fix(stats): resolved stats openai-codex cost fallback to OpenAI rates

- Updated Codex model pricing data to include non-zero input/output/cacheRead rates.
- Added generate-models fallback to copy billable OpenAI costs into bundled openai-codex models.
- Added catalog-cost helpers to fallback openai-codex pricing to openai and compute token totals.
- Added regression tests for openai-codex cost parity and db backfill, and documented the fix in changelog.

packages/ai/CHANGELOG.md

@@ -2,6 +2,10 @@
 
 ## [Unreleased]
 
+### Fixed
+
+- Fixed OpenAI Codex GPT model pricing by inheriting matching OpenAI catalog rates for zero-priced discovered Codex entries.
+
 ## [14.5.3] - 2026-04-27
 ### Added
 

packages/ai/scripts/generate-models.ts

@@ -179,6 +179,37 @@ function applyPremiumMultiplierOverrides(models: readonly Model[]): Model[] {
 		};
 	});
 }
+function hasBillableCost(cost: Model["cost"]): boolean {
+	return cost.input !== 0 || cost.output !== 0 || cost.cacheRead !== 0 || cost.cacheWrite !== 0;
+}
+
+function applyCodexPricingFallback(models: readonly Model[]): Model[] {
+	const openAIModels = new Map(
+		models
+			.filter(model => model.provider === "openai" && hasBillableCost(model.cost))
+			.map(model => [model.id, model.cost]),
+	);
+
+	return models.map(model => {
+		if (model.provider !== "openai-codex" || model.api !== "openai-codex-responses") {
+			return model;
+		}
+		if (hasBillableCost(model.cost)) {
+			return model;
+		}
+
+		const openAICost = openAIModels.get(model.id);
+		if (!openAICost) {
+			return model;
+		}
+
+		return {
+			...model,
+			cost: { ...openAICost },
+		};
+	});
+}
+
 const ANTIGRAVITY_ENDPOINT = "https://daily-cloudcode-pa.sandbox.googleapis.com";
 
 async function getOAuthCredentialsFromStorage(provider: OAuthProvider): Promise<OAuthCredentials | null> {
@@ -334,6 +365,7 @@ async function generateModels() {
 
 	allModels = applyGlobalModelsDevFallback(allModels, modelsDevModels);
 	allModels = applyPremiumMultiplierOverrides(allModels);
+	allModels = applyCodexPricingFallback(allModels);
 	applyGeneratedModelPolicies(allModels);
 	linkOpenAIPromotionTargets(allModels);
 

packages/ai/src/models.json

@@ -33899,9 +33899,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.25,
+				"output": 10,
+				"cacheRead": 0.125,
 				"cacheWrite": 0
 			},
 			"contextWindow": 400000,
@@ -33927,9 +33927,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.25,
+				"output": 10,
+				"cacheRead": 0.125,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -33983,9 +33983,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.25,
+				"output": 10,
+				"cacheRead": 0.13,
 				"cacheWrite": 0
 			},
 			"contextWindow": 400000,
@@ -34011,9 +34011,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.25,
+				"output": 10,
+				"cacheRead": 0.125,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34039,9 +34039,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.25,
+				"output": 10,
+				"cacheRead": 0.125,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34067,9 +34067,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 0.25,
+				"output": 2,
+				"cacheRead": 0.025,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34095,9 +34095,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.75,
+				"output": 14,
+				"cacheRead": 0.175,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34123,9 +34123,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.75,
+				"output": 14,
+				"cacheRead": 0.175,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34151,9 +34151,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 1.75,
+				"output": 14,
+				"cacheRead": 0.175,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34206,9 +34206,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 2.5,
+				"output": 15,
+				"cacheRead": 0.25,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34234,9 +34234,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 0.75,
+				"output": 4.5,
+				"cacheRead": 0.075,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34262,9 +34262,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 0.2,
+				"output": 1.25,
+				"cacheRead": 0.02,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -34290,9 +34290,9 @@
 				"image"
 			],
 			"cost": {
-				"input": 0,
-				"output": 0,
-				"cacheRead": 0,
+				"input": 5,
+				"output": 30,
+				"cacheRead": 0.5,
 				"cacheWrite": 0
 			},
 			"contextWindow": 272000,
@@ -52775,4 +52775,4 @@
 			}
 		}
 	}
-}
+}

packages/ai/test/models-cost.test.ts

@@ -70,4 +70,23 @@ describe("calculateCost", () => {
 		expect(usage.cost.cacheWrite).toBeCloseTo(0.08, 8);
 		expect(usage.cost.total).toBeCloseTo(2.18, 8);
 	});
+
+	it("prices OpenAI Codex GPT models from the matching OpenAI catalog entry", () => {
+		const openAIModel = getBundledModel("openai", "gpt-5.4");
+		const codexModel = getBundledModel("openai-codex", "gpt-5.4");
+		const usage: Usage = {
+			input: 1000,
+			output: 500,
+			cacheRead: 200,
+			cacheWrite: 0,
+			totalTokens: 1700,
+			cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+		};
+
+		expect(codexModel.cost).toEqual(openAIModel.cost);
+
+		calculateCost(codexModel, usage);
+
+		expect(usage.cost.total).toBeCloseTo(0.01005, 8);
+	});
 });

packages/stats/CHANGELOG.md

@@ -2,6 +2,10 @@
 
 ## [Unreleased]
 
+### Fixed
+
+- Fixed GPT cost reporting by deriving missing OpenAI Codex costs from the model catalog and backfilling existing zero-cost rows.
+
 ## [13.6.0] - 2026-03-03
 ### Fixed
 

packages/stats/src/db.ts

@@ -1,5 +1,6 @@
 import { Database } from "bun:sqlite";
 import * as fs from "node:fs/promises";
+import { type GeneratedProvider, getBundledModel, type Usage } from "@oh-my-pi/pi-ai";
 import { getConfigRootDir, getStatsDbPath } from "@oh-my-pi/pi-utils";
 import type {
 	AggregatedStats,
@@ -12,7 +13,19 @@ import type {
 	TimeSeriesPoint,
 } from "./types";
 
-const DB_PATH = getStatsDbPath();
+type ModelCost = { input: number; output: number; cacheRead: number; cacheWrite: number };
+type UsageCost = Usage["cost"];
+type CostTokens = Pick<Usage, "input" | "output" | "cacheRead" | "cacheWrite">;
+
+interface CostBackfillRow {
+	id: number;
+	provider: string;
+	model: string;
+	input_tokens: number;
+	output_tokens: number;
+	cache_read_tokens: number;
+	cache_write_tokens: number;
+}
 
 let db: Database | null = null;
 
@@ -25,7 +38,7 @@ export async function initDb(): Promise<Database> {
 	// Ensure directory exists
 	await fs.mkdir(getConfigRootDir(), { recursive: true });
 
-	db = new Database(DB_PATH);
+	db = new Database(getStatsDbPath());
 	db.exec("PRAGMA journal_mode = WAL");
 
 	// Create tables
@@ -74,9 +87,96 @@ export async function initDb(): Promise<Database> {
 		db.exec("ALTER TABLE messages ADD COLUMN premium_requests REAL NOT NULL DEFAULT 0");
 	}
 	db.exec("UPDATE messages SET premium_requests = 0 WHERE premium_requests IS NULL");
+	backfillMissingCatalogCosts(db);
 	return db;
 }
 
+function hasBillableCost(cost: ModelCost): boolean {
+	return cost.input !== 0 || cost.output !== 0 || cost.cacheRead !== 0 || cost.cacheWrite !== 0;
+}
+
+function getBundledModelCost(provider: string, modelId: string): ModelCost | null {
+	const model = getBundledModel(provider as GeneratedProvider, modelId);
+	return model?.cost ?? null;
+}
+
+function getCatalogCost(provider: string, modelId: string): ModelCost | null {
+	const primaryCost = getBundledModelCost(provider, modelId);
+	if (primaryCost && hasBillableCost(primaryCost)) {
+		return primaryCost;
+	}
+
+	if (provider === "openai-codex") {
+		const openAICost = getBundledModelCost("openai", modelId);
+		if (openAICost && hasBillableCost(openAICost)) {
+			return openAICost;
+		}
+	}
+
+	return null;
+}
+
+function calculateCatalogCost(provider: string, modelId: string, tokens: CostTokens): UsageCost | null {
+	const cost = getCatalogCost(provider, modelId);
+	if (!cost) return null;
+
+	const input = (cost.input / 1_000_000) * tokens.input;
+	const output = (cost.output / 1_000_000) * tokens.output;
+	const cacheRead = (cost.cacheRead / 1_000_000) * tokens.cacheRead;
+	const cacheWrite = (cost.cacheWrite / 1_000_000) * tokens.cacheWrite;
+
+	return {
+		input,
+		output,
+		cacheRead,
+		cacheWrite,
+		total: input + output + cacheRead + cacheWrite,
+	};
+}
+
+function resolveStoredCost(stats: MessageStats): UsageCost {
+	if (stats.usage.cost.total !== 0) {
+		return stats.usage.cost;
+	}
+
+	return calculateCatalogCost(stats.provider, stats.model, stats.usage) ?? stats.usage.cost;
+}
+
+function backfillMissingCatalogCosts(database: Database): void {
+	const rows = database
+		.prepare(`
+			SELECT id, provider, model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens
+			FROM messages
+			WHERE cost_total = 0 AND total_tokens > 0
+		`)
+		.all() as CostBackfillRow[];
+
+	if (rows.length === 0) return;
+
+	const update = database.prepare(`
+		UPDATE messages
+		SET cost_input = ?, cost_output = ?, cost_cache_read = ?, cost_cache_write = ?, cost_total = ?
+		WHERE id = ?
+	`);
+
+	const applyBackfill = database.transaction(() => {
+		for (const row of rows) {
+			const cost = calculateCatalogCost(row.provider, row.model, {
+				input: row.input_tokens,
+				output: row.output_tokens,
+				cacheRead: row.cache_read_tokens,
+				cacheWrite: row.cache_write_tokens,
+			});
+
+			if (!cost || cost.total === 0) continue;
+
+			update.run(cost.input, cost.output, cost.cacheRead, cost.cacheWrite, cost.total, row.id);
+		}
+	});
+
+	applyBackfill();
+}
+
 /**
  * Get the stored offset for a session file.
  */
@@ -120,6 +220,7 @@ export function insertMessageStats(stats: MessageStats[]): number {
 	let inserted = 0;
 	const insert = db.transaction(() => {
 		for (const s of stats) {
+			const cost = resolveStoredCost(s);
 			const result = stmt.run(
 				s.sessionFile,
 				s.entryId,
@@ -138,11 +239,11 @@ export function insertMessageStats(stats: MessageStats[]): number {
 				s.usage.cacheWrite,
 				s.usage.totalTokens,
 				s.usage.premiumRequests ?? 0,
-				s.usage.cost.input,
-				s.usage.cost.output,
-				s.usage.cost.cacheRead,
-				s.usage.cost.cacheWrite,
-				s.usage.cost.total,
+				cost.input,
+				cost.output,
+				cost.cacheRead,
+				cost.cacheWrite,
+				cost.total,
 			);
 			if (result.changes > 0) inserted++;
 		}

packages/stats/test/db-cost.test.ts

@@ -0,0 +1,130 @@
+import { Database } from "bun:sqlite";
+import { afterEach, beforeEach, describe, expect, it } from "bun:test";
+import * as os from "node:os";
+import * as path from "node:path";
+import { getBundledModel } from "@oh-my-pi/pi-ai";
+import { getAgentDir, getStatsDbPath, setAgentDir, TempDir } from "@oh-my-pi/pi-utils";
+import { closeDb, getRecentRequests, initDb, insertMessageStats } from "../src/db";
+import type { MessageStats } from "../src/types";
+
+const originalConfigDir = process.env.PI_CONFIG_DIR;
+const originalAgentDir = getAgentDir();
+let tempDir: TempDir | null = null;
+
+beforeEach(() => {
+	tempDir = TempDir.createSync("@pi-stats-db-");
+	const configDir = path.relative(os.homedir(), tempDir.join("config"));
+	process.env.PI_CONFIG_DIR = configDir;
+	setAgentDir(path.join(os.homedir(), configDir, "agent"));
+});
+
+afterEach(() => {
+	closeDb();
+	if (originalConfigDir === undefined) {
+		delete process.env.PI_CONFIG_DIR;
+	} else {
+		process.env.PI_CONFIG_DIR = originalConfigDir;
+	}
+	setAgentDir(originalAgentDir);
+	tempDir?.removeSync();
+	tempDir = null;
+});
+
+function createCodexGptStats(entryId: string): MessageStats {
+	return {
+		sessionFile: "/tmp/session.jsonl",
+		entryId,
+		folder: "/tmp/project",
+		model: "gpt-5.4",
+		provider: "openai-codex",
+		api: "openai-codex-responses",
+		timestamp: Date.now(),
+		duration: 1000,
+		ttft: 100,
+		stopReason: "stop",
+		errorMessage: null,
+		usage: {
+			input: 1000,
+			output: 500,
+			cacheRead: 200,
+			cacheWrite: 0,
+			totalTokens: 1700,
+			cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+		},
+	};
+}
+
+function expectedCodexGptCost() {
+	const cost = getBundledModel("openai-codex", "gpt-5.4").cost;
+	const input = (cost.input / 1_000_000) * 1000;
+	const output = (cost.output / 1_000_000) * 500;
+	const cacheRead = (cost.cacheRead / 1_000_000) * 200;
+	return {
+		input,
+		output,
+		cacheRead,
+		total: input + output + cacheRead,
+	};
+}
+
+describe("stats GPT cost correction", () => {
+	it("stores catalog-derived cost when OpenAI Codex session usage has zero cost", async () => {
+		await initDb();
+
+		insertMessageStats([createCodexGptStats("inserted")]);
+
+		const expected = expectedCodexGptCost();
+		const request = getRecentRequests(1)[0];
+		expect(expected.total).toBeGreaterThan(0);
+		expect(request?.usage.cost.input).toBeCloseTo(expected.input, 8);
+		expect(request?.usage.cost.output).toBeCloseTo(expected.output, 8);
+		expect(request?.usage.cost.cacheRead).toBeCloseTo(expected.cacheRead, 8);
+		expect(request?.usage.cost.total).toBeCloseTo(expected.total, 8);
+	});
+
+	it("backfills existing zero-cost OpenAI Codex GPT rows on database init", async () => {
+		await initDb();
+		closeDb();
+
+		const database = new Database(getStatsDbPath());
+		database
+			.prepare(`
+				INSERT INTO messages (
+					session_file, entry_id, folder, model, provider, api, timestamp,
+					duration, ttft, stop_reason, error_message,
+					input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, total_tokens, premium_requests,
+					cost_input, cost_output, cost_cache_read, cost_cache_write, cost_total
+				) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+			`)
+			.run(
+				"/tmp/session.jsonl",
+				"backfilled",
+				"/tmp/project",
+				"gpt-5.4",
+				"openai-codex",
+				"openai-codex-responses",
+				Date.now(),
+				1000,
+				100,
+				"stop",
+				null,
+				1000,
+				500,
+				200,
+				0,
+				1700,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+			);
+		database.close();
+
+		await initDb();
+
+		const request = getRecentRequests(1)[0];
+		expect(request?.usage.cost.total).toBeCloseTo(expectedCodexGptCost().total, 8);
+	});
+});