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* feat(codex): compute Codex credit usage (#408, #495) Codex/ChatGPT subscription users consume credits, a unit separate from API dollars: usage is billed as credits-per-million-tokens at per-model rates that differ from the API USD pricing CodeBurn uses for cost. So the reported dollar cost does not match what credits actually consume. Add a credit engine sourced from the official Codex credit rates (developers.openai.com/codex/pricing): GPT-5.5 125/12.5/750, GPT-5.4 62.5/6.25/375, GPT-5.4 mini 18.75/1.875/113 credits per 1M input/cached/output tokens. Surface per-model credit usage in `codeburn models` JSON output (credits field; null for non-Codex or unknown models). models-report already folds reasoning into output and keeps non-cached input + cached-read separately, which is exactly what the credit rates expect, so the figure is exact. Engine + computation are unit-tested. UI display surfaces (the models table, the TUI dashboard, the menubar "credits" view) are intentionally left for a follow-up so the display choice can be decided. * feat(menubar): opt-in Codex credits display metric (#408, #495) Surface Codex credit usage in the menubar as a selectable metric, without changing the default. Cost ($) stays the default in both the menubar and the CLI; credits only appear when explicitly chosen. - TS: buildMenubarPayloadForRange computes the period's Codex credits (via the tested aggregateModels, so reasoning/cached are handled) and exposes current.codexCredits in the menubar JSON. - Swift: new DisplayMetric.credits, a "Credits (Codex)" option in the metric picker, decodes codexCredits, and renders it in the menu-bar title. Default metric remains .cost.
53 lines
2.3 KiB
TypeScript
53 lines
2.3 KiB
TypeScript
import { describe, expect, it } from 'vitest'
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import { codexCredits, codexCreditRate } from '../src/codex-credits.js'
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describe('codexCreditRate', () => {
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it('resolves the documented per-model rates', () => {
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expect(codexCreditRate('gpt-5.5')).toEqual({ input: 125, cachedInput: 12.5, output: 750 })
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expect(codexCreditRate('gpt-5.4')).toEqual({ input: 62.5, cachedInput: 6.25, output: 375 })
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expect(codexCreditRate('gpt-5.4-mini')).toEqual({ input: 18.75, cachedInput: 1.875, output: 113 })
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})
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it('tolerates codex suffix variants and casing', () => {
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expect(codexCreditRate('GPT-5.5-codex')?.input).toBe(125)
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expect(codexCreditRate('gpt-5.4-codex-mini')?.input).toBe(18.75)
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})
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it('returns null for models with no known credit rate', () => {
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expect(codexCreditRate('gpt-4o')).toBeNull()
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expect(codexCreditRate('claude-opus-4-8')).toBeNull()
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})
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})
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describe('codexCredits', () => {
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it('charges 1M input tokens at the input rate', () => {
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expect(codexCredits('gpt-5.5', { inputTokens: 1_000_000, cachedReadTokens: 0, outputTokens: 0 })).toBe(125)
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})
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it('charges 1M output tokens at the output rate', () => {
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expect(codexCredits('gpt-5.5', { inputTokens: 0, cachedReadTokens: 0, outputTokens: 1_000_000 })).toBe(750)
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})
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it('charges cache-read tokens at the cheaper cached rate', () => {
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expect(codexCredits('gpt-5.5', { inputTokens: 0, cachedReadTokens: 1_000_000, outputTokens: 0 })).toBe(12.5)
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})
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it('folds reasoning tokens into the output rate', () => {
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// 500k output + 500k reasoning = 1M output-billed => 750 credits.
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expect(codexCredits('gpt-5.5', { inputTokens: 0, cachedReadTokens: 0, outputTokens: 500_000, reasoningTokens: 500_000 })).toBe(750)
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})
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it('sums a mixed record (gpt-5.4)', () => {
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// 2M input (125) + 1M cached (6.25) + 0.5M output (187.5) = 318.75
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const credits = codexCredits('gpt-5.4', { inputTokens: 2_000_000, cachedReadTokens: 1_000_000, outputTokens: 500_000 })
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expect(credits).toBeCloseTo(125 + 6.25 + 187.5, 6)
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})
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it('clamps negative / non-finite token counts to 0', () => {
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expect(codexCredits('gpt-5.5', { inputTokens: -100, cachedReadTokens: NaN, outputTokens: 1_000_000 })).toBe(750)
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})
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it('returns null for an unknown model', () => {
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expect(codexCredits('gpt-4o', { inputTokens: 1_000_000, cachedReadTokens: 0, outputTokens: 0 })).toBeNull()
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})
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})
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