Finsider Labs
Finsider Labs

Financial Intelligence, Grounded in Evidence

Finsider Labs is our applied AI research lab for financial intelligence. We explore source lineage, reproducible analysis, financial screening, and the role of professional judgment in AI-powered diligence.

[FRONTIER LEADERBOARDS]

Published model comparisons for financial reasoning and document understanding. Historical source results, not Finsider test scores.

AIMultiple / September 2026

FinanceReasoning

Financial numerical reasoning

1

GPT-5.6 Sol Pro

90.76

2

Claude Fable 5.1

90.34

2

GPT-5.6 Sol

90.34

4

Claude Opus 5

89.92

5

Kimi K3

88.24

050100

Accuracy (%) / top five in this selection

Full comparison

Model ML / September 2026

Analytical finance

Financial calculations

1

GPT-6 Astra

93.21

2

Claude Fable 5.1

92.54

3

Gemini 3.8 Flash

92.39

050100

Reported score (0-100) / published comparison

Full comparison

Model ML / September 2026

Financial workflows

Multi-step financial work

1

Gemini 3.8 Flash

76.33

2

GPT-6 Astra

72.36

3

Claude Fable 5.1

60.00

050100

Reported score (0-100) / published comparison

Full comparison

Model ML / September 2026

Single-document analysis

Financial document retrieval

1

GPT-6 Astra

86.30

2

GPT-5.6 Sol

82.96

3

Gemini 3.8 Flash

81.48

050100

Reported score (0-100) / published comparison

Full comparison

Model ML / September 2026

Multi-document analysis

Cross-document financial evidence

1

GPT-6 Astra

78.71

2

GPT-5.6 Sol

76.77

3

Gemini 3.8 Flash

63.23

050100

Reported score (0-100) / published comparison

Full comparison

AIMultiple / September 2026

Open-weight financial reasoning

FinanceReasoning / open-weight selection

1

Kimi K3

88.24

2

GLM-5.2

86.13

3

gpt-oss-120b

81.09

4

Qwen3-235B-A22B-Thinking-2507

75.63

5

Llama 4 Maverick

75.21

050100

Accuracy (%) / top five in this selection

Full comparison
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[METHODOLOGY PAPERS]

Original Finsider methodology drafts on financial screening, traceable evidence, and reviewer-led M&A diligence. No benchmark results or peer review are implied.

Date Title
9/4/2026
An evidence-first framework for pre-QoE screeningFinancial Diligence, Enterprise
8/27/2026
Source lineage from financial record to reviewed findingFinancial Diligence
8/20/2026
A review framework for candidate EBITDA adjustmentsEvidence & Review
8/6/2026
Period alignment and evidence coverage in cash proofFinancial Diligence, Enterprise, Evidence & Review
6/30/2026
A structured approach to working capital analysisFinancial Diligence, Enterprise, Evidence & Review
6/29/2026
Separating extraction, calculation, and professional judgmentFinancial Diligence, Evidence & Review
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[BLOG]

Practical perspectives on financial diligence from Finsider Labs. Local editorial drafts for review.

Financial DiligenceSep 3, 2026

What a pre-QoE screen can and cannot tell you

A first-pass financial screen should identify the next diligence questions, not turn incomplete records into definitive conclusions.

Financial DiligenceSep 2, 2026

Why evidence lineage matters in financial diligence

A financial finding becomes useful when another reviewer can reconstruct its source, calculation, assumptions, and decision.

Financial DiligenceAug 26, 2026

From screening to a CPA-led Quality of Earnings engagement

The transition from a screen to QoE begins with scope, evidence requirements, and professional judgment, not a change in the report title.

Working CapitalAug 13, 2026

Cash proof: connecting reported performance to bank evidence

Cash and earnings tell related but different stories. A useful cash proof makes timing, coverage, and reconciliation differences explicit.

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