Methodology

Every number in TokenLens is derived from a documented calculation. This page explains each one.

Token cost

The base formula for API cost given a fixed input/output token count and a model with published per-token pricing:

cost = (input_tokens / 1,000,000 × input_price_per_1M)
     + (output_tokens / 1,000,000 × output_price_per_1M)

Cost per 1M tokens

To normalise across models, we compute an effective blended cost per 1M tokens using the default output ratio (35% output, 65% input):

cost_per_1M = (1 - output_ratio) × input_price_per_1M
              + output_ratio  × output_price_per_1M

This allows apples-to-apples comparison across models with different input/output price splits. The default ratio of 0.35 reflects typical coding workloads.

Token cap estimation

Subscription plans rarely publish a simple monthly token budget. We estimate it using the first available source in priority order:

1. monthly_tokens_cap           (stated directly)
2. weekly_tokens_cap  × 4.33   (weeks per month)
3. daily_tokens_cap   × 30.4   (days per month)
4. usage_multiplier   × cap(referenced_plan)
5. fair_use / unknown           → shown as "—"

Multiplier resolution is capped at depth 3 to prevent circular references. All derived caps are labelled with their derivation method.

Dev-Hour Equivalent (DHE)

A DHE represents the token cost of approximately one hour of AI-assisted developer work, anchored to a specific task archetype. Each benchmark defines:

token_budget = { input_tokens, output_tokens }
assumed_human_work = 60 minutes

The four benchmark archetypes are:

• Light debug — small bug fix with diagnosis, patch, and unit test (25K in / 15K out)
• Medium feature — multi-file feature with validation and tests (80K in / 60K out)
• Large refactor — module restructure with regression tests (180K in / 120K out)
• Long-context review — large codebase review with patch notes (220K in / 70K out)

To account for real-world overhead, token budgets are multiplied by efficiency factors:

adjusted_tokens = budget × verbosity_multiplier × retry_multiplier
dhe_cost = token_cost(adjusted_input, adjusted_output, model)

A verbosity_multiplier of 1.2 means the model produces 20% more tokens than the theoretical minimum. A retry_multiplier of 1.1 means 10% of requests are retried.

Scenario modeling

The scenario calculator projects monthly token usage and cost from usage parameters:

total_tokens  = hours_per_day × days_per_month × tokens_per_hour
output_tokens = total_tokens × output_ratio
input_tokens  = total_tokens × (1 - output_ratio)
api_cost      = token_cost(input_tokens, output_tokens, model)

Subscription viability is assessed by comparing api_cost against the plan's monthly price, and total_tokens against the estimated token cap.

Confidence tiers

• High — sourced directly from official pricing pages with explicit token counts.
• Medium — derived from documented limits or community reporting with cross-verification.
• Estimated — inferred from marketing language, multipliers, or historical patterns. Treat with caution.

Data freshness

All data is manually maintained. Each plan record includes last_verified and effective_from dates. AI pricing changes frequently — always verify against the source URL before making purchasing decisions.