LLM Cost Benchmarks by Industry
Updated 12 July 2026
Modeled monthly LLM inference costs across key industries and company sizes. These scenarios are calculated from published pricing lists and typical token consumption patterns. Use them to estimate your own spend and identify optimization opportunities.
- Modeled scenarios from published pricing, not customer data
- Input/output token ratios based on typical workload patterns
- Batch API discounts shown separately
- All prices current as of July 2026
Methodology
Pricing Source
All pricing extracted from official LLM provider documentation. Input and output rates are current July 2026.
Token Volumes
Modeled token consumption based on industry-typical workload sizes (queries, documents, chat turns).
Model Selection
Mid-tier models selected for each scenario (e.g., GPT-5.5, Claude Opus 4.8, Gemini 3.5 Flash).
Display Policy
Scenarios marked as modeled. Your actual costs will vary based on model choice and optimization.
E-Commerce & SaaS (Customer Support)
Customer support teams use LLMs to draft responses, classify tickets, and generate knowledge base articles. Token volume scales with ticket volume and response length.
| Company Size | Tickets/Month | Modeled Input Tokens | Modeled Output Tokens | Claude Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|---|---|---|
| Startup (10-person) | 500 | 2.5M | 5.0M | $137.50 | $162.50 | $48.75 |
| Scale-up (50-person) | 2,500 | 12.5M | 25.0M | $687.50 | $812.50 | $243.75 |
| Enterprise (500-person) | 25,000 | 125M | 250M | $6,875.00 | $8,125.00 | $2,437.50 |
Calculation Example (Scale-up): 2,500 tickets/month × 5K tokens per ticket = 12.5M input tokens. Responses averaging 10K tokens = 25M output tokens. Claude Opus 4.8: (12.5M × $5/M) + (25M × $25/M) = $62.50 + $625 = $687.50/month at list prices. All figures in this table are pure list-price token costs — real deployments add retries, system prompts, RAG context, and tooling on top; see hidden LLM costs for how to model that overhead.
Financial Services (Compliance & Risk)
Banks and fintech use LLMs for document analysis, transaction monitoring, and regulatory reporting. Token volume driven by document size and analysis depth.
| Organization Size | Documents/Month | Modeled Input Tokens | Modeled Output Tokens | Claude Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|---|---|---|
| Regional Bank | 5,000 | 50M | 15M | $625.00 | $687.50 | $162.50 |
| Mid-Tier Fintech | 25,000 | 250M | 75M | $3,125.00 | $3,437.50 | $812.50 |
| Large Bank/Institution | 100,000 | 1,000M | 300M | $12,500.00 | $13,750.00 | $3,250.00 |
Note: Financial institutions typically use smaller output ratios (0.3:1 output-to-input) because analysis results are often summary-form. Compliance workloads also benefit significantly from batch API discounts since most processing can accept 12+ hour latency.
Healthcare & Biotech (Data Analysis)
Healthcare uses LLMs for clinical note summarization, literature review, and research data extraction. High input-to-output ratio due to dense medical texts.
| Organization | Records/Month | Modeled Input Tokens | Modeled Output Tokens | Claude Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|---|---|---|
| Clinic (200 patients) | 200 | 15M | 3M | $150.00 | $165.00 | $40.50 |
| Hospital System | 5,000 | 375M | 75M | $3,750.00 | $4,125.00 | $1,012.50 |
| Research Institute | 20,000 | 1,500M | 300M | $15,000.00 | $16,500.00 | $4,050.00 |
Manufacturing & Supply Chain
Supply chain teams use LLMs for demand forecasting, quality inspection via image+text, and supplier communication. Multimodal workloads add complexity.
| Operation Scale | Analyses/Month | Modeled Input Tokens | Modeled Output Tokens | Claude Opus 4.8 | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|---|---|---|---|
| Single Facility | 1,000 | 8M | 4M | $140.00 | $155.00 | $39.00 |
| Multi-Facility (5x) | 5,000 | 40M | 20M | $700.00 | $775.00 | $195.00 |
| Global Network | 50,000 | 400M | 200M | $7,000.00 | $7,750.00 | $1,950.00 |
Cost Optimization Levers
1. Model Routing by Workload
Route complex reasoning to Claude Opus ($5M in) or GPT-5.5 ($5M in), simple queries to Gemini 3.5 Flash ($1.50M in). Average 30-40% savings if 60% of queries are simple.
Typical impact: -$300–$5,000/month depending on baseline.
2. Batch API for Offline Work
Apply 50% batch discount to any processing with 12+ hour latency (compliance reviews, nightly reports, async analysis). Most companies can batch 20–40% of workload.
Typical impact: -$200–$3,000/month (roughly 10–15% of total if batching half the workload).
3. Prompt Caching for Repeated Inputs
Cache long-form context (codebases, PDFs, regulations) with Anthropic prompt caching (90% discount on cache hits after 1-hour write cost). Most orgs can cache 30–60% of input tokens.
Typical impact: -$400–$2,000/month for high-volume teams.
4. Output Length Constraints
Reduce output token budgets via system prompts. If you can lower average output per query from 500 to 300 tokens, that's a 40% savings on output cost alone.
Typical impact: -$300–$2,500/month depending on baseline.
How to Use These Benchmarks
These scenarios are modeled, not surveys. Your actual costs depend on:
- Model choice: Switching from Claude Opus to Gemini 3.5 Flash cuts costs by ~75% if the model meets your quality bar.
- Token efficiency: Shorter prompts and outputs directly lower spend. A 20% reduction in average prompt length saves 20% on input cost.
- Caching strategy: If 50% of your workload reuses the same context, prompt caching can save 40–50% on those tokens.
- Batch usage: Batching 40% of your workload saves ~20% of total spend.
- Team velocity: More users = higher costs, but also more opportunities to optimize per-user cost via routing and caching.
For a detailed cost audit specific to your team, see How Much Does GPT-5 Cost? and LLM Cost Calculator. Both offer worked examples and per-model breakdowns.
Want to see your actual LLM spend broken down by team and model? FinOps LLM runs a free audit of your AI costs. Book free audit →