Financial analysis is one of the clearest places where AI cost can either disappear into the budget or become a material line item. A single 10-K can run 120,000-220,000 tokens after extraction. Add an earnings transcript, prior quarter notes, analyst questions, tables, segment disclosures, and a structured investment memo, and one “analyze this company” workflow can easily exceed 300,000 input tokens before the model writes a single sentence.
The good news: 2026 model pricing makes financial document analysis affordable if you choose the right model for each stage. The bad news: premium long-context models can cost 10x-60x more than efficient alternatives for the same filing review. The right architecture is not “send everything to the most expensive model.” It is a staged workflow: cheap long-context models for ingestion and KPI extraction, stronger models for judgment-heavy synthesis, and premium models only for final high-stakes review.
This guide breaks down the real cost of analyzing 10-Ks, 10-Qs, earnings calls, analyst briefs, and investment memo drafts using current model pricing. You’ll get per-document costs, monthly scenarios, model recommendations, and a practical routing strategy for finance teams, equity research workflows, investor relations, and corporate strategy groups.
💡 Key Takeaway: Most financial analysis workflows are input-heavy. A model’s input price and context window matter more than its output price until you start generating long memos, board decks, or multi-company reports.
The cost formula for AI financial document analysis
AI API costs are based on tokens. A token is roughly a word fragment, and financial filings tokenize aggressively because they contain tables, footnotes, XBRL labels, legal language, and repeated headings. For a deeper primer, see the token guide, but the working formula is simple:
Cost = input tokens × input price + output tokens × output price
All prices in this guide are per 1 million tokens. For example, GPT-5.2 costs $1.75 per 1M input tokens and $14 per 1M output tokens. If you send 200,000 input tokens and receive 8,000 output tokens, the cost is:
- Input: 200,000 / 1,000,000 × $1.75 = $0.35
- Output: 8,000 / 1,000,000 × $14 = $0.112
- Total: $0.462
That is less than one dollar for a full-filing analysis on GPT-5.2. On a premium model like GPT-5.5 Pro, priced at $30 input / $180 output per 1M tokens, the same request costs:
- Input: 200,000 / 1,000,000 × $30 = $6.00
- Output: 8,000 / 1,000,000 × $180 = $1.44
- Total: $7.44
The output is the same size. The bill is 16.1x higher.
For finance teams processing hundreds or thousands of filings, that spread compounds quickly. A 500-company quarterly review at one pass per company costs about $231 on GPT-5.2 and $3,720 on GPT-5.5 Pro before retries, chunking, embeddings, or analyst iterations.
Typical token sizes for financial documents
The first budgeting step is estimating document size. Financial workflows are not like short chatbot prompts. A clean 10-K, plus extracted tables and footnotes, can consume most of a 128k context window. Long-context models are valuable because they reduce chunking complexity and preserve cross-document reasoning.
| Document or task | Typical input tokens | Typical output tokens | Notes |
|---|---|---|---|
| Earnings call transcript | 25,000-50,000 | 2,000-5,000 | Includes prepared remarks and Q&A |
| 10-Q filing | 50,000-90,000 | 3,000-7,000 | Shorter than 10-K but table-heavy |
| 10-K filing | 120,000-220,000 | 5,000-12,000 | Risk factors, MD&A, notes, segments |
| Analyst brief packet | 40,000-120,000 | 4,000-10,000 | Multiple PDFs or notes |
| Full company review | 250,000-450,000 | 8,000-20,000 | Filing + transcript + prior memo |
| Multi-company peer comparison | 600,000-1,500,000 | 15,000-40,000 | Requires 1M+ context or staged summarization |
For cost calculations in this article, we’ll use four standard workloads:
- Earnings call summary: 40,000 input tokens, 4,000 output tokens
- 10-K analysis: 180,000 input tokens, 8,000 output tokens
- Full quarterly company review: 320,000 input tokens, 15,000 output tokens
- Peer comparison pack: 1,000,000 input tokens, 30,000 output tokens
These numbers are conservative for public-market work. They assume text extraction is already done and the model receives clean text, tables converted to markdown, and a structured prompt.
⚠️ Warning: PDF extraction can increase token count by 20-50% if headers, page numbers, duplicated footers, and broken tables are not removed before analysis. Clean filings before sending them to the model.
Long-context model pricing for financial analysis
Financial analysis rewards models that can hold an entire filing, transcript, and prior memo in context. A 128k model can handle many earnings calls and some 10-Qs, but a full 10-K plus historical context usually needs 200k-1M tokens. Peer comparisons often need 1M+.
Here are relevant 2026 models for long-document financial workflows.
| Model | Provider | Input / output price per 1M tokens | Context window | Best use |
|---|---|---|---|---|
| Llama 4 Scout | Meta via Together AI | $0.08 / $0.30 | 10,000,000 | Ultra-cheap bulk ingestion and large peer packs |
| DeepSeek V4 Pro | DeepSeek | $0.435 / $0.87 | 1,000,000 | Low-cost extraction and structured analysis |
| Gemini 2.5 Flash | $0.30 / $2.50 | 1,000,000 | Fast summaries, transcript analysis, screening | |
| GPT-5 mini | OpenAI | $0.25 / $2.00 | 500,000 | Cheap OpenAI workflow for filings under 500k |
| Gemini 3 Pro | $2.00 / $12.00 | 2,000,000 | High-quality synthesis across long context | |
| GPT-5.2 | OpenAI | $1.75 / $14.00 | 1,000,000 | General-purpose premium analysis |
| Claude Sonnet 4.6 | Anthropic | $3.00 / $15.00 | 1,000,000 | Memo drafting and nuanced narrative analysis |
| Claude Opus 4.7 | Anthropic | $5.00 / $25.00 | 1,000,000 | Senior-review style reasoning and high-stakes synthesis |
| GPT-5.5 Pro | OpenAI | $30.00 / $180.00 | 1,050,000 | Final review for highest-value decisions |
The best default cost-quality stack for financial reporting is:
- Bulk extraction: Llama 4 Scout, DeepSeek V4 Pro, Gemini 2.5 Flash, or GPT-5 mini
- Company-level synthesis: GPT-5.2, Gemini 3 Pro, or Claude Sonnet 4.6
- High-stakes final review: Claude Opus 4.7 or GPT-5.5 Pro for selected memos only
Do not use a premium model for every pass. Use it after cheaper models have extracted KPIs, reconciled tables, flagged changes, and produced a concise evidence pack.
Per-document costs: 10-Ks, earnings calls, and peer packs
The table below applies the four standard workloads to common model choices. Costs include both input and output tokens.
| Model | Earnings call 40k + 4k | 10-K 180k + 8k | Full review 320k + 15k | Peer pack 1M + 30k |
|---|---|---|---|---|
| Llama 4 Scout | $0.0044 | $0.0168 | $0.0301 | $0.0890 |
| DeepSeek V4 Pro | $0.0209 | $0.0853 | $0.1523 | $0.4611 |
| Gemini 2.5 Flash | $0.0220 | $0.0740 | $0.1335 | $0.3750 |
| GPT-5 mini | $0.0180 | $0.0610 | $0.1100 | Not recommended over 500k context |
| Gemini 3 Pro | $0.1280 | $0.4560 | $0.8200 | $2.3600 |
| GPT-5.2 | $0.1260 | $0.4270 | $0.7700 | $2.1700 |
| Claude Sonnet 4.6 | $0.1800 | $0.6600 | $1.1850 | $3.4500 |
| Claude Opus 4.7 | $0.3000 | $1.1000 | $1.9750 | $5.7500 |
| GPT-5.5 Pro | $1.9200 | $6.8400 | $12.3000 | $35.4000 |
The cheapest models make individual financial tasks almost free. A 10-K pass on Llama 4 Scout is $0.0168, while the same token volume on GPT-5.5 Pro is $6.84. That is a 407x price difference.
[stat] 407x The cost difference between Llama 4 Scout and GPT-5.5 Pro for a 180k-token 10-K analysis with an 8k-token output
This does not mean the cheapest model is always the right answer. It means every workflow needs routing. Use low-cost models to extract facts and expensive models to evaluate conclusions.
Practical scenario 1: Investor relations earnings call monitoring
An investor relations team wants to monitor quarterly earnings calls for 120 peer companies. For each company, the system summarizes the transcript, extracts guidance changes, identifies analyst concerns, and produces a one-page internal note.
Assumptions:
- 120 earnings calls per quarter
- 40,000 input tokens per call
- 4,000 output tokens per summary
- Quarterly workload spread across one month during earnings season
Monthly cost during earnings season:
| Model | Cost per call | 120-call monthly cost |
|---|---|---|
| Llama 4 Scout | $0.0044 | $0.53 |
| DeepSeek V4 Pro | $0.0209 | $2.51 |
| Gemini 2.5 Flash | $0.0220 | $2.64 |
| GPT-5 mini | $0.0180 | $2.16 |
| GPT-5.2 | $0.1260 | $15.12 |
| Claude Sonnet 4.6 | $0.1800 | $21.60 |
| Claude Opus 4.7 | $0.3000 | $36.00 |
Recommendation: use GPT-5 mini or Gemini 2.5 Flash for first-pass call summaries, then route only flagged calls to GPT-5.2 or Claude Sonnet 4.6. Flagged calls include guidance cuts, unusual margin commentary, auditor language, management turnover, or aggressive analyst questioning.
A realistic routed workflow looks like this:
- 120 calls summarized on GPT-5 mini: $2.16
- 25 flagged calls reviewed on GPT-5.2: 25 × $0.126 = $3.15
- 10 management-risk notes drafted on Claude Sonnet 4.6: 10 × $0.18 = $1.80
- Total monthly earnings-season cost: $7.11
That is the cost of covering a broad peer universe with AI-generated first drafts and targeted premium review.
✅ TL;DR: Earnings call monitoring is inexpensive because transcripts are usually under 50k tokens. Use cheap models for all calls and premium models only for flagged transcripts.
Practical scenario 2: Equity research 10-K and 10-Q review
An equity research team covers 80 companies and wants AI support for annual 10-K review plus quarterly 10-Q updates. The workflow extracts KPIs, compares language changes, identifies risk-factor deltas, summarizes MD&A, and drafts an analyst checklist.
Monthly equivalent workload:
- 80 annual 10-K analyses / year = 6.7 per month
- 240 quarterly 10-Q analyses / year = 20 per month
- 10-K workload: 180,000 input + 8,000 output
- 10-Q workload: 70,000 input + 5,000 output
10-Q cost examples:
- GPT-5 mini: 70k × $0.25 + 5k × $2 = $0.0275
- GPT-5.2: 70k × $1.75 + 5k × $14 = $0.1925
- Claude Sonnet 4.6: 70k × $3 + 5k × $15 = $0.2850
- Claude Opus 4.7: 70k × $5 + 5k × $25 = $0.4750
Monthly cost for the 80-company coverage universe:
| Model | Monthly 10-K equivalent | Monthly 10-Q equivalent | Total monthly cost |
|---|---|---|---|
| GPT-5 mini | $0.41 | $0.55 | $0.96 |
| DeepSeek V4 Pro | $0.57 | $0.65 | $1.22 |
| Gemini 2.5 Flash | $0.50 | $0.67 | $1.17 |
| GPT-5.2 | $2.86 | $3.85 | $6.71 |
| Claude Sonnet 4.6 | $4.42 | $5.70 | $10.12 |
| Claude Opus 4.7 | $7.37 | $9.50 | $16.87 |
These numbers are surprisingly low because one pass per filing is cheap. Real production systems cost more because they run multiple prompts: extraction, validation, delta analysis, memo drafting, and analyst follow-up questions.
A production-grade 10-K workflow often uses 5 passes:
- Extract financial KPIs and segment metrics
- Summarize MD&A and management commentary
- Compare risk factors against prior year
- Identify accounting policy and footnote changes
- Draft an analyst checklist or memo
Multiplying by five, the same 80-company program costs about:
- GPT-5 mini: $4.80/month
- GPT-5.2: $33.55/month
- Claude Sonnet 4.6: $50.60/month
- Claude Opus 4.7: $84.35/month
Recommendation: use GPT-5 mini or DeepSeek V4 Pro for extraction and delta detection, then Claude Sonnet 4.6 for narrative memo drafting. For a direct premium-model comparison, see GPT-5 vs Claude Sonnet 4.5 and GPT-5 vs Gemini 3 Pro.
Practical scenario 3: Buy-side investment memo drafting
A buy-side team evaluates 50 investment ideas per month. Each idea includes a full company review: latest 10-K or 10-Q, recent earnings transcript, prior internal notes, analyst excerpts, and a structured investment memo draft.
Assumptions:
- 50 company reviews per month
- 320,000 input tokens per review
- 15,000 output tokens per memo draft
- One extraction pass, one synthesis pass, one final review pass
A simple single-model workflow costs:
| Model | Cost per full review | 50 reviews/month |
|---|---|---|
| DeepSeek V4 Pro | $0.1523 | $7.62 |
| Gemini 2.5 Flash | $0.1335 | $6.68 |
| GPT-5 mini | $0.1100 | $5.50 |
| GPT-5.2 | $0.7700 | $38.50 |
| Gemini 3 Pro | $0.8200 | $41.00 |
| Claude Sonnet 4.6 | $1.1850 | $59.25 |
| Claude Opus 4.7 | $1.9750 | $98.75 |
| GPT-5.5 Pro | $12.3000 | $615.00 |
A routed institutional workflow is more effective:
- Extraction on GPT-5 mini: 50 × $0.1100 = $5.50
- Synthesis on GPT-5.2: 50 × $0.7700 = $38.50
- Final review on Claude Opus 4.7 for top 15 ideas: 15 × $1.9750 = $29.63
- Total monthly cost: $73.63
This is the recommended architecture for high-stakes investment memos. The extraction model produces structured evidence. The synthesis model drafts the memo. The premium model critiques only the best ideas, focusing on unsupported claims, missing downside cases, accounting risks, and valuation assumptions.
If the same team used GPT-5.5 Pro for all three passes on all 50 ideas, the cost would be:
- $12.30 per pass × 3 passes × 50 ideas = $1,845/month
The routed workflow saves $1,771/month, or 96%, while preserving premium review where it matters.
📊 Quick Math: For 50 monthly investment ideas, routing extraction to GPT-5 mini and reserving Claude Opus 4.7 for the top 15 memos costs $73.63/month. Running every pass on GPT-5.5 Pro costs $1,845/month.
Practical scenario 4: Multi-company peer comparison at scale
A corporate strategy team wants to analyze competitive positioning across 25 public peers each month. The system ingests annual filings, recent transcripts, investor presentations converted to text, and prior summaries. Each peer pack contains 1,000,000 input tokens and produces 30,000 output tokens.
This is where context window matters. Models with 128k or 200k context require chunking and staged summarization. Models with 1M-2M context can handle the pack directly. Llama 4 Scout, with a 10,000,000-token context window, is especially cost-effective for huge ingestion tasks.
Monthly cost for 25 peer packs:
| Model | Cost per peer pack | 25 packs/month |
|---|---|---|
| Llama 4 Scout | $0.0890 | $2.23 |
| DeepSeek V4 Pro | $0.4611 | $11.53 |
| Gemini 2.5 Flash | $0.3750 | $9.38 |
| GPT-5.2 | $2.1700 | $54.25 |
| Gemini 3 Pro | $2.3600 | $59.00 |
| Claude Sonnet 4.6 | $3.4500 | $86.25 |
| Claude Opus 4.7 | $5.7500 | $143.75 |
| GPT-5.5 Pro | $35.4000 | $885.00 |
Recommendation: use Llama 4 Scout for first-pass peer ingestion and clustering because its 10M context and $0.08 input price are unmatched for raw scale. Use Gemini 3 Pro or GPT-5.2 for final cross-company synthesis when the evidence pack has been compressed to the most relevant metrics and excerpts.
For teams evaluating long-context alternatives, compare GPT-5 vs Gemini 3 Pro and GPT-5 vs DeepSeek V3.2.
When to use each model for financial analysis
Financial workflows have different risk levels. KPI extraction, transcript summarization, and table normalization are not the same as producing a buy/sell recommendation. Match the model to the decision.
Use low-cost models for extraction and normalization
Recommended models:
- GPT-5 mini: $0.25 / $2, 500k context
- DeepSeek V4 Pro: $0.435 / $0.87, 1M context
- Gemini 2.5 Flash: $0.30 / $2.50, 1M context
- Llama 4 Scout: $0.08 / $0.30, 10M context
Use these for:
- Extracting revenue, margins, guidance, capex, debt, and segment KPIs
- Converting filing sections into structured JSON
- Detecting changed risk-factor language
- Summarizing earnings call Q&A
- Building evidence packs for senior models
The output should be structured and auditable. Ask for citations by section name, table title, or quoted excerpt. Store extracted fields separately from generated commentary.
Use mid-premium models for synthesis and memo drafting
Recommended models:
- GPT-5.2: $1.75 / $14, 1M context
- Gemini 3 Pro: $2 / $12, 2M context
- Claude Sonnet 4.6: $3 / $15, 1M context
Use these for:
- Drafting investment memos
- Explaining quarter-over-quarter performance changes
- Synthesizing management commentary
- Comparing segment trends across periods
- Identifying bull and bear cases
- Creating analyst question lists
This is the best tier for most production finance workflows. It is strong enough for nuanced synthesis and still cheap enough to run repeatedly.
Use premium models for final review only
Recommended models:
- Claude Opus 4.7: $5 / $25, 1M context
- GPT-5.5 Pro: $30 / $180, 1.05M context
Use these for:
- Reviewing final memos before investment committee
- Challenging assumptions in downside scenarios
- Checking whether conclusions are supported by evidence
- Stress-testing accounting and disclosure risks
- Reviewing high-value M&A or activist situations
Premium models should see compressed evidence packs, not raw filings unless the decision value justifies the cost. Send the final memo, KPI table, source excerpts, and explicit review questions.
⚠️ Warning: Never let a model invent financial figures from memory. Require extracted numbers, source excerpts, and confidence flags for every KPI used in a memo.
A recommended architecture for finance teams
A reliable financial analysis pipeline has five stages.
1. Clean and segment the source documents
Remove duplicated headers, page numbers, table of contents noise, and broken OCR artifacts. Split filings into sections such as Business, Risk Factors, MD&A, Notes, Segment Information, Liquidity, and Controls. Clean input reduces cost and improves accuracy.
2. Extract structured data with a cheap model
Use GPT-5 mini, DeepSeek V4 Pro, Gemini 2.5 Flash, or Llama 4 Scout to produce JSON fields:
- Revenue, gross margin, operating margin, net income
- Segment revenue and segment operating income
- Free cash flow, capex, debt, cash, share count
- Guidance ranges and management commentary
- Named risks, litigation, impairments, restructuring charges
Run validation prompts that compare extracted values against source snippets.
3. Generate a compact evidence pack
Compress the filing into an analyst-ready packet: KPI table, major changes, source quotes, risk flags, and open questions. A good evidence pack is 10,000-40,000 tokens, not 300,000 tokens.
4. Draft the memo with a synthesis model
Use GPT-5.2, Gemini 3 Pro, or Claude Sonnet 4.6 to produce the memo. The prompt should include a fixed structure: thesis, quarter summary, KPI changes, guidance, risks, valuation considerations, and questions for management.
5. Review high-stakes memos with a premium model
Use Claude Opus 4.7 or GPT-5.5 Pro for final critique. Ask the model to identify unsupported claims, missing risks, inconsistent numbers, and alternative interpretations. This is where premium reasoning is worth paying for.
This staged approach controls both cost and quality. It also creates auditability, which is essential for regulated or high-stakes financial environments.
Budget benchmarks by team size
The table below uses realistic routed workflows rather than one-pass toy examples.
| Team type | Monthly volume | Recommended workflow | Estimated monthly API cost |
|---|---|---|---|
| Solo analyst | 20 filings/calls | GPT-5 mini extraction + GPT-5.2 synthesis | $5-$25 |
| IR team | 120 earnings calls/quarter | GPT-5 mini all calls + GPT-5.2 flagged calls | $7-$30 during earnings month |
| Equity research team | 80-company coverage | 5-pass extraction + Sonnet drafting | $35-$100 |
| Buy-side pod | 50 ideas/month | GPT-5 mini extraction + GPT-5.2 synthesis + Opus top ideas | $75-$200 |
| Corporate strategy | 25 peer packs/month | Llama 4 Scout ingestion + Gemini 3 Pro synthesis | $50-$150 |
| Enterprise research platform | 5,000 workflows/month | Routed multi-model pipeline with retries | $1,000-$8,000 |
The enterprise range is wider because retry behavior, user follow-up questions, and memo length dominate at scale. A system that allows analysts to chat with every filing for 20 turns will use far more output tokens than a batch pipeline that produces one structured memo.
Use AI Cost Check to model your own filing counts, token sizes, and routing strategy. The fastest way to get a reliable budget is to price three scenarios: baseline volume, earnings-season spike, and worst-case analyst usage.
Cost-saving tactics that do not reduce quality
Cache static filings
A 10-K does not change after ingestion. Store extracted KPIs, section summaries, and evidence packs. Do not resend the full filing for every analyst question. Route follow-ups against the compressed evidence pack.
Separate facts from interpretation
Use cheap models to extract facts and expensive models to interpret them. This reduces premium-model input size and improves auditability.
Use templates for outputs
A fixed memo format reduces output length. Output tokens are often more expensive than input tokens. For GPT-5.2, output costs $14 per 1M tokens, which is 8x the input price. For GPT-5.5 Pro, output costs $180 per 1M tokens, which is 6x its input price.
Route by risk level
A routine earnings-call summary does not need a premium review. A memo going to an investment committee does. Define routing rules before usage grows.
Compress before comparing peers
Peer comparisons become expensive when you send every raw filing into every prompt. Extract per-company evidence packs first, then compare the packs.
Frequently asked questions
How much does it cost to analyze a 10-K with AI?
A typical 10-K analysis using 180,000 input tokens and 8,000 output tokens costs about $0.061 on GPT-5 mini, $0.427 on GPT-5.2, $0.660 on Claude Sonnet 4.6, and $1.10 on Claude Opus 4.7. Use the AI Cost Check calculator to adjust the estimate for longer filings or multi-pass workflows.
Which AI model is best for financial report analysis?
Use GPT-5 mini, DeepSeek V4 Pro, or Gemini 2.5 Flash for KPI extraction and first-pass summaries. Use GPT-5.2, Gemini 3 Pro, or Claude Sonnet 4.6 for memo drafting. Use Claude Opus 4.7 or GPT-5.5 Pro only for final review of high-value decisions.
How many tokens are in a 10-K filing?
A clean 10-K usually lands around 120,000-220,000 tokens after extraction, depending on company complexity, tables, footnotes, and OCR quality. Large banks, insurers, and multinational companies often sit near the high end because of segment reporting and regulatory disclosures.
How much does it cost to analyze earnings call transcripts?
An earnings call transcript with 40,000 input tokens and a 4,000-token output costs about $0.018 on GPT-5 mini, $0.022 on Gemini 2.5 Flash, $0.126 on GPT-5.2, and $0.18 on Claude Sonnet 4.6. A 120-company earnings-season monitoring workflow can be run for under $10/month with smart routing.
Should financial teams use long-context models or chunk filings?
Use long-context models for full-filing review, cross-section reasoning, and peer comparisons. Use chunking only for extraction tasks or when the model context window is below the document size. For 10-Ks and peer packs, 1M+ context models reduce engineering complexity and preserve relationships across MD&A, footnotes, risks, and management commentary.
Estimate your financial analysis costs
AI financial report analysis is now cheap enough for every analyst workflow, but the model selection still matters. A routed pipeline can cover earnings calls, 10-Ks, KPI extraction, and investment memo drafts for tens to hundreds of dollars per month. A premium-only pipeline can spend 10x-100x more without improving every step.
Start by estimating your real token volumes in AI Cost Check. Compare models like GPT-5.2, Claude Sonnet 4.6, Gemini 3 Pro, and DeepSeek V4 Pro, then build a routing plan: cheap extraction, strong synthesis, premium final review.