AI Coding Subscription Value & Pareto Frontier Matrix

Efficiency Frontier (Intelligence vs Monthly Cost)

5 Frontier Anchors

X-Axis Scaling

Budget Cap Filter

Free/Contributor APIs

Benchmark Focus

Pareto Optimal Sweet Spot: Claude 3.5 / 3.7 Sonnet ($20 Cursor / Claude Pro) & DeepSeek-Coder-V2.5 API ($3) dominate the cost-per-intelligence curve.

Points on the dotted green frontier offer non-dominated coding intelligence per dollar spent.

Claude 3.7 Sonnet (Cursor Pro $20)

Pareto Optimal

Cursor / Anthropic Bundle

$20 / month

Intelligence Score

92.4 / 100

Cost / Intel Unit

$0.22

Per index point

Context Window

200K

Tokens usable

SWE-bench Ver.

70.3%

Verified fix rate

Rate Cap / Mo

500 fast

+ unmetered slow

Subscription Matrix (v4.2 Data)

14 subscriptions evaluated

Status	Subscription Plan	Base Model	Price	Score	CPI

Tool Stacking & ROI Simulator

Monthly Developer Prompt Queries 800 queries

Avg Context Size per Request 45K tokens

Optimal Stack Recommendation: Cursor Pro ($20) + DeepSeek PayG ($3)

Effective Monthly Spend: $23.00 / mo

Savings vs $200 Solo Pro Tier: $177.00 / mo (88.5% saved)

Understanding the stored cost-score frontier

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This page draws from a stored subscription table, not a live price or benchmark feed. The cost score frontier sorts price ascending and score descending at equal price. It retains a row only when its score is strictly higher than every earlier retained score. In this four row subset, a nine dollar row at eighty five point five, ten at eighty seven point eight, and twenty at ninety two point four are retained. A thirty dollar row at ninety point five is excluded: the twenty dollar row is cheaper and scores higher. These are the page stored fixture values, not current purchasing advice or verified model capability. Equal cost and equal score rows are handled by a strict comparison. Two stored twenty dollar rows share a ninety two point four score. The earlier row can enter, but the second cannot beat the maximum, so only one label appears as a frontier anchor. A forty dollar row with the same score is also excluded because it costs more for no gain in this single score. Equal coordinate duplicates are equivalent on these axes, even though this algorithm keeps only the first. Features, privacy, reliability, and query limits are not included in the dominance test. Changing benchmark focus replaces the vertical score using a fixed transformation. The S W E score is multiplied by one point two five; Human Eval by point nine eight; throughput is fifty plus speed divided by one hundred thirty times forty five. A stored speed one hundred ten produces eighty eight point zero eight on the throughput axis. These transformations are not calibrated equivalence between benchmarks. The chart filters budget and contributor rows before computing its frontier. Log scale changes horizontal geometry, and clamps zero dollar points to one so the logarithm can be drawn; it does not make a free tier cost one dollar. The stacking panel does not solve a subscription optimization problem. It selects one fixed twenty three dollar bundle whenever queries are at most eighteen hundred and context at most ninety thousand tokens. It selects a two hundred dollar tier if either threshold is exceeded. The lower branch reports two hundred minus twenty three equals one hundred seventy seven dollars, or eighty eight point five percent saved, against a fixed baseline. CSV and JSON export compute their frontier from the original composite score even when the chart uses another mode. Inspect the exported objective before treating those anchors as the chart result.