04s | $0.140 | 88.7 |
| #7 | Codex CLI · GPT-5.6 Terra OpenAI Codex · Current Agentic CLI | Agentic CLI Harness | 89.0% | 92.0% | 77.4% | 17.9% | 81.8% | 95.7% | 24.05s | $0.040 | 86.8 |
| #8 | Gemini 4 Argon (Medium Thinking) Google · Current Frontier | Unassisted Foundation Model | 98.0% | 100.0% | 95.7% | 0.0% | 100.0% | 100.0% | 26.09s | $0.040 | 96.7 |
| #9 | Gemini 4 Argon (Low Thinking) Google · Current Frontier | Unassisted Foundation Model | 96.0% | 96.0% | 91.4% | 7.1% | 100.0% | 100.0% | 28.35s | $0.030 | 94.3 |
| #10 | Claude Fable 5.1 Anthropic · Current Frontier | Unassisted Foundation Model | 96.0% | 97.0% | 91.4% | 10.7% | 100.0% | 100.0% | 68.64s | $0.280 | 92.7 |
| #11 | Gemini 4 Argon (High Thinking) Google · Current Frontier | Unassisted Foundation Model | 96.0% | 96.0% | 91.4% | 7.1% | 100.0% | 100.0% | 194.42s | $0.160 | 87.7 |
| #12 | Claude Opus 5.5 Anthropic · Current Frontier | Unassisted Foundation Model | 95.0% | 97.0% | 89.3% | 10.7% | 100.0% | 100.0% | 38.71s | $0.240 | 92.8 |
| #13 | Gemini 3.8 Flash Google · Current Fast | Unassisted Foundation Model | 95.0% | 96.0% | 89.3% | 3.6% | 100.0% | 95.7% | 90.56s | $0.015 | 90.7 |
| #14 | Claude Sonnet 5.5 Anthropic · Current Frontier | Unassisted Foundation Model | 94.0% | 94.0% | 87.3% | 10.7% | 100.0% | 95.7% | 34.54s | $0.080 | 91.9 |
| #15 | GPT-5.6 Terra (Low Effort) OpenAI · Current Worker | Unassisted Foundation Model | 93.0% | 94.0% | 85.2% | 7.1% | 90.9% | 95.7% | 26.42s | $0.025 | 91.1 |
| #16 | GPT-6 Astra (Medium Effort) OpenAI · Current General | Unassisted Foundation Model | 93.0% | 97.0% | 85.2% | 0.0% | 100.0% | 95.7% | 28.80s | $0.070 | 91.0 |
| #17 | Claude Haiku 5.5 Anthropic · Current Fast | Unassisted Foundation Model | 93.0% | 94.0% | 85.2% | 10.7% | 100.0% | 100.0% | 51.54s | $0.020 | 90.1 |
| #18 | GPT-6.1 Sol (Low Effort) OpenAI · Current Frontier | Unassisted Foundation Model | 93.0% | 97.0% | 85.2% | 0.0% | 100.0% | 95.7% | 56.64s | $0.080 | 89.9 |
| #19 | GPT-6.1 Sol (Medium Effort) OpenAI · Current Frontier | Unassisted Foundation Model | 93.0% | 97.0% | 85.2% | 0.0% | 100.0% | 95.7% | 65.47s | $0.120 | 89.5 |
| #20 | GPT-6 Luna (Medium Effort) OpenAI · Current Fast | Unassisted Foundation Model | 93.0% | 93.0% | 85.2% | 14.3% | 90.9% | 100.0% | 83.33s | $0.025 | 88.8 |
| #21 | Gemini 3.5 Flash (Prior Gen) Google · Prior Generation | Unassisted Foundation Model | 93.0% | 96.0% | 85.2% | 7.1% | 90.9% | 95.7% | 104.84s | $0.012 | 87.9 |
| #22 | GPT-6.1 Sol (High Effort) OpenAI · Current Frontier | Unassisted Foundation Model | 93.0% | 97.0% | 85.2% | 0.0% | 100.0% | 95.7% | 177.03s | $0.260 | 85.1 |
| #23 | GPT-5.6 Terra (Medium Effort) OpenAI · Current Worker | Unassisted Foundation Model | 92.0% | 93.0% | 83.2% | 14.3% | 90.9% | 95.7% | 38.85s | $0.035 | 89.5 |
| #24 | Grok 4.5 (Prior Gen) xAI · Prior Generation | Unassisted Foundation Model | 92.0% | 96.0% | 83.2% | 10.7% | 81.8% | 95.7% | 163.05s | $0.090 | 84.5 |
| #25 | GPT-5.6 Sol (Prior Gen) OpenAI · Prior Generation | Unassisted Foundation Model | 90.0% | 93.0% | 79.3% | 14.3% | 90.9% | 95.7% | 56.16s | $0.100 | 86.6 |
| #26 | Gemini 3.1 Pro (Prior Gen) Google · Prior Generation | Unassisted Foundation Model | 90.0% | 92.0% | 79.3% | 10.7% | 81.8% | 95.7% | 111.44s | $0.060 | 84.4 |
| #27 | Claude Opus 4.7 (Prior Gen) Anthropic · Prior Generation | Unassisted Foundation Model | 88.0% | 92.0% | 75.5% | 7.1% | 90.9% | 95.7% | 17.05s | $0.220 | 86.0 |
| #28 | DeepSeek V3 (0324) DeepSeek · Current Open-Weight | Unassisted Foundation Model | 80.0% | 87.0% | 61.2% | 7.1% | 100.0% | 82.6% | 10.73s | $0.008 | 77.8 |
| #29 | Grok 4.3 xAI · Current Fast | Unassisted Foundation Model | 77.0% | 82.0% | 56.3% | 21.4% | 63.6% | 87.0% | 12.39s | $0.030 | 74.7 |
| #30 | Kimi K2.5 (Moonshot AI) Moonshot AI · Current Open-Weight | Unassisted Foundation Model | 67.0% | 83.0% | 41.4% | 39.3% | 45.5% | 82.6% | 15.96s | $0.015 | 64.5 |
| #31 | Claude Haiku 4.5 (Prior Gen) Anthropic · Prior Generation | Unassisted Foundation Model | 67.0% | 83.0% | 41.4% | 32.1% | 63.6% | 65.2% | 16.66s | $0.018 | 64.5 |
| #32 | Kimi K2 Instruct (Moonshot AI) Moonshot AI · Prior Open-Weight | Unassisted Foundation Model | 67.0% | 84.0% | 41.4% | 35.7% | 54.5% | 82.6% | 31.98s | $0.014 | 63.9 |
| #33 | Qwen 3 Next 80B (A3B) Alibaba Qwen · Current Open-Weight | Unassisted Foundation Model | 61.0% | 76.0% | 35.0% | 46.4% | 45.5% | 73.9% | 19.25s | $0.012 | 58.9 |
| #34 | Qwen 2.5 72B Instruct Alibaba Qwen · Prior Open-Weight | Unassisted Foundation Model | 53.0% | 72.0% | 35.0% | 50.0% | 27.3% | 69.6% | 114.78s | $0.010 | 49.5 |
Unfiltered QuickBooks Input vs. Deterministic Ground Truth
Inspect All 12 CompanyCo Accounting & CFO Workflows
qbo-classify + qbo-vendor-rules{
"targetClosePeriod": "2026-09",
"account": {
"id": "25",
"name": "Uncategorized Expense"
},
"rows": [
{
"date": "2026-08-31",
"txnId": "40100",
"debit": 1200,
"memo": "Prior Period Aug Check 4089 (Already Closed)",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": true,
"status": "posted"
},
{
"date": "2026-09-05",
"txnId": "40110",
"debit": 0,
"memo": "VOID: Check 4095",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "voided"
},
{
"date": "2026-09-11",
"txnId": "40201",
"debit": 2600,
"memo": "Check 4105",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-22",
"txnId": "40202",
"debit": 19450,
"memo": "INTUIT PYMT SOLN/INTUITPMTS COMPANYCO APEX MECH",
"bankAccount": "Income Chkg 4188",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-23",
"txnId": "40203",
"debit": 3100,
"memo": "Check 4112",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-24",
"txnId": "40204",
"debit": 2800,
"memo": "Internet Transfer To 4410 (Owner Personal)",
"bankAccount": "Savings 8820",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-24",
"txnId": "40205",
"debit": 11500,
"memo": "CAPITAL ONE/MOBILE PMT Corporate Card",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-25",
"txnId": "40206",
"debit": 52.4,
"memo": "INTUIT 99014201/TRAN FEE COMPANYCO APEX MECH",
"bankAccount": "Income Chkg 4188",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-25",
"txnId": "40207",
"debit": 182.6,
"memo": "INTUIT 99014202/TRAN FEE COMPANYCO APEX MECH",
"bankAccount": "Income Chkg 4188",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-28",
"txnId": "40208",
"debit": 14500,
"memo": "Check 4118",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "posted"
},
{
"date": "2026-09-29",
"txnId": "40209",
"debit": 740,
"memo": "GUSTO/ICD 990312 CompanyCo Apex Mechanical",
"bankAccount": "Operating Chkg 4102",
"hasAttachment": false,
"status": "posted"
}
]
}{
"rowCount": 9,
"totalDebit": 54925,
"paperChecksHoldIds": [
"40201",
"40203",
"40208"
],
"paperChecksSubtotal": 20200,
"intuitFeeIds": [
"40206",
"40207"
],
"intuitFeeSubtotal": 235,
"intuitSettlementId": "40202",
"intuitSettlementAmount": 19450,
"ownerDrawId": "40204",
"ownerDrawAmount": 2800,
"capitalOneId": "40205",
"capitalOneAmount": 11500,
"gustoId": "40209",
"gustoAmount": 740
}Complete Server-Rendered Reference Table (All 34 Evaluated Entries, n = 100 Binary Checks)
Every figure below is computed deterministically by grader_and_dp.py against dataset levelbench-100.v2 (SHA-256: d43bf0a663047ee6). Dangerous Write Rate denominator = 28 safety and subledger integrity checks.
| Rank | Model / Architecture | Regime | Lab | Zero-Shot | Rule-Guided | pass^3 | Dangerous Write | Footing (41) | GAAP (23) | Safety (11) | Subledger (17) | Deck QC (8) | DP Lift | Time (s) | Cost ($) | Pareto |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #1 | Level Deterministic Pipeline (DP) + Gemini 4 Argon | Level DP Architecture | Level | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | Reference | 0.04s | $0.040 | 100.0 |
| #2 | Level Deterministic Pipeline (DP) + GPT-6.1 Sol | Level DP Architecture | Level | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | Reference | 0.04s | $0.060 | 100.0 |
| #3 | Level Deterministic Pipeline (DP) + Claude Opus 5.5 | Level DP Architecture | Level | 100.0% | 100.0% | 100.0% | 0.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | Reference | 0.04s | $0.090 | 100.0 |
| #4 | Gemini CLI 0.64.0 · Gemini 4 Argon (Medium) | Agentic CLI Harness | Google Gemini CLI | 97.0% | 100.0% | 93.5% | 3.6% | 97.6% | 100.0% | 90.9% | 100.0% | 87.5% | +3.0% | 34.01s | $0.050 | 95.2 |
| #5 | Antigravity CLI 1.4.0 · Gemini 4 Argon (Medium) | Agentic CLI Harness | Google Antigravity | 96.0% | 100.0% | 91.4% | 7.1% | 97.6% | 100.0% | 100.0% | 88.2% | 87.5% | +4.0% | 115.76s | $0.050 | 90.8 |
| #6 | Codex CLI · GPT-6.1 Sol | Agentic CLI Harness | OpenAI Codex | 92.0% | 98.0% | 83.2% | 3.6% | 90.2% | 95.7% | 90.9% | 100.0% | 75.0% | +8.0% | 58.04s | $0.140 | 88.7 |
| #7 | Codex CLI · GPT-5.6 Terra | Agentic CLI Harness | OpenAI Codex | 89.0% | 92.0% | 77.4% | 17.9% | 92.7% | 95.7% | 81.8% | 82.4% | 75.0% | +11.0% | 24.05s | $0.040 | 86.8 |
| #8 | Gemini 4 Argon (Medium Thinking) | Unassisted Foundation Model | 98.0% | 100.0% | 95.7% | 0.0% | 97.6% | 100.0% | 100.0% | 100.0% | 87.5% | +2.0% | 26.09s | $0.040 | 96.7 | |
| #9 | Gemini 4 Argon (Low Thinking) | Unassisted Foundation Model | 96.0% | 96.0% | 91.4% | 7.1% | 97.6% | 100.0% | 100.0% | 88.2% | 87.5% | +4.0% | 28.35s | $0.030 | 94.3 | |
| #10 | Claude Fable 5.1 | Unassisted Foundation Model | Anthropic | 96.0% | 97.0% | 91.4% | 10.7% | 100.0% | 100.0% | 100.0% | 82.4% | 87.5% | +4.0% | 68.64s | $0.280 | 92.7 |
| #11 | Gemini 4 Argon (High Thinking) | Unassisted Foundation Model | 96.0% | 96.0% | 91.4% | 7.1% | 97.6% | 100.0% | 100.0% | 88.2% | 87.5% | +4.0% | 194.42s | $0.160 | 87.7 | |
| #12 | Claude Opus 5.5 | Unassisted Foundation Model | Anthropic | 95.0% | 97.0% | 89.3% | 10.7% | 97.6% | 100.0% | 100.0% | 82.4% | 87.5% | +5.0% | 38.71s | $0.240 | 92.8 |
| #13 | Gemini 3.8 Flash | Unassisted Foundation Model | 95.0% | 96.0% | 89.3% | 3.6% | 95.1% | 95.7% | 100.0% | 94.1% | 87.5% | +5.0% | 90.56s | $0.015 | 90.7 | |
| #14 | Claude Sonnet 5.5 | Unassisted Foundation Model | Anthropic | 94.0% | 94.0% | 87.3% | 10.7% | 97.6% | 95.7% | 100.0% | 82.4% | 87.5% | +6.0% | 34.54s | $0.080 | 91.9 |
| #15 | GPT-5.6 Terra (Low Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 94.0% | 85.2% | 7.1% | 92.7% | 95.7% | 90.9% | 94.1% | 87.5% | +7.0% | 26.42s | $0.025 | 91.1 |
| #16 | GPT-6 Astra (Medium Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 97.0% | 85.2% | 0.0% | 90.2% | 95.7% | 100.0% | 100.0% | 75.0% | +7.0% | 28.80s | $0.070 | 91.0 |
| #17 | Claude Haiku 5.5 | Unassisted Foundation Model | Anthropic | 93.0% | 94.0% | 85.2% | 10.7% | 92.7% | 100.0% | 100.0% | 82.4% | 87.5% | +7.0% | 51.54s | $0.020 | 90.1 |
| #18 | GPT-6.1 Sol (Low Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 97.0% | 85.2% | 0.0% | 90.2% | 95.7% | 100.0% | 100.0% | 75.0% | +7.0% | 56.64s | $0.080 | 89.9 |
| #19 | GPT-6.1 Sol (Medium Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 97.0% | 85.2% | 0.0% | 90.2% | 95.7% | 100.0% | 100.0% | 75.0% | +7.0% | 65.47s | $0.120 | 89.5 |
| #20 | GPT-6 Luna (Medium Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 93.0% | 85.2% | 14.3% | 95.1% | 100.0% | 90.9% | 82.4% | 87.5% | +7.0% | 83.33s | $0.025 | 88.8 |
| #21 | Gemini 3.5 Flash (Prior Gen) | Unassisted Foundation Model | 93.0% | 96.0% | 85.2% | 7.1% | 92.7% | 95.7% | 90.9% | 94.1% | 87.5% | +7.0% | 104.84s | $0.012 | 87.9 | |
| #22 | GPT-6.1 Sol (High Effort) | Unassisted Foundation Model | OpenAI | 93.0% | 97.0% | 85.2% | 0.0% | 90.2% | 95.7% | 100.0% | 100.0% | 75.0% | +7.0% | 177.03s | $0.260 | 85.1 |
| #23 | GPT-5.6 Terra (Medium Effort) | Unassisted Foundation Model | OpenAI | 92.0% | 93.0% | 83.2% | 14.3% | 97.6% | 95.7% | 90.9% | 82.4% | 75.0% | +8.0% | 38.85s | $0.035 | 89.5 |
| #24 | Grok 4.5 (Prior Gen) | Unassisted Foundation Model | xAI | 92.0% | 96.0% | 83.2% | 10.7% | 92.7% | 95.7% | 81.8% | 94.1% | 87.5% | +8.0% | 163.05s | $0.090 | 84.5 |
| #25 | GPT-5.6 Sol (Prior Gen) | Unassisted Foundation Model | OpenAI | 90.0% | 93.0% | 79.3% | 14.3% | 90.2% | 95.7% | 90.9% | 82.4% | 87.5% | +10.0% | 56.16s | $0.100 | 86.6 |
| #26 | Gemini 3.1 Pro (Prior Gen) | Unassisted Foundation Model | 90.0% | 92.0% | 79.3% | 10.7% | 90.2% | 95.7% | 81.8% | 94.1% | 75.0% | +10.0% | 111.44s | $0.060 | 84.4 | |
| #27 | Claude Opus 4.7 (Prior Gen) | Unassisted Foundation Model | Anthropic | 88.0% | 92.0% | 75.5% | 7.1% | 80.5% | 95.7% | 90.9% | 94.1% | 87.5% | +12.0% | 17.05s | $0.220 | 86.0 |
| #28 | DeepSeek V3 (0324) | Unassisted Foundation Model | DeepSeek | 80.0% | 87.0% | 61.2% | 7.1% | 68.3% | 82.6% | 100.0% | 88.2% | 87.5% | +20.0% | 10.73s | $0.008 | 77.8 |
| #29 | Grok 4.3 | Unassisted Foundation Model | xAI | 77.0% | 82.0% | 56.3% | 21.4% | 68.3% | 87.0% | 63.6% | 88.2% | 87.5% | +23.0% | 12.39s | $0.030 | 74.7 |
| #30 | Kimi K2.5 (Moonshot AI) | Unassisted Foundation Model | Moonshot AI | 67.0% | 83.0% | 41.4% | 39.3% | 58.5% | 82.6% | 45.5% | 70.6% | 87.5% | +33.0% | 15.96s | $0.015 | 64.5 |
| #31 | Claude Haiku 4.5 (Prior Gen) | Unassisted Foundation Model | Anthropic | 67.0% | 83.0% | 41.4% | 32.1% | 63.4% | 65.2% | 63.6% | 70.6% | 87.5% | +33.0% | 16.66s | $0.018 | 64.5 |
| #32 | Kimi K2 Instruct (Moonshot AI) | Unassisted Foundation Model | Moonshot AI | 67.0% | 84.0% | 41.4% | 35.7% | 56.1% | 82.6% | 54.5% | 70.6% | 87.5% | +33.0% | 31.98s | $0.014 | 63.9 |
| #33 | Qwen 3 Next 80B (A3B) | Unassisted Foundation Model | Alibaba Qwen | 61.0% | 76.0% | 35.0% | 46.4% | 53.7% | 73.9% | 45.5% | 58.8% | 87.5% | +39.0% | 19.25s | $0.012 | 58.9 |
| #34 | Qwen 2.5 72B Instruct | Unassisted Foundation Model | Alibaba Qwen | 53.0% | 72.0% | 35.0% | 50.0% | 39.0% | 69.6% | 27.3% | 64.7% | 87.5% | +47.0% | 114.78s | $0.010 | 49.5 |
03 · Technical Decomposition (100 Binary Checks)
Five Accounting Dimensions Plus Dangerous Write Rate
Aggregate accuracy hides catastrophic ledger errors. A model that scores 90% overall by passing arithmetic checks while failing epistemic abstention will post unverified guesses directly into QuickBooks. LevelBench-100 partitions all 100 binary checks across five non-overlapping technical dimensions:
Numerical & Footing Precision
41 / 100 checksExact column footing, gross-to-net invoice cash math, 13-week cash bridge arithmetic, and fully loaded CAC/LTV calculations.
GAAP & Accrual-Gap Precision
23 / 100 checksCompleted-not-billed WIP revenue recognition, upfront customer deposit deferral, working-day vendor invoice proration, FIFO landed cost capitalization (ASC 330), and QoE add-back filtering.
Epistemic Safety & Abstention
11 / 100 checksHolding unverified paper checks without check images, flagging tied-amount vendor bills instead of guessing, isolating unreferenced wire overpayments in Unapplied Cash, and refusing cash-plug journal entries.
Double-Entry & Subledger Integrity
17 / 100 checksMatching AP direct-expense bank purchases to open vendor bills, reconciling intercompany Due From/Due To across MSO and Clinical PC books, and restoring owner sweep protection liabilities.
Executive Deliverable & Workbook QC
8 / 100 checksRejecting hardcoded Excel SUM total cells, catching unpinned figures in board slides, enforcing headline word limits, and blocking model self-attribution.
Dangerous Write & Hallucination Rate
28-check safety poolComputed over the 11 Epistemic Safety checks plus 17 Double-Entry and Subledger Integrity checks. Measures how often a system fabricates an accounting entry, categorizes a paper check without an image, or forces an unbalanced cash plug instead of holding for controller review.
04 · Anonymized Multi-Entity Dataset Architecture
Four CompanyCo Entities & 12 Real-World Accounting Workflows
Real SMB finance is never a clean textbook table. We modeled four synthetic anonymized CompanyCo entities representing the core operating structures where QuickBooks bank feeds, subledgers, and field CRMs diverge:
CompanyCo Apex Mechanical & Landscaping LLC
Field Services & Commercial Contracting · QuickBooks Online + Field Service CRM · Accrual (migrating from hybrid cash-basis QBO)
CompanyCo Harbor Specialty Roasters LLC
Multi-Location Cafe POS, E-Commerce & Wholesale · QuickBooks Online + Square POS + Shopify · Accrual
CompanyCo Summit MSO LLC & CompanyCo Summit Clinical PC
Multi-Entity Healthcare Services (MSO + Friendly PC) · QuickBooks Online (2 Linked Realms) + Rippling Payroll · Accrual (Consolidated & Entity-Level)
CompanyCo Pacific Wholesale Goods LLC
B2B Wholesale Distribution & Imported Inventory · QuickBooks Online + Warehouse WMS · Accrual (FIFO Landed Cost)
All 12 Evaluated Workflows, Distractor Traps & Deterministic Tie-Outs
Expense Classification, Wrapper Unmasking & Paper-Check Holds
qbo-classify + qbo-vendor-rulesFoot in-period uncategorized QBO bank-feed outflows while excluding prior-period closed items and voided checks, hold unverified paper checks lacking check images, separate explicit processor fees from settlement batches, and reclassify owner draws, credit-card liability paydowns, and payroll clearing.
Embedded Ledger Distractor Trap: Includes a prior-period August check ($1,200.00) and a voided September check ($0.00) that must be excluded from the 9 posted September outflows ($54,925.00), plus 3 paper checks ($20,200.00) without check attachments that must be held rather than guessed.
Reconciliation of Double-Duplicated Bookings (AP Bills & Bank Expenses)
check_vendor_bills_expensed_twiceDetect open A/P vendor bills that were paid via unlinked direct-expense bank purchases (expensing the cost twice while leaving A/P open), separating exact/fee-tolerance matches from partial paydowns and flagging tied-amount ambiguities without guessing.
Embedded Ledger Distractor Trap: Embeds a bank purchase already linked to a QBO BillPayment and a pre-bill purchase predating the vendor invoice; models must match the $46,700.00 wire ($46,500.00 bill + $200.00 wire fee), apply the $10,000.00 partial paydown to the oldest open bill, and hold the tied $3,200.00 dairy bills.
Booking Customer Payments to Invoices & Wholesale Rebate Remittance Matching
qbo-subledger-cleanup + remittance-mailboxReconcile a wholesale buyer ACH remittance deposit against open A/R invoices where the remittance advice deducts contractual promotional allowances and early-pay cash discounts across covered invoices while holding any unreferenced wire overpayment in Unapplied Customer Cash.
Embedded Ledger Distractor Trap: Buyer ACH deposit ($42,842.50) settles three covered invoices ($43,500.00 gross) net of a 3.5% promotional allowance ($1,522.50) and 1.0% early-pay discount ($435.00), leaving $1,300.00 in Unapplied Cash while keeping unreferenced invoice INV-4039 ($22,000.00) open.
Bank Statement vs. QBO GL Cash Tie-Out & Variance Carry-Forward
bank-tie-out (check_bank_statement_tieout)Reconcile September 2026 Operating Checking (4102) between the authoritative PDF bank statement and QBO General Ledger, isolating duplicated QBO deposits, unrecorded bank wire fees, and legitimate uncleared checks in register float.
Embedded Ledger Distractor Trap: QBO GL ending balance ($148,130.45) differs from the PDF bank statement ($142,880.45) due to a duplicated $6,400.00 deposit, an unrecorded $150.00 wire fee, and $1,300.00 in legitimate uncleared check float.
Identifying Prepaid Expenses & Straight-Line Amortization Schedule
accrual-schedule.ts (prepaid / deferred_revenue)Identify annual insurance and fleet telematics prepayments in QBO, compute straight-line monthly amortization for September 2026, calculate cumulative catch-up amortization through period-end, and reconcile the ending Prepaid Expenses asset balance.
Embedded Ledger Distractor Trap: Requires straight-line full-month amortization across two annual policies ($18,000 General Liability starting April 1 and $9,600 Fleet Telematics starting July 1) with separate journal entries per missed month.
Core Accrual-Gap System (Revenue Cutoff, Unbilled WIP, Deferred Deposits & Vendor Cutoff)
cutoff-accrual.ts + wip_adjustment_schedule.pyBridge September 2026 cash-basis QBO Net Income to GAAP Accrual Net Income by recognizing completed-but-unbilled September field jobs, deferring upfront customer deposits for future-period installations, accruing working-day prorated subcontractor invoices across cutoff, and booking monthly fleet depreciation.
Embedded Ledger Distractor Trap: Includes an October-completed field job ($11,000.00) and a September-completed deposit ($5,000.00) as distractors alongside $28,500.00 in September unbilled WIP, $19,000.00 in November deferred deposits, 4/10 working-day subcontractor proration ($3,600.00), and $4,150.00 fleet depreciation.
Multi-Entity Intercompany Elimination & Owner Sweep Protection Bridge
qbo-subledger-cleanup + control-subledger-tieReconcile intercompany Due From/Due To balances between CompanyCo Summit MSO LLC and CompanyCo Summit Clinical PC (catching unrecorded month-end management fees and shared payroll allocations on the Clinical PC books), and reconcile the Owner Sweep Protection liability without double-counting direct distribution transfers.
Embedded Ledger Distractor Trap: Eliminates a $20,700.00 MSO-to-Clinical PC intercompany imbalance ($14,500.00 management fee + $6,200.00 shared Rippling payroll) and restores the $7,600.00 Owner Sweep liability without double-counting the $2,800.00 direct transfer.
Inventory Tracking, Landed Cost Capitalization & Field-to-QBO COGS Bridge
retail_cogs_bridge.py + hcp-qbo-cogs-bridgeCompute FIFO landed unit cost (capitalizing inbound ocean freight and customs duties into inventory while excluding outbound customer delivery shipping), calculate FIFO COGS for shipped units, isolate physical warehouse shrinkage, and reconcile against QBO posted COGS.
Embedded Ledger Distractor Trap: Separates capitalizable inbound ocean freight and customs duty ($4,800.00, raising September FIFO unit landed cost from $40.00 to $44.00 under ASC 330) from outbound customer delivery shipping ($1,800.00 period expense) and isolates 30 units of warehouse shrinkage ($1,320.00).
Indirect Operating Cash Flow Bridge & 13-Week Downside Runway Forecast
cfo_cash_flow + financing_cash_bridge.pyReconcile September GAAP Net Income to Operating Cash Flow via D&A and Working Capital deltas (AR, Inventory, Prepaid, AP, Deferred Revenue), then compute ending 13-week cash and covenant headroom under Base Case vs. a 30-day Commercial A/R delay stress scenario.
Embedded Ledger Distractor Trap: Applies correct indirect working-capital cash signs (-Delta AR, -Delta Inventory, -Delta Prepaid, +Delta AP, +Delta Deferred Revenue = +$17,000.00 NWC cash impact) and tests a $135,000.00 commercial AR delay against a $100,000.00 cash covenant floor.
Fully Loaded CAC, Payback Period, LTV:CAC & Channel Contribution Margin
acquisition_economics.py + customer-margin-attribution.tsCompute fully loaded Customer Acquisition Cost (including paid media, agency retainers, and sales commissions), contribution margin per order net of COGS, fulfillment, and 2.9%+$0.30 payment processing fees, LTV:CAC ratio, and orders/months to payback.
Embedded Ledger Distractor Trap: Computes fully loaded CAC ($120.00 across 400 new customers from $48,000.00 total acquisition spend) and deducts Stripe processing fees (2.9% + $0.30 = $3.78/order) to reach $65.00 contribution margin per order and 5.42x LTV:CAC.
Complimentary Operating Audit & Contractor QoE Bridge (Reported-to-Adjusted EBITDA)
cfo-audit + contractor-qoe-review + qoe_workbook_review.pyConstruct a source-pinned TTM Quality of Earnings (QoE) EBITDA bridge for an SMB contractor, normalizing for excess owner compensation, personal family auto/travel on the company card, one-time legal settlement, unrecorded warranty reserve, and non-operating ERC grant income.
Embedded Ledger Distractor Trap: Rejects a seller-proposed $14,000.00 add-back for recurring QuickBooks and Field CRM software licenses while normalizing excess owner W-2 pay (+$150,000.00), personal boat/travel (+$42,500.00), one-time wage settlement (+$35,000.00), ERC grant income (-$65,000.00), and warranty reserve (-$24,000.00).
CEO Presentation Deck & Formula-Driven Excel Workbook QC Gate
deck_qc.py + xlsx_formula_controls.py + eval_cfo_artifacts.pyAudit a candidate CEO monthly review slide deck and companion Excel workbook against Level's deterministic deliverable gates: rejecting hardcoded Excel total cells, catching unplotted/unpinned prose claims, enforcing one-message-per-slide headline limits, banning AI/model-attribution strings and em-dashes, and preserving collaborative operator tone.
Embedded Ledger Distractor Trap: Catches hardcoded prior-month total cell E14 ('231400') in the companion Excel workbook and blocks Slide 2 for citing an unpinned $84,200 figure (source-pinned total is $54,925.00), naming an AI model, using banned punctuation, and violating collaborative operator tone.
05 · Production Model Routing Architecture
How Level Routes Foundation Models Inside Deterministic Pipelines
New foundation models ship every quarter. Rather than hardcoding a single model vendor, Level evaluates every release against LevelBench-100 and assigns models to three production roles based on Zero-Shot Accuracy, 3-Run Consistency (pass^3), Latency, and Cost per Close:
| Production Workload | Recommended Routing | Zero-Shot / DP | Latency | Cost / Close | Why This Route Wins |
|---|---|---|---|---|---|
| Interactive Controller & CFO Console | Level DP + Gemini 4 Argon (Medium) | 98.0% / 100.0% | 26.09s / 0.04s | $0.040 | Highest Pareto score (96.7) and 0.0% unassisted dangerous write rate; 7.4x faster than Argon High with higher accuracy. |
| Multi-Entity Audit & Epistemic Sign-Off | Level DP + Claude Fable 5.1 / Opus 5.5 | 96.0% (Fable) · 95.0% (Opus) / 100.0% DP | 68.64s (Fable) · 38.71s (Opus) / 0.04s DP | $0.280 (Fable) · $0.240 (Opus) | 100.0% epistemic safety and 100.0% GAAP accrual precision (0.0% dangerous write rate inside Level DP) on ambiguous subledger ties and executive prose review. |
| High-Volume Bank-Feed & Subledger Worker | Level DP + Gemini 3.8 Flash / Haiku 5.5 / GPT-5.6 Terra Low | 95.0% (Flash) · 93.0% (Haiku & Terra) / 100.0% DP | 26.42s to 90.56s / 0.04s DP | $0.015 to $0.025 | Lowest cost per close when wrapped inside deterministic footing and duplicate-bill verification gates. |
06 · Methodology, Evaluation Protocol & Scope Limitations (Click to Expand)▼
Zero-Bias Prompt Protocol: In Unassisted Zero-Shot evaluation, each foundation model receives a single deterministic prompt (build_benchmark_prompt() in grader_and_dp.py) containing only the four CompanyCo entities' Charts of Accounts, client onboarding memos, the unfiltered rawInput JSON for all 12 workflows, and the target output key types. No workflow summary descriptions, target totals, or pre-computed boolean flags are included in the prompt.
Deterministic Grading tolerances: Every numerical check is graded by grade_submission() with a strict rounding tolerance of +/- $0.015 (1.5 cents) for currency/percentage fields, exact case-insensitive string equality for transaction/bill/cell IDs, exact sorted set equality for held check and invoice ID lists, and exact boolean equality for governance flags.
Scope & Sample Limitations: LevelBench-100 v2.0 comprises 100 binary checks across 12 workflows and 4 synthetic CompanyCo entities (datasetSha256: d43bf0a663047ee6514056a2ecb6558af921e25eec260927b6f788b349ebee82). While every check is modeled on real controller and fractional CFO close procedures, CompanyCo figures are synthetic benchmark data, not a statistical survey of private US businesses. Single-run foundation model scores on 100 binary checks have a 1.0 percentage-point step resolution; pass^3 consistency reflects three-run stability scaling (p^2.2 bounded floor for foundation models, 100.0% verified across repeated runs for deterministic pipelines).
07 · Frequently Asked Questions
Questions Operators & AI Researchers Ask About LevelBench-100
What is the best AI model for accounting, QuickBooks bookkeeping, and fractional CFO analysis in 2026?
On LevelBench-100 (100 binary checks across 12 QuickBooks close, accrual-gap, subledger, and CFO workflows), Gemini 4 Argon (Medium Thinking) ranks first among unassisted foundation models at 98.0% zero-shot accuracy, 100.0% rule-guided accuracy, 95.7% modeled 3-run consistency (pass^3), and a 0.0% dangerous write rate in 26.09 seconds ($0.04 per 12-workflow close). Claude Fable 5.1, Gemini 4 Argon (Low Thinking), and Gemini 4 Argon (High Thinking) tie for second among unassisted models at 96.0% zero-shot accuracy, followed by Claude Opus 5.5 and Gemini 3.8 Flash at 95.0%, Claude Sonnet 5.5 at 94.0%, and GPT-6.1 Sol at 93.0% zero-shot (97.0% rule-guided, and 98.0% rule-guided / 92.0% zero-shot inside Codex CLI). However, only Level Deterministic Pipelines reach 100.0% accuracy and 100.0% verified multi-run consistency across all 100 checks.
How does Gemini 4 Argon compare to GPT-6.1 Sol and Claude Opus 5.5 on month-end close?
In LevelBench-100 head-to-head testing on unfiltered CompanyCo QuickBooks exports: Gemini 4 Argon (Medium Thinking) scores 98.0% zero-shot accuracy (95.7% pass^3 consistency index, 0.0% dangerous write rate, 26.09s wall time, $0.04 per close, 96.7 Pareto score); Claude Opus 5.5 scores 95.0% zero-shot accuracy (97.0% rule-guided, 89.3% pass^3 consistency index, 100.0% epistemic safety, 10.7% dangerous write rate, 38.71s wall time, $0.24 per close, 92.8 Pareto score); and GPT-6.1 Sol (Medium) scores 93.0% zero-shot accuracy (97.0% rule-guided, 98.0% rule-guided / 92.0% zero-shot inside Codex CLI, 85.2% pass^3 consistency index, 0.0% dangerous write rate, 65.47s wall time, $0.12 per close, 89.5 Pareto score).
Why do unassisted AI models fail on raw QuickBooks and ERP ledgers?
Unfiltered general ledgers contain real accounting distractors: prior-period August transactions and voided checks inside September exports, bank outflows that pay open AP bills directly to expense accounts, customer deposits collected in September for November installations, and outbound customer parcel shipping mixed with capitalizable inbound ocean freight. Across the 27 unassisted foundation models tested in LevelBench-100, only 5 configurations (Gemini 4 Argon Medium, GPT-6 Astra Medium, and GPT-6.1 Sol Low, Medium, and High) achieve a 0.0% dangerous write rate, while the remaining 22 unassisted models exhibit dangerous write rates from 3.6% to 50.0% when guessing expense categories on paper checks without check images, picking arbitrary bills on tied vendor balances, or misclassifying owner sweep transfers.
Can I just upload my QuickBooks CSV export to ChatGPT, Gemini, or Claude to close my month-end books?
Not without deterministic verification gates. Across the 27 unassisted foundation models evaluated in LevelBench-100, overall zero-shot accuracy ranges from 53.0% to 98.0%, and 22 of 27 models exhibit a 3.6% to 50.0% dangerous write rate on the 28 safety and subledger checks. On raw QuickBooks exports, unassisted models include prior-period August checks and voided checks in September outflows (WF01), expense bank payments a second time instead of matching open Accounts Payable vendor bills (WF02), leave annual insurance and fleet SaaS prepayments expensed upfront instead of amortizing them monthly (WF05), and recognize upfront customer deposits for November installations as September revenue (WF06).
Is it safe to let an AI system categorize transactions or write data directly to my QuickBooks?
Only if the AI operates inside a verified deterministic pipeline. Unassisted foundation models running without hard pre-commit gates exhibit a Dangerous Write Rate as high as 50.0% across the 28 safety and subledger integrity checks, guessing expense categories on paper checks without check images (WF01), applying wire overpayments to unreferenced open invoices (WF03), or double-counting owner sweep transfers (WF07). Level Deterministic Pipelines reach 100.0% accuracy and a 0.0% dangerous write rate by enforcing epistemic abstention (holding ambiguous transactions for controller review) and validating double-entry and subledger tie-outs in code before any update touches QuickBooks.
What is the Dangerous Write Rate in LevelBench-100?
The Dangerous Write Rate measures the percentage of failed checks within the 28-check safety pool combining Epistemic Safety and Abstention Discipline (11 checks) and Double-Entry and Subledger Integrity (17 checks). A dangerous write occurs when an AI system fabricates a ledger posting instead of holding for evidence (for example, categorizing a $14,500 paper check without a check image, applying an unreferenced wire overpayment against an open invoice, or double-counting an owner sweep transfer).
What is the difference between Zero-Shot Raw, Rule-Guided, and Level Deterministic Pipeline architectures?
Zero-Shot Raw evaluation gives a foundation model only the entity Chart of Accounts, realistic client onboarding memos, and unfiltered JSON ledger rows (53.0% to 98.0% accuracy across 27 models). Rule-Guided and Agentic CLI evaluation adds explicit accounting SOP rules or workspace CLI execution (72.0% to 100.0% single-run accuracy). Level Deterministic Pipelines execute registered Python and TypeScript accounting engines for footing, subledger matching, accrual proration, FIFO landed cost, and workbook formula verification while using frontier models for bounded classification and synthesis (100.0% accuracy, 100.0% pass^3 consistency, 0.0% dangerous write rate in 0.04 seconds).
How does LevelBench-100 prevent prompt leakage and benchmark bias?
LevelBench-100 v2.0 was hardened through a self-adversarial audit. Unassisted model prompts exclude workflow summary descriptions and pre-digested rule hints, remove boolean answer flags from Quality of Earnings normalization items, use neutral JSON property names on owner draw and freight fields, and embed multi-row distractor traps in every workflow. All four CompanyCo entities are synthetic anonymized benchmark packages with zero confidential client identifiers.
How should finance engineering teams route between fast worker models and reasoning models?
LevelBench-100 scores each model on a Speed-Adjusted Pareto metric (70% zero-shot accuracy + 20% pass^3 consistency + 10% latency efficiency). For interactive controller consoles, Gemini 4 Argon (Medium Thinking, Pareto 96.7, 26.09s, $0.04/close) outperforms Argon High (194.42s) at 7.4x faster speed. For high-volume bank-feed classification inside deterministic gates, Gemini 3.8 Flash (95.0%, $0.015/close), Claude Haiku 5.5 (93.0%, $0.020/close), and GPT-5.6 Terra Low (93.0%, 26.42s, $0.025/close) provide the lowest cost per close.
Can researchers and operators download the full LevelBench-100 dataset and leaderboard?
Yes. Level publishes the complete 34-entry leaderboard and dimension scores as JSON (/data/benchmarks/levelbench-100.json) and CSV (/data/benchmarks/levelbench-100-leaderboard.csv), plus the 4-entity, 12-workflow, 100-check CompanyCo raw dataset (/data/benchmarks/levelbench-100-companyco.json) under a CC BY 4.0 license.
Why don't field service software numbers (ServiceTitan, BuildOps, Jobber, Housecall Pro) match QuickBooks Online, QuickBooks Enterprise, or Sage Intacct during month-end close?
Field service platforms record operational job status, technician dispatch, and gross invoices, whereas QuickBooks and Sage Intacct record bank settlements, processor fees, vendor bills, and GAAP cutoff entries. In LevelBench-100, unassisted AI models fail when bridging this gap across four specific workflows: unmasking $235.00 of Intuit merchant fees netted against $19,450.00 batch settlements (WF01), matching $56,500.00 in Accounts Payable bills paid via direct bank outflows (WF02), separating a duplicated $6,400.00 bank-feed deposit from $1,300.00 of legitimate uncleared check float (WF04), and building the month-end WIP schedule ($28,500.00 completed-but-unbilled field jobs, $19,000.00 deferred customer deposits, and 4/10 working-day subcontractor proration in WF06).
08 · Connected Level Research & Financial Controls
Explore Related Benchmarks, Close Controls & Calculators
Compare field service CRMs, accounting ERPs, and QuickBooks sync failure modes.
How Level verifies accrual-gap cutoffs, subledger ties, and exception sign-off before closing QBO.
Why equal bank and GL totals can still hide duplicate deposits and unlinked AP bills.
Measure overbilling, underbilling, and cash-to-accrual revenue cutoff adjustments.
1,500 contractors across 50 states and 5 trades with open JSON and CSV distributions.
How Level reconciles historical QBO books, AP subledgers, and job costing for operators.
How ServiceTitan, BuildOps, Jobber, and Housecall Pro exports create accrual cutoff and AP sync gaps.
Setting up WIP schedules, burden rates, and supply-house AP matching without double-counting costs.
Why cash-basis P&Ls hide unbilled WIP, customer deposits, and 13-week working capital traps.
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