Top picks for Financial Analysis (2026)
Reading earnings, modeling cash flows. Ranked from 452 live models on the OpenRouter catalog, weighted for reasoning quality, tool calling, structured output.
Updated 2026-10-13 · prices checked at this morning's rebuild
Which should you use? Anthropic: Claude Opus 4.7 (batch) tops this ranking on blended score. If cost drives the decision, OpenAI: o3 (batch) is the cheapest of the leaders at $1.00/M input.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Opus 4.7 (batch)anthropic/claude-opus-4.7:batch | 147 | $2.50 | $12.50 | 1,000,000 | Details → |
| 2 | OpenAI: o3openai/o3 | 143 | $2.00 | $8.00 | 200,000 | Details → |
| 3 | OpenAI: o3 (batch)openai/o3:batch | 143 | $1.00 | $4.00 | 200,000 | Details → |
| 4 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 142 | $3.00 | $15.00 | 1,000,000 | Details → |
| 5 | Anthropic: Claude Sonnet 4.6 (batch)anthropic/claude-sonnet-4.6:batch | 142 | $1.50 | $7.50 | 1,000,000 | Details → |
| 6 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 142 | $5.00 | $25.00 | 1,000,000 | Details → |
| 7 | OpenAI: GPT-5openai/gpt-5 | 140 | $1.25 | $10.00 | 400,000 | Details → |
| 8 | OpenAI: GPT-5 (batch)openai/gpt-5:batch | 140 | $0.62 | $5.00 | 400,000 | Details → |
| 9 | OpenAI: o4 Miniopenai/o4-mini | 139 | $1.10 | $4.40 | 200,000 | Details → |
| 10 | OpenAI: o4 Mini (batch)openai/o4-mini:batch | 139 | $0.55 | $2.20 | 200,000 | Details → |
| 11 | Anthropic: Claude Opus 4.8 (batch)anthropic/claude-opus-4.8:batch | 135 | $2.50 | $12.50 | 1,000,000 | Details → |
| 12 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 135 | $0.30 | $2.50 | 1,048,576 | Details → |
| 13 | Google: Gemini 2.5 Flash (batch)google/gemini-2.5-flash:batch | 135 | $0.15 | $1.25 | 1,048,576 | Details → |
| 14 | OpenAI: GPT-5.4openai/gpt-5.4 | 134 | $2.50 | $15.00 | 1,050,000 | Details → |
| 15 | OpenAI: GPT-5.4 (batch)openai/gpt-5.4:batch | 134 | $1.25 | $7.50 | 1,050,000 | Details → |
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How we ranked these
For Financial Analysis, we weight models on reasoning quality, tool calling, structured output. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Financial Analysis
Financial analysis is the task of extracting insights from earnings reports, balance sheets, and cash flow statements, then building predictive models of future financial performance. You need this when evaluating investment opportunities, stress-testing corporate scenarios, or automating quarterly reviews. A good model reads dense financial documents accurately, catches material line items others miss, and produces cash flow projections that align with stated assumptions. Poor models hallucinate numbers, miss footnotes that reverse prior earnings, or project growth without accounting for working capital drag. The main speed constraint: document parsing at scale hits token limits fast on 10-K filings, so batch processing or chunking strategies matter more than raw model speed.
When to use: Use this when you need to quickly extract financial data from company reports, build forward-looking cash flow models, or compare financial health across multiple companies without reading hundreds of pages yourself.
Common questions
What is the difference between using AI for financial analysis versus traditional spreadsheet modeling?
AI models can extract and interpret unstructured text from earnings calls, 10-Ks, and management commentary in seconds, then populate spreadsheets or databases automatically. Traditional spreadsheets still handle the math, but AI removes the manual copy-paste step and catches details humans skip. For complex scenario analysis, you combine both: AI extracts the baseline, then spreadsheet formulas run sensitivity tables.
How accurate are AI models at reading financial statements, and can they miss material details?
GPT-4 and similar models score 85-95% accuracy on structured line items like revenue and net income, but they do miss footnotes, contingent liabilities, or one-time charges buried in prose. Always validate key numbers against source documents and use AI as a first-pass filter, not a final audit. For regulatory or investment decisions, human review of flagged items is mandatory.