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* chore(internal-testing): 退役内置 provider;bump presetDataVersion to 52 internal-testing 一直作为内测阶段的硬编码 provider 存在,现在客户端已切换到 登录后绑定 desirecore-cloud(NewAPI 网关)方案,internal-testing 不再需要。 从此 commit 起: - compute/providers/internal-testing.json 删除 - _index.json 移除 internal-testing 条目 - presetDataVersion 51 → 52,触发客户端重新合并预置 客户端需配套实现 retired-providers 清理逻辑:在 mergeBuiltinIfNewer 之后 执行一次性清理,移除本地 compute.json 中残留的 provider-internal-testing-001 + secrets.json#internal-testing。 * chore: bump preset data version to 61 * feat: 增量同步 16 个厂商旗舰模型 (首批精选) 首次从 OpenRouter 增量同步,覆盖 13 家厂商的最新旗舰: 概要 --- - 新增 .sync-watermark 水位线文件 (1782276303 / 2026-06-24) - presetDataVersion: 61 → 62 - 校验 70/70 全部通过 新增模型 --- OpenAI: gpt-image-2 (image_gen), gpt-5.5, gpt-5.4 Anthropic: claude-fable-5, claude-opus-4.8 Google: gemini-3.5-flash, gemini-3.1-flash-image (Nano Banana 2) DeepSeek: deepseek-v4-pro Qwen: qwen3.7-max, qwen3.7-plus MiniMax: MiniMax-M3 Moonshot: kimi-k2.7-code Zhipu: glm-5.2 xAI: grok-4.3 Mistral: mistral-medium-3.5 Tencent: hy3-preview Cohere: north-mini-code 后续机制 --- 每日 10:00 心跳检查 OpenRouter 新模型,对比 .sync-watermark 增量入库,有更新才提 PR,无更新静默。
114 lines
4.0 KiB
JSON
114 lines
4.0 KiB
JSON
{
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"description": "Google Gemini 系列模型规格。参数来源:config-center compute/providers/google.json。",
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"specs": [
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{
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"id": "gemini-3.5-flash",
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"displayName": "Gemini 3.5 Flash",
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"family": "gemini-3.5",
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"match": { "exact": ["gemini-3.5-flash"], "patterns": ["gemini-3.5-flash*"] },
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "video_understanding", "audio_understanding", "ultra_long_context", "tool_use", "fast"],
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"serviceType": ["chat"],
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"defaultTemperature": 1,
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"supportsReasoning": true,
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"description": "Google Gemini 3.5 Flash,全模态高性价比模型,100 万上下文"
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}
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},
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{
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"id": "gemini-3.1-flash-image",
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"displayName": "Gemini 3.1 Flash Image (Nano Banana 2)",
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"family": "gemini-3.1",
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"match": { "exact": ["gemini-3.1-flash-image"], "patterns": ["gemini-3.1-flash-image*"] },
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"spec": {
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"contextWindow": 131072,
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"maxOutputTokens": 32768,
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"capabilities": ["image_generation", "image_editing", "text_rendering"],
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"serviceType": ["image_gen"],
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"description": "Google Nano Banana 2,最新文生图与编辑模型"
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}
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},
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{
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"id": "gemini-3.1-pro-preview",
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"displayName": "Gemini 3.1 Pro",
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"family": "gemini-3.1",
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "ultra_long_context", "tool_use"],
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"serviceType": ["chat"],
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"defaultTemperature": 1,
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"supportsReasoning": true
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}
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},
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{
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"id": "gemini-3-flash-preview",
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"displayName": "Gemini 3 Flash",
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"family": "gemini-3",
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "ultra_long_context", "tool_use", "fast"],
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"serviceType": ["chat"],
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"defaultTemperature": 1,
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"supportsReasoning": true
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}
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},
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{
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"id": "gemini-2.5-pro",
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"displayName": "Gemini 2.5 Pro",
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"family": "gemini-2.5",
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"match": { "patterns": ["gemini-2.5-pro*"] },
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "ultra_long_context", "tool_use"],
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"serviceType": ["chat"],
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"defaultTemperature": 1,
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"supportsReasoning": true
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}
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},
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{
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"id": "gemini-2.5-flash",
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"displayName": "Gemini 2.5 Flash",
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"family": "gemini-2.5",
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"match": { "patterns": ["gemini-2.5-flash*"] },
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "ultra_long_context", "tool_use", "fast"],
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"serviceType": ["chat"],
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"defaultTemperature": 1,
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"supportsReasoning": true
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}
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},
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{
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"id": "gemini-3.1-flash-lite-preview",
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"displayName": "Gemini 3.1 Flash-Lite Preview",
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"family": "gemini-3.1",
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"match": { "exact": ["gemini-3.1-flash-lite-preview"], "patterns": ["gemini-3.1-flash-lite*"] },
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"spec": {
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"contextWindow": 1048576,
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"maxOutputTokens": 65536,
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"capabilities": ["chat", "reasoning", "code", "vision", "ultra_long_context", "tool_use", "fast"],
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"serviceType": ["fast"],
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"defaultTemperature": 1,
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"description": "Google Gemini 3.1 Flash-Lite Preview,低成本高吞吐多模态模型"
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}
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},
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{
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"id": "text-embedding-005",
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"displayName": "Text Embedding 005",
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"family": "text-embedding",
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"match": { "exact": ["text-embedding-005"] },
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"spec": {
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"contextWindow": 2048,
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"capabilities": ["text_embedding", "semantic_search"],
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"serviceType": ["embedding"],
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"description": "Google 文本嵌入模型,768 维度"
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}
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}
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]
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}
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