Approved models
11
Records published from this collection
Provider information
lab
Review approach
Verify each model’s source, terms, capabilities, and current availability
Tencent models in this directory cover text-to-text ai models. Start with the task you need to solve, then open a model profile to check its recorded inputs, outputs, licensing and deployment options.
Use inputs that resemble deployment traffic, including difficult and out-of-domain cases. Compare output correctness, the response format, latency and memory requirements with a simple baseline under the same conditions. Open-weight checkpoints need a runtime and enough memory for the exact format you select; a repository listing alone does not provide a hosted API.
Check whether two entries describe distinct models or different hosted routes for the same model. Keep the checkpoint, host, test settings and evaluation date together. Missing prices and limits mean the directory has no recorded figure, not that use is free or unlimited.
Browse the AI model directory or compare two models.
Review each record individually; collection membership does not imply a recommendation or guarantee of availability.
| Hunyuan Hy4 Preview Tencent | 26.7 | 1M | — | — | — |
| Hy-MT2-7B Tencent | — | 8.19K | — | — | — |
| Hy3 Tencent | 57.4 | 256K | $0.00 | $0.00 | — |
| Hy3 preview Tencent | 57.0 | 256K | $0.00 | $0.00 | — |
| EVIE 4.5B Tencent | 10.0 | — | — | — | — |
| EVIE 8B Tencent | 10.0 | — | — | — | — |
| WeMM Embedding 2B Tencent | 15.0 | — | — | — | — |
| WeMM Embedding 4B Tencent | 15.0 | — | — | — | — |
| WeMM Embedding 9B Tencent | 15.0 | — | — | — | — |
| WeVisDoc 2B Tencent | 5.0 | — | — | — | — |
| WeVisDoc 4B Tencent | 5.0 | — | — | — | — |
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About this order: Recommended shows our editorial shortlist first, followed by recent releases. The spec score is a 0–100 figure SyncDev calculates from each vendor's published specifications — context window, reasoning support, input modalities, tool calling, maximum output and how recently the model shipped. It measures breadth of capability, not benchmark performance, so a higher-scoring model is not automatically the better choice for your task.