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fin-telligent series
AI Model Shopping Cart
A company wants to adopt AI. How much should it spend? Walk the aisles end to end: check the benchmarks, read the price tags, fill the cart, get the receipt. Across 450+ tracked LLMs.
Raw Table Previews
Joined Table Construction (SQL logic)
Joined Table Preview
520 rows × 22 columns. Every interactive section below is powered by this table.
Source: Artificial Analysis, via Kaggle (LLM API Pricing and Performance Benchmark Tracker)
🎓 Models take exams too. 8 exams, 5 skills.
📡 Source: Artificial Analysis, via Kaggle.
Judged on two dimensions: exam score and time to first token (TTFT). ⚡ = fastest third on TTFT.
🧩 No all round champion. Each department picks what it needs.
🏢 Meet our 100-person company. The cart will use these numbers later.
🏢 Every department needs a different mix.
★★★ core ★★☆ important ★☆☆ helpful
💲 Price Tag: what models cost and how 1,000 requests get billed
Three levers set the bill: which tier you pick, whether you cache, whether you batch.
Prices: LLM Price-Performance Tracker · live API rates as collected at snapshot
Tiers are cut on blended price, weighted 3 input tokens to 1 output token. Each tier starts roughly 6 to 10x above the last.
Tier cutoffs observed from the data.
🧮 How the Bill Adds Up
Cost = Input Tokens × Input Price + Output Tokens × Output Price
🔁 Cache · Reuse what you already sent. Repeated input costs 50% less.
Triggers when the prompt starts with content you sent recently. Why cheaper: repeated content needs no recompute.
📦 Batch · Trade speed for price. The whole bill costs 50% less.
Triggers when you submit jobs in bulk and wait up to 24h. Why cheaper: waiting lets providers fill idle GPU time.
Example: Finance / Tax asks to summarize a policy doc · 10K tokens in (8K repeated doc + 2K new question) · 1K tokens out · × 1,000 requests
🗣️
Ask
Input
Repeated tokens + New tokens = Input tokens
🔁
Cached Input
🆕
Fresh Input
💬
Answer
Output
⚡
Real-time
📦
Batch
🧾 Bill
No levers
With cache
Cache + batch
Blended Cost per 1M tokens
Blended = Bill ÷ Total tokens
Unit Budget = (I/O Ratio × Input price + Thinking Multiplier × Output price) ÷ (I/O Ratio + 1)
I/O Ratio = input : output tokens; Thinking Multiplier = extra output for reasoning (1 = no reasoning)
Industry default: I/O Ratio = 3, Thinking Multiplier = 1 brings Unit Budget back to Blended.
Different tasks, different mix.
Same price tag, bigger bill. Reasoning modes think in output tokens.
Illustrative token mix. Cache and batch discounts assumed to stack.
Total Model Cost = Price × Tokens
| Task | Best Estimate of Tokens per Person per Day | How It Adds Up |
|---|
Daily usage is an assumption by task type, adjustable.
🛒 Cart
Pick a department, pick a pack, watch the register run.
Who’s shopping
Tap a department to drop it in the cart. Tap its card’s × to lift it back out.
Pick a Pack
Monthly Cost = Σ ( Team Size × Task Share × Daily Tokens per Person × Days × Unit Budget )
Unit Budget = (I/O Ratio × Input price + Thinking Multiplier × Output price) ÷ (I/O Ratio + 1)
Task presets are illustrative usage assumptions. 1 token ≈ 0.75 words; 1 page ≈ 500 words.
Latest Models & Prices
Your Cart
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New Shopping Cart
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🔍 Checked by Nimble: Search finds the latest models, Extract reads today's prices.
Model Supermarket · Monthly Receipt
Illustrative company, monthly
Fill the Cart to see tokens land here.
📦 Business
Every token lands on a business outcome.
Placeholder volumes. Swap in your own.
Selected (up to 3)
Benchmark Comparison
All 8 benchmarks, normalized 0–100 across the full 520-model field.
Cost vs. Budget Ceiling
Monthly cost per selected model against your ceiling.
Modeling note: the cost and gain formulas in the brief are both pure rate × months (no fixed cost), so a crossing point only exists if there's an upfront cost to amortize. We added this one editable "setup & integration cost" input so breakeven has somewhere to move — set it to $0 to see the pure rate comparison instead.
Cumulative Cost vs. Cumulative Value
24-month horizon. One cost line per selected model; a single shared value line (it doesn't depend on model choice).
Cost Explosion
Cost per request-batch (context tokens × concurrent sessions) as context grows.
Latency Degradation
Estimated time-to-first-token as context grows. SLA threshold: 2s.
