can1357
d8c5659d9a
- Updated GPT-5.6 context window floor to 1,000,000 tokens across discovery, policies, and tests. - Updated model configurations and pricing parameters in catalog models JSON.
701 lines
22 KiB
TypeScript
701 lines
22 KiB
TypeScript
import { describe, expect, it } from "bun:test";
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import { Effort } from "@oh-my-pi/pi-catalog/effort";
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import type { Api, ModelSpec, Provider } from "@oh-my-pi/pi-catalog/types";
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import {
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applyAntigravityPricingFallback,
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applyGeneratedModelPolicies,
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applyOllamaCloudOutputCap,
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linkOpenAIPromotionTargets,
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} from "../scripts/generated-policies";
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function createSpec<TApi extends Api>(overrides: {
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id: string;
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api: TApi;
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provider: Provider;
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reasoning?: boolean;
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contextWindow?: number;
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maxTokens?: number;
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priority?: number;
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applyPatchToolType?: "freeform" | "function";
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cost?: ModelSpec<TApi>["cost"];
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thinking?: ModelSpec<TApi>["thinking"];
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}): ModelSpec<TApi> {
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return {
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id: overrides.id,
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name: overrides.id,
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api: overrides.api,
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provider: overrides.provider,
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baseUrl: "https://example.com",
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reasoning: overrides.reasoning ?? true,
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thinking: overrides.thinking,
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input: ["text"],
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cost: overrides.cost ?? { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: overrides.contextWindow ?? 200000,
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maxTokens: overrides.maxTokens ?? 32000,
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priority: overrides.priority,
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applyPatchToolType: overrides.applyPatchToolType,
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};
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}
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describe("generated model policies", () => {
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it("re-bakes thinking metadata and applies parsed catalog corrections", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "claude-opus-4-5",
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api: "anthropic-messages",
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provider: "anthropic",
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// Stale baked metadata must be replaced by the deriver's output.
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thinking: { mode: "budget", efforts: [Effort.High] },
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cost: { input: 0, output: 0, cacheRead: 1.5, cacheWrite: 18.75 },
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contextWindow: 1000000,
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}),
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createSpec({
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id: "anthropic.claude-opus-4-6-v1:0",
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api: "bedrock-converse-stream",
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provider: "amazon-bedrock",
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cost: { input: 0, output: 0, cacheRead: 1.5, cacheWrite: 18.75 },
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contextWindow: 1000000,
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}),
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createSpec({
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id: "gpt-5.2-codex",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 400000,
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}),
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createSpec({
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id: "gpt-5.4-mini",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 400000,
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priority: 2,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.thinking).toEqual({
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mode: "anthropic-budget-effort",
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efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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});
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expect(models[0]?.cost.cacheRead).toBe(0.5);
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expect(models[0]?.cost.cacheWrite).toBe(6.25);
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expect(models[1]?.thinking).toEqual({
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mode: "anthropic-adaptive",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.Max],
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});
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expect(models[1]?.cost.cacheRead).toBe(0.5);
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expect(models[1]?.cost.cacheWrite).toBe(6.25);
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expect(models[1]?.contextWindow).toBe(1000000);
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expect(models[2]?.contextWindow).toBe(272000);
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expect(models[3]?.contextWindow).toBe(272000);
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expect(models[3]?.priority).toBe(1);
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});
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it("applies GPT-5.6 off and long-context pricing through request-model aliases", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({ id: "gpt-5.6", api: "openai-responses", provider: "openai" }),
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createSpec({ id: "gpt-5.6-luna", api: "openai-responses", provider: "openai" }),
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{
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...createSpec({ id: "gpt-5.6-sol-pro", api: "openai-responses", provider: "openai" }),
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requestModelId: "gpt-5.6-sol",
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},
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{
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...createSpec({ id: "gpt-5.6-terra-pro", api: "openai-responses", provider: "openai" }),
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requestModelId: "gpt-5.6-terra",
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},
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createSpec({ id: "gpt-5.6", api: "openai-responses", provider: "openrouter" }),
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];
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applyGeneratedModelPolicies(models);
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for (const model of models.slice(0, 4)) {
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expect(model.compat).toMatchObject({ reasoningDisableMode: "none-effort" });
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expect(model.cost.longContext?.inputThreshold).toBe(272_000);
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}
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expect(models[0]?.cost.longContext).toMatchObject({ input: 10, output: 45 });
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expect(models[1]?.cost.longContext).toMatchObject({ input: 0.4, output: 1.8 });
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expect(models[2]?.cost.longContext).toMatchObject({ input: 10, output: 45 });
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expect(models[3]?.cost.longContext).toMatchObject({ input: 4, output: 18 });
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expect(models[4]?.compat).toBeUndefined();
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expect(models[4]?.cost.longContext).toBeUndefined();
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});
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it("floors GPT-5.6 Codex-transport context windows at 1M (openai/codex#38917)", () => {
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const models: ModelSpec<Api>[] = [
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// Codex discovery/registry still reports the stale 272000 for these.
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createSpec({
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id: "gpt-5.6-luna",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 272000,
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}),
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createSpec({
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id: "gpt-5.6-sol",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 272000,
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}),
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createSpec({
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id: "gpt-5.6-terra",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 272000,
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}),
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// The first-party API-key entry uses openai-responses and is untouched.
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createSpec({ id: "gpt-5.6-sol", api: "openai-responses", provider: "openai", contextWindow: 1050000 }),
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// The Codex registry actively reports 272K for this alias, so the
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// luna/sol/terra correction must not overwrite it.
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createSpec({
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id: "gpt-daybreak-blue-latest",
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api: "openai-codex-responses",
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provider: "openai-codex",
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contextWindow: 272000,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.contextWindow).toBe(1_000_000);
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expect(models[1]?.contextWindow).toBe(1_000_000);
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expect(models[2]?.contextWindow).toBe(1_000_000);
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expect(models[3]?.contextWindow).toBe(1050000);
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expect(models[4]?.contextWindow).toBe(272000);
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});
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it("pins Claude Mythos 5 first-party Anthropic catalog metadata", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "claude-mythos-5",
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api: "anthropic-messages",
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provider: "anthropic",
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.contextWindow).toBe(1_000_000);
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expect(models[0]?.maxTokens).toBe(128_000);
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expect(models[0]?.cost).toEqual({ input: 10, output: 50, cacheRead: 1, cacheWrite: 12.5 });
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expect(models[0]?.thinking).toEqual({
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mode: "anthropic-adaptive",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh, Effort.Max],
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supportsDisplay: true,
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});
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});
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it("preserves QwenCloud's provider-authored qwen3.8 effort ladders", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "qwen3.8-max-preview",
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api: "openai-completions",
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provider: "alibaba-token-plan",
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.High, Effort.XHigh],
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requiresEffort: true,
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},
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}),
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createSpec({
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id: "qwen3.8-max",
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api: "openai-completions",
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provider: "alibaba-token-plan",
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.XHigh],
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defaultLevel: Effort.XHigh,
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},
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models.map(model => model.thinking)).toEqual([
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{
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mode: "effort",
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efforts: [Effort.Low, Effort.High, Effort.XHigh],
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requiresEffort: true,
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},
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{
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.XHigh],
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defaultLevel: Effort.XHigh,
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},
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]);
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});
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it("pins zai glm-5.2 base id to 1M context", () => {
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const models = [
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createSpec({
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id: "glm-5.2",
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api: "anthropic-messages",
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provider: "zai",
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contextWindow: 200_000,
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maxTokens: 8192,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.contextWindow).toBe(1_000_000);
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expect(models[0]?.maxTokens).toBe(131_072);
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});
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it("pins zai glm-5.3 to 1M context and derives uniform low/high/max thinking with mandatory reasoning", () => {
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const models = [
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createSpec({
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id: "glm-5.3",
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api: "anthropic-messages",
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provider: "zai",
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contextWindow: 200_000,
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maxTokens: 8192,
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}),
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createSpec({
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id: "glm-5.3",
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api: "openai-completions",
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provider: "zhipu-coding-plan",
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contextWindow: 200_000,
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maxTokens: 8192,
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}),
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];
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applyGeneratedModelPolicies(models);
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// Context pinning — same 1M tier as glm-5.2 on both GLM coding-plan hosts.
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for (const model of models) {
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expect(model.contextWindow).toBe(1_000_000);
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expect(model.maxTokens).toBe(131_072);
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// Uniform wire-exact low/high/max ladder (NOT the host-specific
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// high/max scale GLM-5.2 uses on zai/zhipu).
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expect(model.thinking?.efforts).toEqual([Effort.Low, Effort.High, Effort.Max]);
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// Thinking can no longer be disabled.
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expect(model.thinking?.requiresEffort).toBe(true);
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// Default effort is `max` per the GLM-5.3 API spec.
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expect(model.thinking?.defaultLevel).toBe(Effort.Max);
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}
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});
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it("pins MiniMax-M3 long-context providers to 1M context", () => {
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const models = [
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createSpec({
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id: "MiniMax-M3",
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api: "anthropic-messages",
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provider: "minimax",
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contextWindow: 512_000,
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maxTokens: 128_000,
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}),
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createSpec({
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id: "MiniMax-M3",
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api: "anthropic-messages",
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provider: "minimax-cn",
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contextWindow: 512_000,
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maxTokens: 128_000,
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}),
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createSpec({
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id: "MiniMax-M3",
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api: "openai-completions",
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provider: "minimax-code",
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contextWindow: 512_000,
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maxTokens: 128_000,
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}),
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createSpec({
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id: "MiniMax-M3",
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api: "openai-completions",
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provider: "minimax-code-cn",
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contextWindow: 512_000,
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maxTokens: 128_000,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.contextWindow).toBe(1_000_000);
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expect(models[0]?.maxTokens).toBe(128_000);
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expect(models[1]?.contextWindow).toBe(1_000_000);
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expect(models[1]?.maxTokens).toBe(128_000);
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expect(models[2]?.contextWindow).toBe(1_000_000);
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expect(models[2]?.maxTokens).toBe(128_000);
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expect(models[3]?.contextWindow).toBe(1_000_000);
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expect(models[3]?.maxTokens).toBe(128_000);
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});
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it("normalizes Copilot generated fallback limits", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "claude-opus-4.6",
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api: "anthropic-messages",
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provider: "github-copilot",
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contextWindow: 144000,
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maxTokens: 64000,
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}),
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createSpec({
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id: "gpt-5.4-mini",
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api: "openai-responses",
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provider: "github-copilot",
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contextWindow: 400000,
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maxTokens: 128000,
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}),
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createSpec({
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id: "grok-code-fast-1",
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api: "openai-completions",
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provider: "github-copilot",
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contextWindow: 128000,
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maxTokens: 64000,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.contextWindow).toBe(168000);
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expect(models[0]?.maxTokens).toBe(32000);
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expect(models[1]?.contextWindow).toBe(272000);
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expect(models[1]?.maxTokens).toBe(128000);
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expect(models[2]?.contextWindow).toBe(192000);
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expect(models[2]?.maxTokens).toBe(64000);
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});
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it("marks Ollama Cloud generated rows to omit max output tokens", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "deepseek-v4-flash",
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api: "ollama-chat",
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provider: "ollama-cloud",
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contextWindow: 1048576,
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maxTokens: 1048576,
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}),
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createSpec({
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id: "deepseek-v4-flash",
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api: "ollama-chat",
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provider: "ollama",
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contextWindow: 1048576,
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maxTokens: 1048576,
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.omitMaxOutputTokens).toBe(true);
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expect(models[1]?.omitMaxOutputTokens).toBeUndefined();
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});
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it("marks OpenCode Go MiMo models as not supporting tool_choice", () => {
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const models: ModelSpec<"openai-completions">[] = [
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createSpec({
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id: "mimo-v2.5-pro",
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api: "openai-completions",
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provider: "opencode-go",
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.compat?.supportsToolChoice).toBe(false);
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});
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it("sets OpenCode Go DeepSeek V4 tool-call request compat for both OpenAI APIs", () => {
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const models: ModelSpec<Api>[] = [
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createSpec({
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id: "deepseek-v4-flash",
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api: "openai-responses",
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provider: "opencode-go",
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}),
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createSpec({
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id: "deepseek-v4-pro",
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api: "openai-completions",
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provider: "opencode-go",
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}),
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];
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applyGeneratedModelPolicies(models);
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for (const model of models) {
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expect(model.compat).toMatchObject({
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supportsToolChoice: false,
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maxTokensField: "max_tokens",
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reasoningContentField: "reasoning_content",
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requiresReasoningContentForToolCalls: true,
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});
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}
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});
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it("marks OpenCode Go Kimi K2.7 Code as not supporting forced tool_choice", () => {
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const models: ModelSpec<"openai-completions">[] = [
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createSpec({
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id: "kimi-k2.7-code",
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api: "openai-completions",
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provider: "opencode-go",
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}),
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];
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applyGeneratedModelPolicies(models);
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expect(models[0]?.compat?.supportsForcedToolChoice).toBe(false);
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});
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it("links spark variants and gpt-5.5 to their context promotion targets", () => {
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const models = [
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createSpec({ id: "gpt-5.3-codex-spark", api: "openai-codex-responses", provider: "openai-codex" }),
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createSpec({ id: "gpt-5.5", api: "openai-codex-responses", provider: "openai-codex" }),
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createSpec({ id: "gpt-5.4", api: "openai-codex-responses", provider: "openai-codex" }),
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];
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linkOpenAIPromotionTargets(models);
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expect(models[0]?.contextPromotionTarget).toBe("openai-codex/gpt-5.5");
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expect(models[1]?.contextPromotionTarget).toBe("openai-codex/gpt-5.4");
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});
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it("links every gpt-5.5 flavor to its gpt-5.4 sibling across namespaced and dated provider ids", () => {
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const models = [
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// Namespaced provider ids (id carries an `openai/` prefix).
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createSpec({ id: "openai/gpt-5.5", api: "openai-responses", provider: "openrouter" }),
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createSpec({ id: "openai/gpt-5.5-pro", api: "openai-responses", provider: "openrouter" }),
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createSpec({ id: "openai/gpt-5.4", api: "openai-responses", provider: "openrouter" }),
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createSpec({ id: "openai/gpt-5.4-pro", api: "openai-responses", provider: "openrouter" }),
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createSpec({ id: "openai/gpt-5.4-mini", api: "openai-responses", provider: "openrouter" }),
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// Dated snapshot ids on a provider with no plain `gpt-5.4`.
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createSpec({ id: "gpt-5.5-2026-04-23", api: "openai-responses", provider: "aimlapi" }),
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createSpec({ id: "gpt-5.4-2026-03-05", api: "openai-responses", provider: "aimlapi" }),
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// Dotted namespace (amazon-bedrock `openai.gpt-5.x`).
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createSpec({ id: "openai.gpt-5.5", api: "openai-responses", provider: "amazon-bedrock" }),
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createSpec({ id: "openai.gpt-5.4", api: "openai-responses", provider: "amazon-bedrock" }),
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];
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linkOpenAIPromotionTargets(models);
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// Base and pro both promote to the plainest same-provider gpt-5.4 (base wins
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// over `-pro`/`-mini`), and the namespaced target round-trips through
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// parseModelString (first-slash split → provider `openrouter`, id `openai/gpt-5.4`).
|
|
expect(models[0]?.contextPromotionTarget).toBe("openrouter/openai/gpt-5.4");
|
|
expect(models[1]?.contextPromotionTarget).toBe("openrouter/openai/gpt-5.4");
|
|
// A gpt-5.4 model itself is never given a promotion target.
|
|
expect(models[2]?.contextPromotionTarget).toBeUndefined();
|
|
expect(models[3]?.contextPromotionTarget).toBeUndefined();
|
|
expect(models[4]?.contextPromotionTarget).toBeUndefined();
|
|
// Dated and dotted siblings resolve by parsed version, not literal id.
|
|
expect(models[5]?.contextPromotionTarget).toBe("aimlapi/gpt-5.4-2026-03-05");
|
|
expect(models[7]?.contextPromotionTarget).toBe("amazon-bedrock/openai.gpt-5.4");
|
|
});
|
|
|
|
it("sets freeform apply_patch metadata for first-party GPT-5 Responses models", () => {
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({ id: "gpt-5.4", api: "openai-responses", provider: "openai" }),
|
|
createSpec({ id: "gpt-5.3-codex-spark", api: "openai-codex-responses", provider: "openai-codex" }),
|
|
createSpec({
|
|
id: "gpt-5.3-codex-spark",
|
|
api: "openai-responses",
|
|
provider: "opencode",
|
|
applyPatchToolType: "freeform",
|
|
}),
|
|
createSpec({
|
|
id: "gpt-5.4",
|
|
api: "openai-completions",
|
|
provider: "litellm",
|
|
applyPatchToolType: "freeform",
|
|
}),
|
|
];
|
|
|
|
applyGeneratedModelPolicies(models);
|
|
|
|
expect(models[0]?.applyPatchToolType).toBe("freeform");
|
|
expect(models[1]?.applyPatchToolType).toBe("freeform");
|
|
expect(models[2]?.applyPatchToolType).toBeUndefined();
|
|
expect(models[3]?.applyPatchToolType).toBeUndefined();
|
|
});
|
|
|
|
it("strips paid xAI Responses effort dials for off-allowlist reasoners", () => {
|
|
const models: ModelSpec<"openai-responses">[] = [
|
|
createSpec({
|
|
id: "grok-code-fast-1",
|
|
api: "openai-responses",
|
|
provider: "xai",
|
|
thinking: { mode: "effort", efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High] },
|
|
}),
|
|
createSpec({
|
|
id: "grok-4.5",
|
|
api: "openai-responses",
|
|
provider: "xai",
|
|
thinking: { mode: "effort", efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High] },
|
|
}),
|
|
createSpec({
|
|
id: "grok-code-fast-1",
|
|
api: "openai-responses",
|
|
provider: "openrouter",
|
|
thinking: { mode: "effort", efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High] },
|
|
}),
|
|
];
|
|
|
|
applyGeneratedModelPolicies(models);
|
|
|
|
expect(models[0]?.thinking).toBeUndefined();
|
|
expect(models[0]?.compat).toMatchObject({
|
|
supportsReasoningEffort: false,
|
|
omitReasoningEffort: true,
|
|
});
|
|
expect(models[0]?.compat).not.toHaveProperty("reasoningEffortMap");
|
|
expect(models[1]?.thinking?.efforts).toEqual([Effort.Minimal, Effort.Low, Effort.Medium, Effort.High]);
|
|
expect(models[1]?.compat?.supportsReasoningEffort).toBe(true);
|
|
// Non-xAI hosts are outside this policy — no baked no-dial compat.
|
|
expect(models[2]?.thinking).toBeDefined();
|
|
expect(models[2]?.compat?.supportsReasoningEffort).toBeUndefined();
|
|
});
|
|
});
|
|
|
|
describe("applyOllamaCloudOutputCap", () => {
|
|
it("pins DeepSeek V4 Pro/Flash (and their tag variants) to the enforced ceiling (#7266)", () => {
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({
|
|
id: "deepseek-v4-flash",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 1048576,
|
|
maxTokens: 1048576,
|
|
}),
|
|
createSpec({
|
|
id: "deepseek-v4-flash:0731",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 1048576,
|
|
maxTokens: 8192,
|
|
}),
|
|
createSpec({
|
|
id: "deepseek-v4-pro",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 1048576,
|
|
maxTokens: 1048576,
|
|
}),
|
|
];
|
|
|
|
applyOllamaCloudOutputCap(models);
|
|
|
|
expect(models[0]?.maxTokens).toBe(65536);
|
|
expect(models[1]?.maxTokens).toBe(65536);
|
|
expect(models[2]?.maxTokens).toBe(65536);
|
|
});
|
|
|
|
it("leaves other Ollama Cloud models' discovered limits untouched", () => {
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({
|
|
id: "kimi-k2.5",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 262144,
|
|
maxTokens: 262144,
|
|
}),
|
|
createSpec({
|
|
id: "deepseek-v3.1:671b",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 163840,
|
|
maxTokens: 163840,
|
|
}),
|
|
];
|
|
|
|
applyOllamaCloudOutputCap(models);
|
|
|
|
expect(models[0]?.maxTokens).toBe(262144);
|
|
expect(models[1]?.maxTokens).toBe(163840);
|
|
});
|
|
|
|
it("caps by the context window when a capped model's window is below the ceiling", () => {
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({
|
|
id: "deepseek-v4-flash",
|
|
api: "ollama-chat",
|
|
provider: "ollama-cloud",
|
|
contextWindow: 32768,
|
|
maxTokens: 32768,
|
|
}),
|
|
];
|
|
|
|
applyOllamaCloudOutputCap(models);
|
|
|
|
expect(models[0]?.maxTokens).toBe(32768);
|
|
});
|
|
|
|
it("does not touch other providers", () => {
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({
|
|
id: "deepseek-v4-flash",
|
|
api: "openai-completions",
|
|
provider: "deepseek",
|
|
contextWindow: 1048576,
|
|
maxTokens: 1048576,
|
|
}),
|
|
];
|
|
|
|
applyOllamaCloudOutputCap(models);
|
|
|
|
expect(models[0]?.maxTokens).toBe(1048576);
|
|
});
|
|
});
|
|
|
|
describe("applyAntigravityPricingFallback", () => {
|
|
it("prices Gemini ids at Google API peers and Claude ids at Vertex, falling back to Anthropic", () => {
|
|
const googleCost = { input: 1.5, output: 9, cacheRead: 0.15, cacheWrite: 0 };
|
|
const previewCost = { input: 2, output: 12, cacheRead: 0.2, cacheWrite: 0 };
|
|
const vertexCost = { input: 6, output: 30, cacheRead: 0.6, cacheWrite: 7.5 };
|
|
const anthropicCost = { input: 5, output: 25, cacheRead: 0.5, cacheWrite: 6.25 };
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({ id: "gemini-3.5-flash", api: "google-generative-ai", provider: "google", cost: googleCost }),
|
|
createSpec({
|
|
id: "gemini-3.1-pro-preview",
|
|
api: "google-generative-ai",
|
|
provider: "google",
|
|
cost: previewCost,
|
|
}),
|
|
createSpec({
|
|
id: "claude-opus-4-6@default",
|
|
api: "anthropic-messages",
|
|
provider: "google-vertex",
|
|
cost: vertexCost,
|
|
}),
|
|
createSpec({ id: "claude-opus-4-6", api: "anthropic-messages", provider: "anthropic", cost: anthropicCost }),
|
|
createSpec({ id: "claude-sonnet-4-6", api: "anthropic-messages", provider: "anthropic", cost: anthropicCost }),
|
|
createSpec({ id: "gemini-3.5-flash", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
createSpec({ id: "gemini-3.1-pro", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
createSpec({ id: "claude-opus-4-6", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
createSpec({ id: "claude-sonnet-4-6", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
];
|
|
|
|
const result = applyAntigravityPricingFallback(models);
|
|
|
|
expect(result[5]?.cost).toEqual(googleCost);
|
|
expect(result[6]?.cost).toEqual(previewCost);
|
|
// Vertex list price wins over Anthropic for aliased Claude ids.
|
|
expect(result[7]?.cost).toEqual(vertexCost);
|
|
// Dangling Vertex alias (no google-vertex row) falls back to Anthropic.
|
|
expect(result[8]?.cost).toEqual(anthropicCost);
|
|
});
|
|
|
|
it("keeps zero cost for ids without a priced peer and never overwrites billable antigravity cost", () => {
|
|
const zeroCost = { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 };
|
|
const pricedCost = { input: 0.5, output: 3, cacheRead: 0.05, cacheWrite: 0 };
|
|
const models: ModelSpec<Api>[] = [
|
|
createSpec({ id: "gemini-3-flash-preview", api: "google-generative-ai", provider: "google", cost: zeroCost }),
|
|
createSpec({ id: "tab_flash_lite_preview", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
createSpec({ id: "gemini-3-flash", api: "google-gemini-cli", provider: "google-antigravity" }),
|
|
createSpec({
|
|
id: "gemini-3.6-flash",
|
|
api: "google-gemini-cli",
|
|
provider: "google-antigravity",
|
|
cost: pricedCost,
|
|
}),
|
|
createSpec({
|
|
id: "gemini-3.6-flash",
|
|
api: "google-generative-ai",
|
|
provider: "google",
|
|
cost: { input: 9, output: 9, cacheRead: 9, cacheWrite: 9 },
|
|
}),
|
|
];
|
|
|
|
const result = applyAntigravityPricingFallback(models);
|
|
|
|
// No billable google peer (zero-cost peer is not a pricing source).
|
|
expect(result[1]?.cost).toEqual(zeroCost);
|
|
expect(result[2]?.cost).toEqual(zeroCost);
|
|
// Already-billable antigravity rows keep their own pricing.
|
|
expect(result[3]?.cost).toEqual(pricedCost);
|
|
});
|
|
});
|