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research/llm-model-comparison/references/chinese-model-platforms.md

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+# Chinese AI Model Platforms Reference
+
+## Major Providers & Model Families
+
+| Provider | Platform | Model Family | Notes |
+|----------|----------|-------------|-------|
+| 商汤 SenseTime | cloud.sensenova.cn | SenseNova (6.7B, U1, etc.) | Named as `sensenova-*` in APIs |
+| 深度求索 DeepSeek | platform.deepseek.com | DeepSeek-V3/V4, R1, Coder | `deepseek-*` naming |
+| 阿里 Alibaba | dashscope.aliyun.com | Qwen (通义千问) | `qwen-*` naming |
+| 字节跳动 ByteDance | volcengine.com | Doubao (豆包) | `doubao-*` naming |
+| 月之暗面 Moonshot | platform.moonshot.cn | Kimi | `moonshot-*` naming |
+| 智谱 Zhipu | open.bigmodel.cn | GLM (ChatGLM) | `glm-*` naming |
+| 百度 Baidu | cloud.baidu.com | 文心 ERNIE | `ernie-*` naming |
+| 零一万物 01.AI | platform.lingyiwanwu.com | Yi | `yi-*` naming |
+| MiniMax | platform.minimaxi.com | MiniMax (M2.7, etc.) | `minimax-*` naming |
+| 小米 Xiaomi | mimo.xiaomi.com | MiMo | `mimo-*` naming |
+
+## Common Model Naming Patterns
+
+- `*-flash` / `*-lite` → lightweight/fast inference variants
+- `*-fast` → speed-optimized, may sacrifice some quality
+- `*-instruct` → instruction-tuned for chat
+- `*-coder` / `*-code` → code-specialized
+- `*-v1`, `*-v2`, `*-v3` → version iterations
+- Parameter count often embedded: `6.7B`, `72B`, etc.
+
+## How to Research an Unknown Model
+
+1. **mmx search** with model name + "评测" or "benchmark"
+2. Check the provider's official docs (see table above)
+3. Check LMSYS Chatbot Arena leaderboard (lmarena.ai)
+4. Check non-linear Chinese LLM benchmark (github.com/jeinlee1991/chinese-llm-benchmark)
+
+## Quick Classification Heuristics
+
+- If name contains a provider prefix (sensenova, deepseek, qwen...) → look up that provider
+- If name contains parameter count (6.7B, 7B, 72B) → compare against known models of similar size
+- If name contains "flash/lite/fast" → speed variant, likely lower quality than base model
+- "Lite" models: often 1B-7B range, good for simple tasks
+- "Flash/Fast" models: optimized inference, may use MoE or quantization