mirror of
https://github.com/farion1231/cc-switch.git
synced 2026-10-25 17:22:27 +08:00
Jason
50a9d103d3
Move first-party, sponsor and relay presets to current model IDs so a pasted Key works without editing the model list. - Claude: opus/sonnet 5 -> 5.5, haiku 4.5 -> 5.5, fable 5 -> 5.1 - GPT: gpt-5.6-sol / gpt-6-sol -> gpt-6.1-sol; add gpt-6-astra/luna - Gemini 3.6 Flash -> 3.8 Flash, 3.1 Pro preview -> 4 Argon; Grok 4.5 -> 4.7; GLM 5.1/5.2 -> 5.3 (+5.3 Flash); DeepSeek V4 Flash -> V4.1 Flash; Doubao 2.1 Pro 260628 -> 260915; StepFun Step Plan -> step-5-preview; add Kimi K3.1 Pro, Qwen3.8 Max, MiniMax M3.1 Flash where the site serves them - Drop retired models: NVIDIA moonshotai/kimi-k3 (EOL 2026-10-15, now kimi-k2.6 without unverified Codex reasoning levels), MiMo V2.5 / V2.5 Pro, Claude Sonnet 4.5, MiniMax M2.7 on Tencent Token Plan Enterprise Pro - Relay presets follow each site's own ID spelling and catalog; DIT.ai, BuyToken and hao.ai are left as configured from their docs; Soshow, SiliconFlow CN, OpenCode Zen and the Ark rolling alias stay - MoArk Codex keeps the tested Kimi-K2.7-Code; K3.1 Pro and Qwen3.8 Max stay out of its Codex catalog until their levels are verified Tests updated to the new IDs; cross-app invariants unchanged.
405 lines
13 KiB
TypeScript
405 lines
13 KiB
TypeScript
import { describe, expect, it } from "vitest";
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import { codexProviderPresets } from "@/config/codexProviderPresets";
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import {
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extractCodexBaseUrl,
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extractCodexModelName,
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extractCodexWireApi,
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} from "@/utils/providerConfigUtils";
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const expectedChatPresets = new Map<
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string,
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{ baseUrl: string; contextWindows: Record<string, number> }
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>([
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// 火山 Agent Plan / Coding Plan 与 BytePlus 国际站（coding/v3）、智谱 GLM、
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// Kimi 两条（开放平台 + Kimi Code）均已切原生 Responses，见下方 native 清单
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[
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"Baidu Qianfan Coding Plan",
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{
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baseUrl: "https://qianfan.baidubce.com/v2/coding",
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contextWindows: { "qianfan-code-latest": 131072 },
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},
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],
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[
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"Baidu Qianfan Token Plan",
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{
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baseUrl: "https://qianfan.baidubce.com/v2/tokenplan/personal",
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contextWindows: {
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"deepseek-v4-pro": 1048576,
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"deepseek-v4-flash": 1048576,
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"deepseek-v4-flash-0731": 1048576,
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"glm-5.2": 1048576,
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"glm-5.1": 198000,
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"kimi-k2.6": 262144,
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},
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},
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],
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[
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"StepFun",
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{
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baseUrl: "https://api.stepfun.com/step_plan/v1",
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contextWindows: {
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"step-3.7-flash": 262144,
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"step-3.5-flash-2603": 262144,
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"step-3.5-flash": 262144,
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},
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},
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],
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[
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"StepFun en",
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{
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baseUrl: "https://api.stepfun.ai/step_plan/v1",
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contextWindows: {
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"step-3.7-flash": 262144,
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"step-3.5-flash-2603": 262144,
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"step-3.5-flash": 262144,
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},
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},
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],
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[
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"ModelScope",
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{
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baseUrl: "https://api-inference.modelscope.cn/v1",
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contextWindows: { "ZhipuAI/GLM-5.2": 200000 },
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},
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],
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[
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"BaiLing",
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{
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baseUrl: "https://api.ant-ling.com/v1",
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contextWindows: { "Ling-2.6-1T": 262144 },
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},
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],
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[
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"SiliconFlow",
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{
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baseUrl: "https://api.siliconflow.cn/v1",
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contextWindows: { "deepseek-ai/DeepSeek-V4-Flash": 1048576 },
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},
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],
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[
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"SiliconFlow en",
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{
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baseUrl: "https://api.siliconflow.com/v1",
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contextWindows: { "MiniMaxAI/MiniMax-M3": 1048576 },
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},
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],
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[
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"AtlasCloud",
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{
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baseUrl: "https://api.atlascloud.ai/v1",
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contextWindows: { "zai-org/glm-5.3": 1048576 },
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},
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],
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[
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"Novita AI",
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{
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baseUrl: "https://api.novita.ai/openai/v1",
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contextWindows: { "zai-org/glm-5.3": 1048576 },
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},
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],
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[
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"Nvidia",
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{
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baseUrl: "https://integrate.api.nvidia.com/v1",
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contextWindows: { "moonshotai/kimi-k2.6": 262144 },
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},
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],
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[
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"OpenCode Go",
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{
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baseUrl: "https://opencode.ai/zen/go/v1",
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contextWindows: {
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"glm-5.3": 1000000,
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"glm-5.3-flash": 1000000,
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"kimi-k3.1-pro": 1048576,
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"deepseek-v4-pro": 1048576,
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"deepseek-v4.1-flash": 1048576,
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"mimo-v2.6-pro": 1048576,
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},
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},
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],
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]);
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describe("Codex Chat provider presets", () => {
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it.each([
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["StepFun API", "https://api.stepfun.com/v1", "step-3.7-flash"],
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["StepFun API en", "https://api.stepfun.ai/v1", "step-3.7-flash"],
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["Baidu Qianfan", "https://qianfan.baidubce.com/v2", "deepseek-v4-pro"],
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[
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"Astron Coding Plan",
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"https://maas-coding-api.cn-huabei-1.xf-yun.com/v1",
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"astron-code-latest",
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],
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])(
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"connects %s to its native Responses endpoint",
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(name, baseUrl, modelId) => {
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const preset = codexProviderPresets.find((item) => item.name === name);
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expect(preset, `${name} preset`).toBeDefined();
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expect(preset?.apiFormat).toBe("openai_responses");
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expect(extractCodexBaseUrl(preset?.config)).toBe(baseUrl);
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expect(extractCodexWireApi(preset?.config)).toBe("responses");
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expect(extractCodexModelName(preset?.config)).toBe(modelId);
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expect(preset?.endpointCandidates).toContain(baseUrl);
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expect(preset?.modelCatalog?.[0]?.model).toBe(modelId);
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expect(preset?.codexChatReasoning).toBeUndefined();
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expect(preset?.promptCacheRouting).toBeUndefined();
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},
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);
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it("drops prompt cache routing once Kimi Coding is direct-connect", () => {
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// promptCacheRouting 只被 Responses→Chat 转换层消费（forwarder 在转换后
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// 重注入 prompt_cache_key）。原生直连由 Codex 自己发 prompt_cache_key，
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// 留着这面旗只会让人误以为该卡仍需路由接管。
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const preset = codexProviderPresets.find(
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(item) => item.name === "Kimi For Coding",
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);
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expect(preset?.apiFormat).toBe("openai_responses");
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expect(preset?.promptCacheRouting).toBeUndefined();
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});
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it("keeps open-weight Qwen models scoped to pay-as-you-go catalogs", () => {
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for (const name of ["千问AI平台", "QwenCloud"]) {
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const preset = codexProviderPresets.find((item) => item.name === name);
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expect(preset, name).toBeDefined();
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expect(preset?.modelCatalog).toEqual(
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expect.arrayContaining([
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expect.objectContaining({
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model: "qwen3.8-2.4t-a95b",
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inputModalities: ["text"],
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}),
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expect.objectContaining({
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model: "qwen3.8-27b",
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inputModalities: ["text", "image"],
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}),
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]),
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);
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}
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for (const name of ["千问AI平台 Token Plan", "QwenCloud Token Plan"]) {
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const preset = codexProviderPresets.find((item) => item.name === name);
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expect(preset, name).toBeDefined();
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const models = preset?.modelCatalog?.map((row) => row.model) ?? [];
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expect(models).not.toContain("qwen3.8-2.4t-a95b");
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expect(models).not.toContain("qwen3.8-27b");
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}
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});
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it("marks migrated Chat Completions presets for local routing", () => {
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for (const [name, expected] of expectedChatPresets) {
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const preset = codexProviderPresets.find((item) => item.name === name);
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expect(preset, `${name} preset`).toBeDefined();
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expect(preset?.apiFormat).toBe("openai_chat");
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expect(extractCodexBaseUrl(preset?.config)).toBe(expected.baseUrl);
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expect(extractCodexWireApi(preset?.config)).toBe("responses");
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expect(preset?.endpointCandidates).toContain(expected.baseUrl);
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expect(preset?.modelCatalog?.length).toBeGreaterThan(0);
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expect(extractCodexModelName(preset?.config)).toBe(
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preset?.modelCatalog?.[0]?.model,
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);
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expect(
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Object.fromEntries(
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(preset?.modelCatalog ?? []).map((model) => [
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model.model,
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model.contextWindow,
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]),
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),
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).toEqual(expected.contextWindows);
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}
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});
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it("uses native Responses API for migrated CN providers without local route mapping", () => {
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const nativeResponsesPresets = new Map<
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string,
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{ baseUrl?: string; contextWindows: Record<string, number> }
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>([
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// 官方 Codex 文档确认 Agent Plan /api/plan/v3 与 Coding Plan
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// /api/coding/v3 均支持 Responses API；BytePlus 国际站 coding/v3
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// 同（docs.byteplus.com/en/docs/ModelArk/2556056，2026-08-15 核实）
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["火山 Agent Plan", { contextWindows: { "ark-code-latest": 256000 } }],
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["火山 Coding Plan", { contextWindows: { "ark-code-latest": 256000 } }],
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["BytePlus", { contextWindows: { "ark-code-latest": 256000 } }],
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[
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"Volcengine Doubao",
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{ contextWindows: { "doubao-seed-2-1-pro-260915": 262144 } },
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],
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[
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"千问AI平台",
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{
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contextWindows: {
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"qwen3.8-max": 983616,
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"qwen3.8-2.4t-a95b": 983616,
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"qwen3.8-27b": 983616,
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},
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},
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],
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// 腾讯 TokenHub 官方 Codex 文档确认 hy3 原生 Responses（2026-07-14）
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[
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"Tencent Hunyuan",
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{
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contextWindows: {
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hy3: 256000,
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"hy3-preview": 256000,
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"hy4-preview": 960000,
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},
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},
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],
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// DeepSeek 官方 Codex 文档的 V4.1 Flash 使用 deepseek-flash；
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// catalog 由后端按 deepseek.com host 镜像官方 models.json 生成
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[
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"DeepSeek",
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{
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contextWindows: {
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"deepseek-flash": 1048576,
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"deepseek-v4-pro": 1048576,
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},
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},
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],
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["Longcat", { contextWindows: { "LongCat-2.0": 1048576 } }],
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[
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"MiniMax",
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{
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baseUrl: "https://api.minimax.cn/v1",
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contextWindows: {
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"MiniMax-M3": 1000000,
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"MiniMax-M3.1-Flash": 1000000,
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},
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},
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],
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[
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"MiniMax en",
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{
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contextWindows: {
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"MiniMax-M3": 1000000,
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"MiniMax-M3.1-Flash": 1000000,
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},
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},
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],
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[
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"Xiaomi MiMo",
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{
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contextWindows: {
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"mimo-v2.6-pro": 1048576,
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"mimo-v2.6-flash": 1048576,
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"mimo-v2.6-pro-ultraspeed": 1048576,
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},
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},
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],
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[
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"Xiaomi MiMo Token Plan (China)",
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{
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contextWindows: {
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"mimo-v2.6-pro": 1048576,
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"mimo-v2.6-flash": 1048576,
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},
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},
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],
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// 智谱三端点分立（Anthropic /api/anthropic、Chat /api/coding/paas/v4、
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// Responses /api/v1），官方明示错误端点无法使用 Coding Plan 套餐额度——
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// 原生 Responses 预设必须锁在 /api/v1（#6944；docs.bigmodel.cn/cn/coding-plan/
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// tool/codex 与 docs.z.ai/devpack/tool/codex 自带 models.json，2026-09-04 核对：
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// 国内站 glm-5.3 + glm-5-turbo，国际站仅 glm-5.3）
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[
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"Zhipu GLM",
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{
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baseUrl: "https://open.bigmodel.cn/api/v1",
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contextWindows: {
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"glm-5.3": 1048576,
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"glm-5.3-flash": 1048576,
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"glm-5-turbo": 204800,
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},
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},
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],
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[
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"Zhipu GLM en",
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{
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baseUrl: "https://api.z.ai/api/v1",
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contextWindows: { "glm-5.3": 1048576, "glm-5.3-flash": 1048576 },
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},
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],
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// Kimi 两份官方 Codex 接入文档均要求 wire_api = "responses"，并明写服务
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// 端原生实现 Responses API、无需本地路由或协议转换（platform.kimi.com/
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// docs/guide/codex-kimi.md 与 kimi.com/code/docs/third-party-tools/
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// codex.html，2026-09-09 真 Key 探针复核）
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[
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"Kimi",
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{
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baseUrl: "https://api.moonshot.cn/v1",
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contextWindows: { "kimi-k3": 1048576, "kimi-k2.7-code": 262144 },
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},
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],
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[
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"Kimi For Coding",
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{
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baseUrl: "https://api.kimi.com/coding/v1",
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contextWindows: {
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"kimi-for-coding": 1048576,
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"kimi-for-coding-highspeed": 262144,
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k3: 1048576,
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"k3-256k": 262144,
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},
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},
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],
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]);
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for (const [name, expected] of nativeResponsesPresets) {
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const preset = codexProviderPresets.find((item) => item.name === name);
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expect(preset, `${name} preset`).toBeDefined();
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expect(preset?.apiFormat).toBe("openai_responses");
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if (expected.baseUrl) {
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// 直连预设的 base_url 必须是厂商的 Responses 端点本身（不是同站的
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// Chat 端点）；endpointCandidates 与主地址同路径档
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expect(extractCodexBaseUrl(preset?.config)).toBe(expected.baseUrl);
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expect(preset?.endpointCandidates).toContain(expected.baseUrl);
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expect(extractCodexModelName(preset?.config)).toBe(
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preset?.modelCatalog?.[0]?.model,
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);
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}
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// 原生 Responses 预设现在带 modelCatalog：cc-switch 直连时据此生成
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// ~/.codex 的 model-catalogs.json（shell_command 编辑、不发 freeform
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// apply_patch）。带 catalog 不再强制开“本地路由映射”——前端已按
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// apiFormat 解耦（openai_responses 默认不开接管）。
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expect((preset?.modelCatalog ?? []).length).toBeGreaterThan(0);
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expect(
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Object.fromEntries(
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(preset?.modelCatalog ?? []).map((model) => [
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model.model,
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model.contextWindow,
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]),
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),
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).toEqual(expected.contextWindows);
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// 原生（直连）不走 Chat 转换，因此不需要 codexChatReasoning。
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expect(preset?.codexChatReasoning).toBeUndefined();
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}
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});
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it("ships per-model reasoningLevels for OpenCode Go mirroring models.dev", () => {
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// Zen 网关的合法 effort 档位是逐模型的（models.dev reasoning_options，
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// 2026-09-10）：统一并集映射会把 high 发给仅声明 max 的 kimi-k3.1-pro（原 kimi-k3），
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// 此测试锁住逐模型表，防回退。
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const preset = codexProviderPresets.find(
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(item) => item.name === "OpenCode Go",
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);
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expect(preset, "OpenCode Go preset").toBeDefined();
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expect(preset?.codexChatReasoning?.effortValueMode).toBe("zen");
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expect(
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Object.fromEntries(
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(preset?.modelCatalog ?? []).map((model) => [
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model.model,
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model.reasoningLevels ?? null,
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]),
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),
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).toEqual({
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"glm-5.3": ["low", "high", "max"],
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"glm-5.3-flash": ["low", "high", "max"],
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"kimi-k3.1-pro": ["max"],
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"deepseek-v4-pro": ["high", "max"],
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"deepseek-v4.1-flash": ["low", "high", "max"],
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"mimo-v2.6-pro": null,
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});
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});
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});
|