Accounting has long been considered one of the hardest verticals for LLMs: numbers must be exact to the cent, debit and credit directions cannot flip, and asking the same question three times should yield the same answer. DualEntry, a New York-based AI-native ERP company, has just published its first full answer — it ran 18 frontier models through the same accounting environment three times each, using an identical test-company ledger, the same tools and the same instructions. Grading is deterministic against pre-written reference answers, with no AI judge: an 80% score passes, and a task counts only if passed in at least two of three runs.

A tightly packed leaderboard

The top of the table is crowded. Google's Gemini 4 Argon ranks first at 91.1% accuracy; tied for second are Zhipu AI's GLM-5.3 Flash and SpaceXAI's Grok 4.7 at 88.1%, just three points behind. Anthropic's best, Claude Fable 5.1, scored 85.1%; OpenAI's best, GPT-6 Astra / GPT-6.1 Sol, came in at 81.2%. Behind them, DeepSeek V4.1 Flash and Kimi K3 both sit at 84.2%, Meta's Muse Wonder at 83.2% — twelve models in total packed into the 80.2%–85.1% band.

One detail deserves emphasis: GLM-5.3 Flash, tied for second, is an open-weight model. After two years of open models chasing closed ones on general leaderboards, on the concrete job of bookkeeping an open model now ties Grok 4.7 and sits three points from Argon.

Consistency is the real divider

DualEntry reports a separate "consistency" metric — the share of tasks a model passes in all three runs. This is where models with equal accuracy separate: Grok 4.7, tied for second on accuracy, managed only 74.3% consistency; Claude Fable 5.1, fourth on accuracy at 85.1%, reached 80.2%. Argon is the only model leading both at 86.1%. As DualEntry co-founder Benedict Dohmen put it: "A model that gets a journal entry right once and wrong the next time isn't ready for the books. Argon was the most accurate model we tested and the most consistent, and finance teams need both."

For teams building finance automation, this metric may matter more than accuracy itself — books cannot be a dice roll.

A 400x cost spread

The other glaring dimension is price. DeepSeek V4.1 Flash scored 84.2% at $0.003 per task; Claude Fable 5.1 scored under one point higher at $1.18 per task — roughly 400 times the cost. GLM-5.3 Flash is more extreme still: 88.1% accuracy, tied second, at $0.006 per task, about one-nineteenth of Argon's $0.115.

Cheap has a price, though: GLM-5.3 Flash averages 57 seconds and 4.9k output tokens per task, versus 18 seconds and 0.7k for Argon. The fast model trades "think less, answer now" for speed — a real difference in latency-sensitive interactive settings.

Shared weakness: receivables and month-end close

Across the eight task areas, models averaged 94.4% on transaction classification and every model aced the multiple-choice accounting-knowledge tasks. But on accounts receivable and month-end close they averaged just 64.4%, with AR scores scattering from 41.7% to 91.7%. Category champions vary: Muse Wonder leads bank reconciliation (91.7%), Kimi K3 leads month-end close (91.7%), while Argon manages only 75.0% on reconciliation. On hard cases, Argon, Grok 4.7 and Gemini 3.8 Flash share the best result at 82.1%; Claude Opus 5.5 and Mistral Large 4 drop 30.3 points from easy to hard, the steepest falls in the field.

So what

The value of this benchmark is not "who is number one" but how close it stays to real work: all 101 synthetic cases are completed with tools inside an actual accounting system, with no AI grading AI. Two signals are worth noting. First, the gap at the top has shrunk to three points, so model selection is shifting from "who is most accurate" to "who is most consistent and cheapest". Second, open-weight models have formally entered the top tier on vertical tasks — a tied-second model at $0.006 per task is not a small number for any pipeline running classification, reconciliation or document review at scale.

The full benchmark and the DualBench100 methodology are public at dualentry.com/benchmark (October 13, 2026 release, Version 1.0), with cost, duration and consistency detail for all 18 models.