wehub-resource-sync
1633 lines
59 KiB
TypeScript
1633 lines
59 KiB
TypeScript
import { describe, expect, test } from "bun:test";
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import { type Api, Effort, type Model } from "@oh-my-pi/pi-ai";
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import { buildModel } from "@oh-my-pi/pi-catalog/build";
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import { DEFAULT_MODEL_PER_PROVIDER } from "@oh-my-pi/pi-catalog/provider-models";
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import {
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expandRoleAlias,
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extractExplicitThinkingSelector,
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filterAvailableModelsByEnabledPatterns,
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parseModelPattern,
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parseModelString,
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pickDefaultAvailableModel,
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resolveAgentModelPatterns,
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resolveAllowedModels,
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resolveCliModel,
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resolveModelFromString,
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resolveModelOverride,
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resolveModelRoleValue,
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resolveModelScope,
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} from "@oh-my-pi/pi-coding-agent/config/model-resolver";
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import { Settings } from "@oh-my-pi/pi-coding-agent/config/settings";
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// Mock models for testing
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const mockModels: Model<"anthropic-messages">[] = [
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buildModel({
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id: "claude-sonnet-4-5",
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name: "Claude Sonnet 4.5",
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api: "anthropic-messages",
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provider: "anthropic",
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baseUrl: "https://api.anthropic.com",
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reasoning: true,
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thinking: {
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mode: "budget",
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efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High],
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},
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input: ["text", "image"],
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cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },
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contextWindow: 200000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "gpt-4o",
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name: "GPT-4o",
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api: "anthropic-messages", // Using same type for simplicity
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provider: "openai",
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baseUrl: "https://api.openai.com",
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reasoning: false,
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input: ["text", "image"],
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cost: { input: 5, output: 15, cacheRead: 0.5, cacheWrite: 5 },
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contextWindow: 128000,
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maxTokens: 4096,
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}),
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];
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// Mock OpenRouter models with colons in IDs
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const mockOpenRouterModels: Model<Api>[] = [
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buildModel({
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id: "qwen/qwen3-coder:exacto",
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name: "Qwen3 Coder Exacto",
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api: "anthropic-messages",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: true,
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thinking: {
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mode: "budget",
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efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High],
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},
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "openai/gpt-4o:extended",
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name: "GPT-4o Extended",
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api: "anthropic-messages",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: false,
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input: ["text", "image"],
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cost: { input: 5, output: 15, cacheRead: 0.5, cacheWrite: 5 },
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contextWindow: 128000,
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maxTokens: 4096,
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}),
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buildModel({
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id: "z-ai/glm-4.7",
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name: "GLM 4.7",
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api: "openai-completions",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: true,
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thinking: {
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mode: "budget",
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efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High],
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},
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "deepseek/deepseek-v4-pro",
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name: "DeepSeek V4 Pro",
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api: "openai-completions",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: true,
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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];
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const mockMaxSuffixModels: Model<Api>[] = [
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buildModel({
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id: "coding-router",
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name: "NanoGPT Coding Router",
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api: "openai-completions",
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provider: "nanogpt",
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baseUrl: "https://nano-gpt.com/api/v1",
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reasoning: true,
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "coding-router:max",
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name: "NanoGPT Coding Router Max",
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api: "openai-completions",
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provider: "nanogpt",
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baseUrl: "https://nano-gpt.com/api/v1",
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reasoning: false,
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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];
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const mockAutoSuffixModels: Model<Api>[] = [
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buildModel({
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id: "runtime:auto",
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name: "Runtime Auto",
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api: "openai-completions",
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provider: "example",
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baseUrl: "https://example.com/api",
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reasoning: false,
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input: ["text"],
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cost: { input: 1, output: 2, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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];
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const mockProviderOverlapModels: Model<"anthropic-messages">[] = [
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buildModel({
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id: "kimi-k2.5",
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name: "Kimi K2.5",
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api: "anthropic-messages",
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provider: "kimi-code",
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baseUrl: "https://api.kimi.ai",
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reasoning: false,
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input: ["text"],
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cost: { input: 2, output: 6, cacheRead: 0.2, cacheWrite: 2 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "moonshotai/kimi-k2.5",
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name: "Kimi K2.5 (OpenRouter)",
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api: "anthropic-messages",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: false,
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input: ["text"],
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cost: { input: 2.2, output: 6.2, cacheRead: 0.22, cacheWrite: 2.2 },
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contextWindow: 128000,
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maxTokens: 8192,
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}),
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];
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const mockCodexOverlapModels: Model<"anthropic-messages">[] = [
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buildModel({
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id: "gpt-5.3-codex",
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name: "GPT-5.3 Codex",
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api: "anthropic-messages",
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provider: "openai-codex",
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baseUrl: "https://api.openai.com",
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reasoning: true,
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text"],
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cost: { input: 1.5, output: 6, cacheRead: 0.15, cacheWrite: 1.5 },
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contextWindow: 200000,
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maxTokens: 8192,
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}),
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buildModel({
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id: "gpt-5.3-codex-spark",
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name: "GPT-5.3 Codex Spark",
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api: "anthropic-messages",
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provider: "openai-codex",
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baseUrl: "https://api.openai.com",
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reasoning: true,
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text"],
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cost: { input: 1, output: 4, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 200000,
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maxTokens: 8192,
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}),
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];
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const mockMaxCapableModels: Model<"anthropic-messages">[] = [
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buildModel({
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id: "claude-opus-4-7",
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name: "Claude Opus 4.7",
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api: "anthropic-messages",
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provider: "anthropic",
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baseUrl: "https://api.anthropic.com",
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reasoning: true,
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thinking: {
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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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},
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input: ["text", "image"],
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cost: { input: 15, output: 75, cacheRead: 1.5, cacheWrite: 18.75 },
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contextWindow: 200000,
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maxTokens: 32000,
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}),
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];
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const openaiGpt55Models: Model<Api>[] = [
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buildModel({
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id: "gpt-5.5",
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name: "GPT-5.5",
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api: "openai-responses",
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provider: "openai",
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baseUrl: "https://api.openai.com",
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reasoning: true,
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text"],
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cost: { input: 1, output: 4, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 400000,
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maxTokens: 128000,
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}),
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buildModel({
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id: "gpt-5.5",
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name: "GPT-5.5 Codex",
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api: "openai-codex-responses",
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provider: "openai-codex",
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baseUrl: "https://chatgpt.com/backend-api/codex/responses",
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reasoning: true,
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thinking: {
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mode: "effort",
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efforts: [Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text"],
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cost: { input: 1, output: 4, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 400000,
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maxTokens: 128000,
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}),
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];
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function createBedrockDefaultModel(): Model<"bedrock-converse-stream"> {
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return buildModel({
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id: "us.anthropic.claude-opus-4-8",
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name: "Claude Opus 4.8 (US)",
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api: "bedrock-converse-stream",
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provider: "amazon-bedrock",
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baseUrl: "https://bedrock-runtime.us-east-1.amazonaws.com",
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reasoning: true,
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input: ["text", "image"],
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cost: { input: 5, output: 25, cacheRead: 0.5, cacheWrite: 6.25 },
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contextWindow: 1000000,
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maxTokens: 128000,
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});
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}
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function createOpusModel(provider: string, id: string, name: string): Model<"anthropic-messages"> {
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return buildModel({
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id,
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name,
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api: "anthropic-messages",
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provider,
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baseUrl: provider === "anthropic" ? "https://api.anthropic.com" : "https://api.githubcopilot.com",
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reasoning: true,
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thinking: {
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mode: "budget",
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efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High, Effort.XHigh],
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},
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input: ["text", "image"],
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cost: { input: 15, output: 75, cacheRead: 1.5, cacheWrite: 18.75 },
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contextWindow: 200000,
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maxTokens: 32000,
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});
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}
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const allModels = [...mockModels, ...mockOpenRouterModels, ...mockProviderOverlapModels, ...mockCodexOverlapModels];
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describe("pickDefaultAvailableModel", () => {
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test("prefers Codex OAuth over plain OpenAI for the shared GPT default", () => {
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const result = pickDefaultAvailableModel(openaiGpt55Models);
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expect(result?.provider).toBe("openai-codex");
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expect(result?.id).toBe("gpt-5.5");
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});
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test("keeps earlier unrelated provider defaults ahead of shared Codex defaults", () => {
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const anthropicDefault = buildModel({
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id: DEFAULT_MODEL_PER_PROVIDER.anthropic,
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name: "Anthropic Default",
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api: "anthropic-messages",
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provider: "anthropic",
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baseUrl: "https://api.anthropic.com",
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reasoning: true,
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thinking: {
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mode: "budget",
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efforts: [Effort.Low, Effort.Medium, Effort.High],
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},
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input: ["text"],
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cost: { input: 1, output: 4, cacheRead: 0.1, cacheWrite: 1 },
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contextWindow: 200000,
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maxTokens: 8192,
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});
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const result = pickDefaultAvailableModel([anthropicDefault, ...openaiGpt55Models]);
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expect(result?.provider).toBe("anthropic");
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expect(result?.id).toBe(DEFAULT_MODEL_PER_PROVIDER.anthropic);
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});
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test("uses the Zhipu Coding Plan login-validated model before newer z.ai defaults", () => {
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const zhipuGlm51 = buildModel({
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id: "glm-5.1",
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name: "GLM-5.1",
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api: "openai-completions",
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provider: "zhipu-coding-plan",
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baseUrl: "https://open.bigmodel.cn/api/coding/paas/v4",
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reasoning: true,
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input: ["text"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 200000,
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maxTokens: 131072,
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});
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const zhipuGlm52 = buildModel({
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id: "glm-5.2",
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name: "GLM-5.2",
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api: "openai-completions",
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provider: "zhipu-coding-plan",
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baseUrl: "https://open.bigmodel.cn/api/coding/paas/v4",
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reasoning: true,
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input: ["text"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 1000000,
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maxTokens: 131072,
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});
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const zaiGlm52 = buildModel({
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id: "glm-5.2",
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name: "GLM-5.2",
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api: "anthropic-messages",
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provider: "zai",
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baseUrl: "https://api.z.ai/api/anthropic",
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reasoning: true,
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input: ["text"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 1000000,
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maxTokens: 131072,
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});
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const result = pickDefaultAvailableModel([zhipuGlm51, zhipuGlm52, zaiGlm52]);
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expect(result?.provider).toBe("zhipu-coding-plan");
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expect(result?.id).toBe("glm-5.1");
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});
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});
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describe("parseModelPattern", () => {
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describe("simple patterns without colons", () => {
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test("exact match returns model with undefined thinking level", () => {
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const result = parseModelPattern("claude-sonnet-4-5", allModels);
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expect(result.model?.id).toBe("claude-sonnet-4-5");
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
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expect(result.warning).toBeUndefined();
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});
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test("partial match returns best model with undefined thinking level", () => {
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const result = parseModelPattern("sonnet", allModels);
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expect(result.model?.id).toBe("claude-sonnet-4-5");
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
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expect(result.warning).toBeUndefined();
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});
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test("no match returns undefined model and thinking level", () => {
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const result = parseModelPattern("nonexistent", allModels);
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expect(result.model).toBeUndefined();
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
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expect(result.warning).toBeUndefined();
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});
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});
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describe("patterns with valid thinking levels", () => {
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test("sonnet:high returns sonnet with high thinking level", () => {
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const result = parseModelPattern("sonnet:high", allModels);
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expect(result.model?.id).toBe("claude-sonnet-4-5");
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expect(result.thinkingLevel).toBe(Effort.High);
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expect(result.warning).toBeUndefined();
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});
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test("gpt-4o:medium returns gpt-4o with medium thinking level", () => {
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const result = parseModelPattern("gpt-4o:medium", allModels);
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expect(result.model?.id).toBe("gpt-4o");
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expect(result.thinkingLevel).toBe(Effort.Medium);
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expect(result.warning).toBeUndefined();
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});
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test("all valid thinking levels work", () => {
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const levels = [
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"off",
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Effort.Minimal,
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Effort.Low,
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Effort.Medium,
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Effort.High,
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Effort.XHigh,
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Effort.Max,
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] as const;
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for (const level of levels) {
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const result = parseModelPattern(`sonnet:${level}`, allModels);
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expect(result.model?.id).toBe("claude-sonnet-4-5");
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expect(result.thinkingLevel).toBe(level);
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expect(result.warning).toBeUndefined();
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}
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});
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test("max parses as a real thinking level after the literal pattern misses", () => {
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const result = parseModelPattern("gpt-5.3-codex:max", allModels);
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expect(result.model?.id).toBe("gpt-5.3-codex");
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expect(result.thinkingLevel).toBe(Effort.Max);
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expect(result.explicitThinkingLevel).toBe(true);
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expect(result.warning).toBeUndefined();
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});
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test("literal model ids ending in max win over the thinking suffix", () => {
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const result = parseModelPattern("nanogpt/coding-router:max", mockMaxSuffixModels);
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expect(result.model?.id).toBe("coding-router:max");
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
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expect(result.warning).toBeUndefined();
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});
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test("literal model ids ending in auto win over the auto sentinel alias", () => {
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const result = parseModelPattern("example/runtime:auto", mockAutoSuffixModels);
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expect(result.model?.id).toBe("runtime:auto");
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
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expect(result.warning).toBeUndefined();
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});
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});
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describe("patterns with invalid thinking levels", () => {
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test("sonnet:random returns sonnet with undefined thinking level and warning", () => {
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const result = parseModelPattern("sonnet:random", allModels);
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expect(result.model?.id).toBe("claude-sonnet-4-5");
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expect(result.thinkingLevel).toBeUndefined();
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expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toContain("Invalid thinking level");
|
|
expect(result.warning).toContain("random");
|
|
});
|
|
|
|
test("gpt-4o:invalid returns gpt-4o with undefined thinking level and warning", () => {
|
|
const result = parseModelPattern("gpt-4o:invalid", allModels);
|
|
expect(result.model?.id).toBe("gpt-4o");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toContain("Invalid thinking level");
|
|
});
|
|
});
|
|
|
|
describe("OpenRouter models with colons in IDs", () => {
|
|
test("qwen3-coder:exacto matches the model with undefined thinking level", () => {
|
|
const result = parseModelPattern("qwen/qwen3-coder:exacto", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("openrouter/qwen/qwen3-coder:exacto matches with provider prefix", () => {
|
|
const result = parseModelPattern("openrouter/qwen/qwen3-coder:exacto", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("qwen3-coder:exacto:high matches model with high thinking level", () => {
|
|
const result = parseModelPattern("qwen/qwen3-coder:exacto:high", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("openrouter/qwen/qwen3-coder:exacto:high matches with provider and thinking level", () => {
|
|
const result = parseModelPattern("openrouter/qwen/qwen3-coder:exacto:high", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("gpt-4o:extended matches the extended model with undefined thinking level", () => {
|
|
const result = parseModelPattern("openai/gpt-4o:extended", allModels);
|
|
expect(result.model?.id).toBe("openai/gpt-4o:extended");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("supports OpenRouter route suffixes that are not present in the catalog", () => {
|
|
const result = parseModelPattern("openrouter/z-ai/glm-4.7-20251222:nitro", allModels);
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7-20251222:nitro");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("supports OpenRouter route suffixes with an appended thinking level", () => {
|
|
const result = parseModelPattern("openrouter/z-ai/glm-4.7-20251222:nitro:high", allModels);
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7-20251222:nitro");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("openrouter/<id>:max applies max through the exact-selector path, not an OpenRouter route", () => {
|
|
// `max` is a thinking-level suffix, never an OpenRouter route suffix: the request
|
|
// must resolve the base model and carry max, not clone a literal `z-ai/glm-4.7:max`.
|
|
const result = parseModelPattern("openrouter/z-ai/glm-4.7:max", allModels);
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7");
|
|
expect(result.thinkingLevel).toBe(Effort.Max);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
});
|
|
|
|
describe("invalid thinking levels with OpenRouter models", () => {
|
|
test("qwen3-coder:exacto:random returns model with undefined thinking level and warning", () => {
|
|
const result = parseModelPattern("qwen/qwen3-coder:exacto:random", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toContain("Invalid thinking level");
|
|
expect(result.warning).toContain("random");
|
|
});
|
|
|
|
test("qwen3-coder:exacto:high:random returns model with undefined thinking level and warning", () => {
|
|
const result = parseModelPattern("qwen/qwen3-coder:exacto:high:random", allModels);
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toContain("Invalid thinking level");
|
|
expect(result.warning).toContain("random");
|
|
});
|
|
});
|
|
|
|
describe("edge cases", () => {
|
|
test("empty pattern matches via partial matching", () => {
|
|
// Empty string is included in all model IDs, so partial matching finds a match
|
|
const result = parseModelPattern("", allModels);
|
|
expect(result.model).not.toBeNull();
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
});
|
|
|
|
test("pattern ending with colon treats empty suffix as invalid", () => {
|
|
const result = parseModelPattern("sonnet:", allModels);
|
|
// Empty string after colon is not a valid thinking level
|
|
// So it tries to match "sonnet:" which won't match, then tries "sonnet"
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.warning).toContain("Invalid thinking level");
|
|
});
|
|
});
|
|
|
|
describe("preference logic", () => {
|
|
test("prefers most recently used model when multiple providers match", () => {
|
|
const result = parseModelPattern("k2.5", allModels, {
|
|
usageOrder: ["kimi-code/kimi-k2.5"],
|
|
});
|
|
expect(result.model?.provider).toBe("kimi-code");
|
|
});
|
|
|
|
test("prefers first-party providers over OpenRouter when no usage data exists", () => {
|
|
const result = parseModelPattern("k2.5", allModels, { usageOrder: [] });
|
|
expect(result.model?.provider).toBe("kimi-code");
|
|
});
|
|
|
|
test("respects most recently used provider even if openrouter", () => {
|
|
const result = parseModelPattern("k2.5", allModels, {
|
|
usageOrder: ["openrouter/moonshotai/kimi-k2.5"],
|
|
});
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("moonshotai/kimi-k2.5");
|
|
});
|
|
});
|
|
});
|
|
|
|
describe("resolveModelRoleValue", () => {
|
|
test("resolves pi/<role>:<thinking> by expanding role alias before parsing thinking", () => {
|
|
const settings = {
|
|
getModelRole: (role: string) => (role === "smol" ? "openrouter/qwen/qwen3-coder:exacto" : undefined),
|
|
} as NonNullable<Parameters<typeof resolveModelRoleValue>[2]>["settings"];
|
|
|
|
const result = resolveModelRoleValue("pi/smol:high", allModels, { settings });
|
|
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("resolves pi/<role>:max by expanding role alias before parsing thinking", () => {
|
|
const settings = {
|
|
getModelRole: (role: string) => (role === "smol" ? "openai-codex/gpt-5.3-codex" : undefined),
|
|
} as NonNullable<Parameters<typeof resolveModelRoleValue>[2]>["settings"];
|
|
|
|
const result = resolveModelRoleValue("pi/smol:max", allModels, { settings });
|
|
|
|
expect(result.model?.provider).toBe("openai-codex");
|
|
expect(result.model?.id).toBe("gpt-5.3-codex");
|
|
// Role-value resolution clamps: gpt-5.3-codex's ladder tops out at xhigh.
|
|
expect(result.thinkingLevel).toBe(Effort.XHigh);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("resolves pi/default through configured default role alias", () => {
|
|
const settings = {
|
|
getModelRole: (role: string) => (role === "default" ? "openrouter/qwen/qwen3-coder:exacto" : undefined),
|
|
} as NonNullable<Parameters<typeof resolveModelRoleValue>[2]>["settings"];
|
|
|
|
const result = resolveModelRoleValue("pi/default", allModels, { settings });
|
|
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
expect(result.explicitThinkingLevel).toBe(false);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("splits direct comma fallback chains before parsing thinking selectors", () => {
|
|
const result = resolveModelRoleValue("anthropic/claude-sonnet-4-5:off,openai/gpt-4o:off", allModels);
|
|
|
|
expect(result.model?.provider).toBe("anthropic");
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.thinkingLevel).toBe("off");
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("tries later direct comma fallback entries when earlier entries miss", () => {
|
|
const result = resolveModelRoleValue("anthropic/missing:off,openai/gpt-4o:off", allModels);
|
|
|
|
expect(result.model?.provider).toBe("openai");
|
|
expect(result.model?.id).toBe("gpt-4o");
|
|
expect(result.thinkingLevel).toBe("off");
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("does not resolve exact codex role values to codex-spark via substring matching", () => {
|
|
const providerQualified = resolveModelRoleValue("openai-codex/gpt-5.3-codex:xhigh", allModels);
|
|
expect(providerQualified.model?.provider).toBe("openai-codex");
|
|
expect(providerQualified.model?.id).toBe("gpt-5.3-codex");
|
|
expect(providerQualified.thinkingLevel).toBe(Effort.XHigh);
|
|
expect(providerQualified.explicitThinkingLevel).toBe(true);
|
|
|
|
const idOnly = resolveModelRoleValue("gpt-5.3-codex:xhigh", allModels);
|
|
expect(idOnly.model?.provider).toBe("openai-codex");
|
|
expect(idOnly.model?.id).toBe("gpt-5.3-codex");
|
|
expect(idOnly.thinkingLevel).toBe(Effort.XHigh);
|
|
expect(idOnly.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("clamps explicit thinking selectors from model metadata", () => {
|
|
const result = resolveModelRoleValue("anthropic/claude-sonnet-4-5:xhigh", allModels);
|
|
|
|
expect(result.model?.provider).toBe("anthropic");
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("passes max through unclamped when the model ladder includes it", () => {
|
|
const result = resolveModelRoleValue("anthropic/claude-opus-4-7:max", mockMaxCapableModels);
|
|
|
|
expect(result.model?.provider).toBe("anthropic");
|
|
expect(result.model?.id).toBe("claude-opus-4-7");
|
|
expect(result.thinkingLevel).toBe(Effort.Max);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("preserves an explicit :auto suffix as an explicit thinking selector", () => {
|
|
const result = resolveModelRoleValue("anthropic/claude-sonnet-4-5:auto", allModels);
|
|
|
|
expect(result.model?.provider).toBe("anthropic");
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.thinkingLevel).toBe("auto");
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
expect(result.warning).toBeUndefined();
|
|
});
|
|
|
|
test("does not clamp :auto against the model's supported efforts", () => {
|
|
// claude-sonnet-4-5 caps at "high"; ensure auto isn't collapsed onto it
|
|
// by resolveThinkingLevelForModel.
|
|
const result = resolveModelRoleValue("anthropic/claude-sonnet-4-5:auto", allModels);
|
|
|
|
expect(result.thinkingLevel).toBe("auto");
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
});
|
|
describe("resolveAgentModelPatterns", () => {
|
|
test("falls back to the active session model when pi/task is unset", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: { default: "anthropic/claude-sonnet-4-5" },
|
|
});
|
|
|
|
const result = resolveAgentModelPatterns({
|
|
agentModel: "pi/task",
|
|
settings,
|
|
activeModelPattern: "openai/gpt-4o",
|
|
});
|
|
|
|
expect(result).toEqual(["openai/gpt-4o"]);
|
|
});
|
|
|
|
test("uses the configured task role before falling back to the session model", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: {
|
|
default: "openai/gpt-4o",
|
|
task: "anthropic/claude-sonnet-4-5:high",
|
|
},
|
|
});
|
|
|
|
const result = resolveAgentModelPatterns({
|
|
agentModel: "pi/task",
|
|
settings,
|
|
activeModelPattern: "openai/gpt-4o",
|
|
});
|
|
|
|
expect(result).toEqual(["anthropic/claude-sonnet-4-5:high"]);
|
|
});
|
|
|
|
test("accepts YAML list values for configured task role patterns", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: {
|
|
task: ["anthropic/claude-sonnet-4-6", "zai/glm-5.2:high"],
|
|
},
|
|
});
|
|
|
|
const result = resolveAgentModelPatterns({
|
|
agentModel: "pi/task",
|
|
settings,
|
|
});
|
|
|
|
expect(result).toEqual(["anthropic/claude-sonnet-4-6", "zai/glm-5.2:high"]);
|
|
});
|
|
|
|
test("uses default for unconfigured smol, slow, and designer agent roles before priority defaults", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: { default: "local/llama" },
|
|
});
|
|
|
|
expect(resolveAgentModelPatterns({ agentModel: "pi/smol", settings })).toEqual(["local/llama"]);
|
|
expect(resolveAgentModelPatterns({ agentModel: "pi/slow", settings })).toEqual(["local/llama"]);
|
|
expect(resolveAgentModelPatterns({ agentModel: "pi/designer", settings })).toEqual(["local/llama"]);
|
|
});
|
|
|
|
test("expands cross-role default aliases when inheriting for an unset role", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: { default: "pi/slow", slow: "anthropic/claude-sonnet-4-5" },
|
|
});
|
|
|
|
expect(resolveAgentModelPatterns({ agentModel: "pi/smol", settings })).toEqual(["anthropic/claude-sonnet-4-5"]);
|
|
});
|
|
|
|
test("prefers configured designer role override over priority defaults", () => {
|
|
const settings = Settings.isolated({
|
|
modelRoles: {
|
|
default: "anthropic/claude-sonnet-4-5",
|
|
designer: "openai/gpt-4o",
|
|
},
|
|
});
|
|
|
|
const result = resolveAgentModelPatterns({
|
|
agentModel: "pi/designer",
|
|
settings,
|
|
});
|
|
|
|
expect(result).toEqual(["openai/gpt-4o"]);
|
|
});
|
|
|
|
test("slow priority falls forward to Opus 4.8 before older Opus aliases", () => {
|
|
const settings = Settings.isolated();
|
|
const patterns = resolveAgentModelPatterns({ agentModel: "pi/slow", settings });
|
|
|
|
const dottedRegistry = {
|
|
getAvailable: () => [
|
|
createOpusModel("github-copilot", "claude-opus-4.7", "Claude Opus 4.7"),
|
|
createOpusModel("github-copilot", "claude-opus-4.8", "Claude Opus 4.8"),
|
|
],
|
|
} as Parameters<typeof resolveModelOverride>[1];
|
|
const dotted = resolveModelOverride(patterns, dottedRegistry, settings);
|
|
expect(dotted.model?.provider).toBe("github-copilot");
|
|
expect(dotted.model?.id).toBe("claude-opus-4.8");
|
|
|
|
const dashedRegistry = {
|
|
getAvailable: () => [
|
|
createOpusModel("anthropic", "claude-opus-4-7", "Claude Opus 4.7"),
|
|
createOpusModel("anthropic", "claude-opus-4-8", "Claude Opus 4.8"),
|
|
],
|
|
} as Parameters<typeof resolveModelOverride>[1];
|
|
const dashed = resolveModelOverride(patterns, dashedRegistry, settings);
|
|
expect(dashed.model?.provider).toBe("anthropic");
|
|
expect(dashed.model?.id).toBe("claude-opus-4-8");
|
|
});
|
|
});
|
|
|
|
describe("resolveModelFromString", () => {
|
|
test("falls back to pattern parsing for provider/model:thinking when strict provider+id miss", () => {
|
|
const resolved = resolveModelFromString("openrouter/qwen/qwen3-coder:exacto:high", allModels);
|
|
expect(resolved?.provider).toBe("openrouter");
|
|
expect(resolved?.id).toBe("qwen/qwen3-coder:exacto");
|
|
});
|
|
|
|
test("treats colon-containing model IDs without thinking suffix as exact IDs", () => {
|
|
const resolved = resolveModelFromString("openrouter/qwen/qwen3-coder:exacto", allModels);
|
|
expect(resolved?.provider).toBe("openrouter");
|
|
expect(resolved?.id).toBe("qwen/qwen3-coder:exacto");
|
|
});
|
|
});
|
|
|
|
describe("resolveModelOverride", () => {
|
|
test("preserves explicit off and explicit-thinking metadata", () => {
|
|
const registry = {
|
|
getAvailable: () => allModels,
|
|
} as Parameters<typeof resolveModelOverride>[1];
|
|
|
|
const result = resolveModelOverride(["sonnet:off"], registry);
|
|
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.thinkingLevel).toBe("off");
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("resolves colon-containing model IDs with appended thinking suffix", () => {
|
|
const registry = {
|
|
getAvailable: () => allModels,
|
|
} as Parameters<typeof resolveModelOverride>[1];
|
|
|
|
const result = resolveModelOverride(["openrouter/qwen/qwen3-coder:exacto:high"], registry);
|
|
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.explicitThinkingLevel).toBe(true);
|
|
});
|
|
});
|
|
describe("resolveCliModel", () => {
|
|
test("resolves --model provider/id without --provider", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "openai/gpt-4o",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openai");
|
|
expect(result.model?.id).toBe("gpt-4o");
|
|
});
|
|
|
|
test("resolves fuzzy patterns within an explicit provider", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliProvider: "openai",
|
|
cliModel: "4o",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openai");
|
|
expect(result.model?.id).toBe("gpt-4o");
|
|
});
|
|
|
|
test("supports --model <pattern>:<thinking> (without explicit --thinking)", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "sonnet:high",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.id).toBe("claude-sonnet-4-5");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
});
|
|
|
|
test("prefers exact model id match over provider inference (OpenRouter-style ids)", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "openai/gpt-4o:extended",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("openai/gpt-4o:extended");
|
|
});
|
|
|
|
test("does not strip invalid :suffix as thinking level in --model (fail fast)", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliProvider: "openai",
|
|
cliModel: "gpt-4o:extended",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.model).toBeUndefined();
|
|
expect(result.error).toContain("not found");
|
|
});
|
|
|
|
test("supports provider-prefixed OpenRouter route suffixes even when the base model is cataloged without them", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "openrouter/z-ai/glm-4.7-20251222:nitro",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7-20251222:nitro");
|
|
});
|
|
|
|
test("supports explicit OpenRouter provider with route suffixes that are not in the catalog", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliProvider: "openrouter",
|
|
cliModel: "z-ai/glm-4.7-20251222:nitro",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7-20251222:nitro");
|
|
});
|
|
|
|
test("accepts Bedrock inference profile ARNs and preserves thinking suffixes", () => {
|
|
const defaultBedrockModel = createBedrockDefaultModel();
|
|
const profileArn = "arn:aws:bedrock:us-east-2:1234567890:application-inference-profile/company-opus-48";
|
|
|
|
const baseResult = resolveCliModel({
|
|
cliProvider: "amazon-bedrock",
|
|
cliModel: profileArn,
|
|
modelRegistry: {
|
|
getAll: () => [defaultBedrockModel],
|
|
},
|
|
});
|
|
const offResult = resolveCliModel({
|
|
cliProvider: "amazon-bedrock",
|
|
cliModel: `${profileArn}:off`,
|
|
modelRegistry: {
|
|
getAll: () => [defaultBedrockModel],
|
|
},
|
|
});
|
|
|
|
expect(baseResult.error).toBeUndefined();
|
|
expect(baseResult.model?.provider).toBe("amazon-bedrock");
|
|
expect(baseResult.model?.api).toBe("bedrock-converse-stream");
|
|
expect(baseResult.model?.id).toBe(profileArn);
|
|
expect(baseResult.model?.name).toBe("Bedrock inference profile");
|
|
expect(baseResult.model?.reasoning).toBe(false);
|
|
expect(baseResult.model?.thinking).toBeUndefined();
|
|
expect(baseResult.model?.contextWindow).toBeNull();
|
|
expect(baseResult.model?.maxTokens).toBeNull();
|
|
expect(baseResult.thinkingLevel).toBeUndefined();
|
|
expect(offResult.error).toBeUndefined();
|
|
expect(offResult.model?.id).toBe(profileArn);
|
|
expect(offResult.thinkingLevel).toBe("off");
|
|
});
|
|
|
|
test("returns a clear error when there are no models", () => {
|
|
const registry = {
|
|
getAll: () => [],
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliProvider: "openai",
|
|
cliModel: "gpt-4o",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.model).toBeUndefined();
|
|
expect(result.error).toContain("No models available");
|
|
});
|
|
|
|
test("resolves provider-prefixed fuzzy patterns (openrouter/qwen -> openrouter model)", () => {
|
|
const registry = {
|
|
getAll: () => allModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "openrouter/qwen",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
|
|
});
|
|
|
|
test("prefers decomposed provider+id over flat id match when ambiguous", () => {
|
|
// Simulates the zai/glm-5 bug: vercel-ai-gateway has id="zai/glm-5",
|
|
// zai has id="glm-5". Input "zai/glm-5" should resolve to provider=zai.
|
|
const ambiguousModels: Model<"anthropic-messages">[] = [
|
|
buildModel({
|
|
id: "zai/glm-5",
|
|
name: "GLM-5 (Vercel)",
|
|
api: "anthropic-messages",
|
|
provider: "vercel-ai-gateway",
|
|
baseUrl: "https://vercel.ai",
|
|
reasoning: false,
|
|
input: ["text"],
|
|
cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
|
|
contextWindow: 128000,
|
|
maxTokens: 4096,
|
|
}),
|
|
buildModel({
|
|
id: "glm-5",
|
|
name: "GLM-5",
|
|
api: "anthropic-messages",
|
|
provider: "zai",
|
|
baseUrl: "https://api.z.ai",
|
|
reasoning: false,
|
|
input: ["text"],
|
|
cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
|
|
contextWindow: 128000,
|
|
maxTokens: 4096,
|
|
}),
|
|
];
|
|
const registry = {
|
|
getAll: () => ambiguousModels,
|
|
} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
|
|
const result = resolveCliModel({
|
|
cliModel: "zai/glm-5",
|
|
modelRegistry: registry,
|
|
});
|
|
|
|
expect(result.error).toBeUndefined();
|
|
expect(result.model?.provider).toBe("zai");
|
|
expect(result.model?.id).toBe("glm-5");
|
|
});
|
|
});
|
|
|
|
describe("resolveModelScope", () => {
|
|
test("does not coalesce explicit provider/id patterns to Codex (regression for enabledModels)", async () => {
|
|
const scoped = await resolveModelScope(["openai/gpt-5.5"], {
|
|
getAvailable: () => openaiGpt55Models,
|
|
});
|
|
expect(scoped).toHaveLength(1);
|
|
expect(scoped[0].model.provider).toBe("openai");
|
|
expect(scoped[0].model.id).toBe("gpt-5.5");
|
|
});
|
|
|
|
test("applies max thinking selectors to glob scopes when no literal max ids match", async () => {
|
|
const registry = {
|
|
getAvailable: () => mockCodexOverlapModels,
|
|
};
|
|
|
|
const scoped = await resolveModelScope(["openai-codex/*:max"], registry);
|
|
|
|
expect(scoped).toHaveLength(2);
|
|
// Scoped levels clamp per model: max on an xhigh-ceiling ladder resolves to xhigh.
|
|
expect(scoped.map(entry => entry.thinkingLevel)).toEqual([Effort.XHigh, Effort.XHigh]);
|
|
expect(scoped.every(entry => entry.explicitThinkingLevel)).toBe(true);
|
|
});
|
|
|
|
test("keeps max on glob scopes when the model ladder includes it", async () => {
|
|
const registry = {
|
|
getAvailable: () => mockMaxCapableModels,
|
|
};
|
|
|
|
const scoped = await resolveModelScope(["anthropic/*:max"], registry);
|
|
|
|
expect(scoped).toHaveLength(1);
|
|
expect(scoped[0].thinkingLevel).toBe(Effort.Max);
|
|
expect(scoped[0].explicitThinkingLevel).toBe(true);
|
|
});
|
|
|
|
test("preserves literal :max in scoped-model globs", async () => {
|
|
const registry = {
|
|
getAvailable: () => mockMaxSuffixModels,
|
|
};
|
|
|
|
const scoped = await resolveModelScope(["nanogpt/*:max"], registry);
|
|
|
|
expect(scoped).toHaveLength(1);
|
|
expect(scoped[0].model.id).toBe("coding-router:max");
|
|
expect(scoped[0].thinkingLevel).toBeUndefined();
|
|
expect(scoped[0].explicitThinkingLevel).toBe(false);
|
|
});
|
|
});
|
|
|
|
describe("parseModelString", () => {
|
|
test("parses standard provider/id format", () => {
|
|
const result = parseModelString("anthropic/claude-sonnet-4-5");
|
|
expect(result).toEqual({ provider: "anthropic", id: "claude-sonnet-4-5" });
|
|
});
|
|
|
|
test("returns undefined for strings without a slash", () => {
|
|
expect(parseModelString("claude-sonnet-4-5")).toBeUndefined();
|
|
expect(parseModelString("")).toBeUndefined();
|
|
expect(parseModelString("sonnet:high")).toBeUndefined();
|
|
});
|
|
|
|
test("returns undefined for strings starting with slash", () => {
|
|
expect(parseModelString("/claude-sonnet-4-5")).toBeUndefined();
|
|
});
|
|
|
|
describe("thinking level suffix extraction", () => {
|
|
test("extracts valid thinking level from provider/id:level", () => {
|
|
const result = parseModelString("anthropic/claude-sonnet-4-5:high");
|
|
expect(result).toEqual({ provider: "anthropic", id: "claude-sonnet-4-5", thinkingLevel: Effort.High });
|
|
});
|
|
|
|
test("extracts all valid thinking levels", () => {
|
|
const levels = ["off", Effort.Minimal, Effort.Low, Effort.Medium, Effort.High, Effort.XHigh] as const;
|
|
for (const level of levels) {
|
|
const result = parseModelString(`anthropic/claude-sonnet-4-5:${level}`);
|
|
expect(result?.id).toBe("claude-sonnet-4-5");
|
|
expect(result?.thinkingLevel).toBe(level);
|
|
}
|
|
});
|
|
|
|
test("does NOT strip invalid suffix — treats it as part of model ID", () => {
|
|
const result = parseModelString("openrouter/qwen/qwen3-coder:exacto");
|
|
expect(result).toEqual({ provider: "openrouter", id: "qwen/qwen3-coder:exacto" });
|
|
});
|
|
|
|
test("handles model ID with colon followed by valid thinking level", () => {
|
|
// e.g. "openrouter/qwen/qwen3-coder:exacto:high" — last colon is thinking level
|
|
const result = parseModelString("openrouter/qwen/qwen3-coder:exacto:high");
|
|
expect(result).toEqual({
|
|
provider: "openrouter",
|
|
id: "qwen/qwen3-coder:exacto",
|
|
thinkingLevel: Effort.High,
|
|
});
|
|
});
|
|
|
|
test("extracts max when explicitly enabled for provider id selectors", () => {
|
|
const result = parseModelString("deepseek/deepseek-v4-pro:max", { allowMaxSuffix: true });
|
|
expect(result).toEqual({ provider: "deepseek", id: "deepseek-v4-pro", thinkingLevel: Effort.Max });
|
|
});
|
|
|
|
test("preserves literal max model ids when the caller can prove they exist", () => {
|
|
const result = parseModelString("nanogpt/coding-router:max", {
|
|
allowMaxSuffix: true,
|
|
isLiteralModelId: (provider, id) => provider === "nanogpt" && id === "coding-router:max",
|
|
});
|
|
expect(result).toEqual({ provider: "nanogpt", id: "coding-router:max" });
|
|
});
|
|
|
|
test("leaves :max attached to the model id unless the caller opts in via allowMaxSuffix", () => {
|
|
// Without allowMaxSuffix, the strict suffix parser must not silently
|
|
// reinterpret a literal `:max` id as a thinking suffix.
|
|
const result = parseModelString("anthropic/claude-sonnet-4-5:max");
|
|
expect(result).toEqual({ provider: "anthropic", id: "claude-sonnet-4-5:max" });
|
|
});
|
|
|
|
test("leaves :auto attached to the model id unless the caller opts in via allowAutoAlias", () => {
|
|
// Without allowAutoAlias, the strict suffix parser must not silently
|
|
// reinterpret a literal `:auto` id as an auto-thinking selector.
|
|
const result = parseModelString("example/runtime:auto");
|
|
expect(result).toEqual({ provider: "example", id: "runtime:auto" });
|
|
});
|
|
|
|
test("extracts auto sentinel when explicitly enabled for provider id selectors", () => {
|
|
const result = parseModelString("openai/gpt-5:auto", { allowAutoAlias: true });
|
|
expect(result).toEqual({ provider: "openai", id: "gpt-5", thinkingLevel: "auto" });
|
|
});
|
|
|
|
test("preserves literal :auto model ids when the caller can prove they exist", () => {
|
|
const result = parseModelString("example/runtime:auto", {
|
|
allowAutoAlias: true,
|
|
isLiteralModelId: (provider, id) => provider === "example" && id === "runtime:auto",
|
|
});
|
|
expect(result).toEqual({ provider: "example", id: "runtime:auto" });
|
|
});
|
|
|
|
test("does not strip inherited object keys as thinking suffixes", () => {
|
|
const result = parseModelString("anthropic/claude-sonnet-4-5:constructor");
|
|
expect(result).toEqual({ provider: "anthropic", id: "claude-sonnet-4-5:constructor" });
|
|
});
|
|
test("does not extract thinking level from model ID with invalid suffix", () => {
|
|
const result = parseModelString("openrouter/openai/gpt-4o:extended");
|
|
// :extended is not a valid thinking level, so it stays as part of the ID
|
|
expect(result).toEqual({ provider: "openrouter", id: "openai/gpt-4o:extended" });
|
|
});
|
|
|
|
test("handles empty suffix after colon", () => {
|
|
const result = parseModelString("anthropic/claude-sonnet-4-5:");
|
|
// Empty string is not a valid thinking level, so colon stays as part of ID
|
|
expect(result).toEqual({ provider: "anthropic", id: "claude-sonnet-4-5:" });
|
|
});
|
|
});
|
|
});
|
|
|
|
describe("resolveModelFromString", () => {
|
|
test("applies max as a provider model selector alias after literal lookup misses", () => {
|
|
const result = resolveModelFromString("nanogpt/coding-router:max", [mockMaxSuffixModels[0]]);
|
|
expect(result?.provider).toBe("nanogpt");
|
|
expect(result?.id).toBe("coding-router");
|
|
});
|
|
|
|
test("preserves literal max provider model ids before alias parsing", () => {
|
|
const result = resolveModelFromString("nanogpt/coding-router:max", mockMaxSuffixModels);
|
|
expect(result?.provider).toBe("nanogpt");
|
|
expect(result?.id).toBe("coding-router:max");
|
|
});
|
|
|
|
test("preserves literal :auto provider model ids before alias parsing", () => {
|
|
const result = resolveModelFromString("example/runtime:auto", mockAutoSuffixModels);
|
|
expect(result?.provider).toBe("example");
|
|
expect(result?.id).toBe("runtime:auto");
|
|
});
|
|
});
|
|
|
|
describe("expandRoleAlias", () => {
|
|
test("expands pi/vision to configured vision role", () => {
|
|
const settings = Settings.isolated();
|
|
settings.setModelRole("vision", "openai/gpt-4o");
|
|
|
|
expect(expandRoleAlias("pi/vision", settings)).toBe("openai/gpt-4o");
|
|
});
|
|
|
|
test("keeps pi/vision alias when vision role is unset", () => {
|
|
const settings = Settings.isolated();
|
|
settings.setModelRole("default", "anthropic/claude-sonnet-4-5");
|
|
|
|
expect(expandRoleAlias("pi/vision", settings)).toBe("pi/vision");
|
|
});
|
|
});
|
|
|
|
describe("extractExplicitThinkingSelector", () => {
|
|
test("does not carry max from literal role model ids", () => {
|
|
const result = extractExplicitThinkingSelector("nanogpt/coding-router:max", undefined, {
|
|
isLiteralModelId: (provider, id) => provider === "nanogpt" && id === "coding-router:max",
|
|
});
|
|
expect(result).toBeUndefined();
|
|
});
|
|
|
|
test("treats max as an explicit selector when the model id is not literal", () => {
|
|
const result = extractExplicitThinkingSelector("nanogpt/coding-router:max", undefined, {
|
|
isLiteralModelId: () => false,
|
|
});
|
|
expect(result).toBe(Effort.Max);
|
|
});
|
|
|
|
test("treats max on pi role aliases as an explicit selector before expansion", () => {
|
|
const settings = Settings.isolated();
|
|
settings.setModelRole("smol", "nanogpt/coding-router:max");
|
|
const result = extractExplicitThinkingSelector("pi/smol:max", settings, {
|
|
isLiteralModelId: (provider, id) => provider === "nanogpt" && id === "coding-router:max",
|
|
});
|
|
expect(result).toBe(Effort.Max);
|
|
});
|
|
|
|
test("does not carry auto from literal role model ids", () => {
|
|
const result = extractExplicitThinkingSelector("nanogpt/coding-router:auto", undefined, {
|
|
isLiteralModelId: (provider, id) => provider === "nanogpt" && id === "coding-router:auto",
|
|
});
|
|
expect(result).toBeUndefined();
|
|
});
|
|
|
|
test("treats auto as an explicit selector when the model id is not literal", () => {
|
|
const result = extractExplicitThinkingSelector("openai/gpt-5:auto", undefined, {
|
|
isLiteralModelId: () => false,
|
|
});
|
|
expect(result).toBe("auto");
|
|
});
|
|
});
|
|
|
|
describe("provider routing selector (@upstream)", () => {
|
|
const openRouterOnly = (model: Model<Api> | undefined): string[] | undefined =>
|
|
(model?.compat as { openRouterRouting?: { only?: string[] } } | undefined)?.openRouterRouting?.only;
|
|
|
|
test("pins an OpenRouter model to one upstream via @slug", () => {
|
|
const result = parseModelPattern("openrouter/z-ai/glm-4.7@cerebras", allModels);
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7");
|
|
expect(result.model?.provider).toBe("openrouter");
|
|
expect(result.upstream).toBe("cerebras");
|
|
expect(openRouterOnly(result.model)).toEqual(["cerebras"]);
|
|
});
|
|
|
|
test("resolves @slug without an explicit provider prefix", () => {
|
|
const result = parseModelPattern("z-ai/glm-4.7@cerebras", allModels);
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7");
|
|
expect(openRouterOnly(result.model)).toEqual(["cerebras"]);
|
|
});
|
|
|
|
test("combines @slug with a trailing thinking level", () => {
|
|
const result = parseModelPattern("openrouter/z-ai/glm-4.7@cerebras:high", allModels);
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(openRouterOnly(result.model)).toEqual(["cerebras"]);
|
|
});
|
|
|
|
test("preserves @upstream when the slug also matches model tokens", () => {
|
|
const result = parseModelPattern("openrouter/deepseek/deepseek-v4-pro@deepseek:high", allModels);
|
|
expect(result.model?.id).toBe("deepseek/deepseek-v4-pro");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
expect(result.upstream).toBe("deepseek");
|
|
expect(openRouterOnly(result.model)).toEqual(["deepseek"]);
|
|
});
|
|
|
|
test("routes Vercel AI Gateway models via vercelGatewayRouting", () => {
|
|
const gatewayModel: Model<"openai-completions"> = buildModel({
|
|
id: "zai/glm-4.7",
|
|
name: "GLM 4.7 (Gateway)",
|
|
api: "openai-completions",
|
|
provider: "vercel-ai-gateway",
|
|
baseUrl: "https://ai-gateway.vercel.sh/v1",
|
|
reasoning: true,
|
|
input: ["text"],
|
|
cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
|
|
contextWindow: 128000,
|
|
maxTokens: 8192,
|
|
});
|
|
const result = parseModelPattern("vercel-ai-gateway/zai/glm-4.7@cerebras", [gatewayModel]);
|
|
expect(result.model?.id).toBe("zai/glm-4.7");
|
|
expect(
|
|
(result.model?.compat as { vercelGatewayRouting?: { only?: string[] } } | undefined)?.vercelGatewayRouting
|
|
?.only,
|
|
).toEqual(["cerebras"]);
|
|
expect(openRouterOnly(result.model)).toBeUndefined();
|
|
});
|
|
|
|
test("does not split a model id that legitimately ends in @ (Vertex)", () => {
|
|
const vertexModel: Model<"anthropic-messages"> = buildModel({
|
|
id: "claude-opus-4-8@default",
|
|
name: "Claude Opus 4.8",
|
|
api: "anthropic-messages",
|
|
provider: "google-vertex",
|
|
baseUrl: "https://us-aiplatform.googleapis.com",
|
|
reasoning: true,
|
|
input: ["text"],
|
|
cost: { input: 15, output: 75, cacheRead: 1.5, cacheWrite: 18.75 },
|
|
contextWindow: 200000,
|
|
maxTokens: 32000,
|
|
});
|
|
const result = parseModelPattern("claude-opus-4-8@default", [vertexModel]);
|
|
expect(result.model?.id).toBe("claude-opus-4-8@default");
|
|
expect(result.upstream).toBeUndefined();
|
|
expect(openRouterOnly(result.model)).toBeUndefined();
|
|
});
|
|
|
|
test("keeps fuzzy matching a non-aggregator provider id that ends in @ (Vertex)", () => {
|
|
const vertexModel: Model<"anthropic-messages"> = buildModel({
|
|
id: "claude-opus-4-8@default",
|
|
name: "Claude Opus 4.8",
|
|
api: "anthropic-messages",
|
|
provider: "google-vertex",
|
|
baseUrl: "https://us-aiplatform.googleapis.com",
|
|
reasoning: true,
|
|
input: ["text"],
|
|
cost: { input: 15, output: 75, cacheRead: 1.5, cacheWrite: 18.75 },
|
|
contextWindow: 200000,
|
|
maxTokens: 32000,
|
|
});
|
|
// `opus@default` is a fuzzy provider-qualified pattern: the `@upstream` bypass must not
|
|
// swallow it, because google-vertex is not an aggregator and the routing fallback would
|
|
// never resolve it, leaving the selector unmatched.
|
|
const result = parseModelPattern("google-vertex/opus@default", [vertexModel]);
|
|
expect(result.model?.id).toBe("claude-opus-4-8@default");
|
|
expect(result.upstream).toBeUndefined();
|
|
expect(openRouterOnly(result.model)).toBeUndefined();
|
|
});
|
|
|
|
test("ignores @slug on a non-aggregator model (no silent routing)", () => {
|
|
const result = parseModelPattern("gpt-4o@cerebras", allModels);
|
|
expect(result.model).toBeUndefined();
|
|
});
|
|
|
|
test("resolveCliModel round-trips @upstream in the selector and carries compat", () => {
|
|
const registry = { getAll: () => allModels } as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];
|
|
const result = resolveCliModel({ cliModel: "openrouter/z-ai/glm-4.7@cerebras", modelRegistry: registry });
|
|
expect(result.model?.id).toBe("z-ai/glm-4.7");
|
|
expect(result.selector).toBe("openrouter/z-ai/glm-4.7@cerebras");
|
|
expect(openRouterOnly(result.model)).toEqual(["cerebras"]);
|
|
});
|
|
});
|
|
|
|
describe("filterAvailableModelsByEnabledPatterns", () => {
|
|
const models = mockModels as Model[];
|
|
test("returns all models when patterns is empty", () => {
|
|
expect(filterAvailableModelsByEnabledPatterns(models, [])).toEqual(models);
|
|
});
|
|
|
|
test("filters by exact provider/modelId", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["anthropic/claude-sonnet-4-5"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("claude-sonnet-4-5");
|
|
});
|
|
|
|
test("filters by bare model id matching across providers", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["claude-sonnet-4-5"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].provider).toBe("anthropic");
|
|
});
|
|
|
|
test("strips :thinkingLevel suffix before matching", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["anthropic/claude-sonnet-4-5:high"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("claude-sonnet-4-5");
|
|
});
|
|
|
|
test("preserves colon-bearing OpenRouter ids (suffix is not a thinking level)", () => {
|
|
const openRouterModels = mockOpenRouterModels as Model[];
|
|
const result = filterAvailableModelsByEnabledPatterns(openRouterModels, ["openrouter/qwen/qwen3-coder:exacto"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("qwen/qwen3-coder:exacto");
|
|
});
|
|
|
|
test("matches bare OpenRouter-style model id with slash but no provider prefix", () => {
|
|
const openRouterModels = mockOpenRouterModels as Model[];
|
|
const result = filterAvailableModelsByEnabledPatterns(openRouterModels, ["qwen/qwen3-coder:exacto"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("qwen/qwen3-coder:exacto");
|
|
expect(result[0].provider).toBe("openrouter");
|
|
});
|
|
|
|
test("evaluates glob patterns against provider/modelId", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["anthropic/*"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].provider).toBe("anthropic");
|
|
});
|
|
test("preserves literal :max in enabledModels globs", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(mockMaxSuffixModels, ["nanogpt/*:max"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("coding-router:max");
|
|
});
|
|
|
|
test("evaluates glob patterns against bare model id", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["claude-*"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].id).toBe("claude-sonnet-4-5");
|
|
});
|
|
|
|
test("applies glob and exact patterns together", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["anthropic/*", "openai/gpt-4o"]);
|
|
expect(result).toHaveLength(2);
|
|
});
|
|
|
|
test("returns empty list when no pattern matches (misconfiguration)", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["nonexistent-model"]);
|
|
expect(result).toHaveLength(0);
|
|
});
|
|
|
|
test("includes multiple patterns from different providers", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(models, ["anthropic/claude-sonnet-4-5", "openai/gpt-4o"]);
|
|
expect(result).toHaveLength(2);
|
|
});
|
|
|
|
test("keeps synthetic Bedrock inference profile matches", () => {
|
|
const bedrockModels = [createBedrockDefaultModel()];
|
|
const profileArn = "arn:aws:bedrock:us-east-2:1234567890:application-inference-profile/company-opus-48";
|
|
|
|
const result = filterAvailableModelsByEnabledPatterns(bedrockModels, [`amazon-bedrock/${profileArn}`]);
|
|
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].provider).toBe("amazon-bedrock");
|
|
expect(result[0].id).toBe(profileArn);
|
|
expect(result[0].reasoning).toBe(false);
|
|
});
|
|
|
|
test("resolveAllowedModels keeps synthetic Bedrock inference profile matches", async () => {
|
|
const bedrockModels = [createBedrockDefaultModel()];
|
|
const profileArn = "arn:aws:bedrock:us-east-2:1234567890:application-inference-profile/company-opus-48";
|
|
const settings = Settings.isolated({ enabledModels: [profileArn] });
|
|
|
|
const result = await resolveAllowedModels(
|
|
{
|
|
getAvailable: () => bedrockModels,
|
|
},
|
|
settings,
|
|
);
|
|
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].provider).toBe("amazon-bedrock");
|
|
expect(result[0].id).toBe(profileArn);
|
|
expect(result[0].reasoning).toBe(false);
|
|
});
|
|
test("does not coalesce explicit provider/id patterns to Codex (regression for enabledModels)", () => {
|
|
const result = filterAvailableModelsByEnabledPatterns(openaiGpt55Models, ["openai/gpt-5.5"]);
|
|
expect(result).toHaveLength(1);
|
|
expect(result[0].provider).toBe("openai");
|
|
expect(result[0].id).toBe("gpt-5.5");
|
|
});
|
|
});
|
|
|
|
describe("effort-tier variant aliases", () => {
|
|
const variantModels: Model<Api>[] = [
|
|
buildModel({
|
|
id: "gemini-3.5-flash",
|
|
requestModelId: "gemini-3.5-flash-extra-low",
|
|
name: "Gemini 3.5 Flash",
|
|
api: "google-gemini-cli",
|
|
provider: "google-antigravity",
|
|
baseUrl: "https://daily-cloudcode-pa.googleapis.com",
|
|
reasoning: true,
|
|
thinking: {
|
|
mode: "google-level",
|
|
efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High],
|
|
effortRouting: {
|
|
off: "gemini-3.5-flash-extra-low",
|
|
[Effort.Minimal]: "gemini-3-flash-agent",
|
|
[Effort.Low]: "gemini-3.5-flash-extra-low",
|
|
[Effort.Medium]: "gemini-3.5-flash-extra-low",
|
|
[Effort.High]: "gemini-3.5-flash-low",
|
|
},
|
|
suppressWhenOff: true,
|
|
},
|
|
input: ["text", "image"],
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
contextWindow: 1_048_576,
|
|
maxTokens: 65_535,
|
|
}),
|
|
// Live legacy model whose id is also a recycled alias of the family —
|
|
// exact matches must keep winning while it exists.
|
|
buildModel({
|
|
id: "gemini-3-flash",
|
|
name: "Gemini 3 Flash",
|
|
api: "google-gemini-cli",
|
|
provider: "google-antigravity",
|
|
baseUrl: "https://daily-cloudcode-pa.googleapis.com",
|
|
reasoning: true,
|
|
thinking: { mode: "google-level", efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High] },
|
|
input: ["text", "image"],
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
contextWindow: 1_048_576,
|
|
maxTokens: 65_535,
|
|
}),
|
|
// Auto-derived pair target on a provider without a hand table.
|
|
buildModel({
|
|
id: "kimi-k2",
|
|
name: "Kimi K2",
|
|
api: "openai-completions",
|
|
provider: "venice",
|
|
baseUrl: "https://api.venice.ai/api/v1",
|
|
reasoning: true,
|
|
thinking: { mode: "budget", efforts: [Effort.Minimal, Effort.Low, Effort.Medium, Effort.High] },
|
|
input: ["text"],
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
contextWindow: 128_000,
|
|
maxTokens: 8_192,
|
|
}),
|
|
];
|
|
|
|
test("provider-qualified retired tier ids resolve to the collapsed model", () => {
|
|
const result = parseModelPattern("google-antigravity/gemini-3.5-flash-low", variantModels);
|
|
expect(result.model?.id).toBe("gemini-3.5-flash");
|
|
expect(result.thinkingLevel).toBeUndefined();
|
|
});
|
|
|
|
test("retired tier ids keep explicit :level suffixes", () => {
|
|
const result = parseModelPattern("google-antigravity/gemini-3.5-flash-low:high", variantModels);
|
|
expect(result.model?.id).toBe("gemini-3.5-flash");
|
|
expect(result.thinkingLevel).toBe(Effort.High);
|
|
});
|
|
|
|
test("bare retired tier ids resolve through the alias table", () => {
|
|
const result = parseModelPattern("gemini-3.5-flash-extra-low", variantModels);
|
|
expect(result.model?.id).toBe("gemini-3.5-flash");
|
|
expect(result.model?.provider).toBe("google-antigravity");
|
|
});
|
|
|
|
test("live models always beat recycled aliases", () => {
|
|
const result = parseModelPattern("google-antigravity/gemini-3-flash", variantModels);
|
|
expect(result.model?.id).toBe("gemini-3-flash");
|
|
});
|
|
|
|
test("consumed X-thinking twins resolve via the grammar fallback", () => {
|
|
expect(parseModelPattern("venice/kimi-k2-thinking", variantModels).model?.id).toBe("kimi-k2");
|
|
expect(parseModelPattern("kimi-k2-thinking", variantModels).model?.id).toBe("kimi-k2");
|
|
});
|
|
});
|