# 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