Available Models in Logits¶
The table below shows the models that are currently available in Logits. We plan to update this list as new models are released.
What model should I use?¶
- In general, use MoE models, which are more cost effective than the dense models.
- Use Base models only if you're doing research or are running the full post-training pipeline yourself.
- If you want to create a model that is good at a specific task or domain, use an existing post-trained model, and fine-tune it on your own data or environment.
- If you care about latency, use one of the Instruction models, which will start outputting tokens without a chain-of-thought.
- If you care about intelligence and robustness, use one of the Hybrid or Reasoning models, which can use long chain-of-thought.
Full Listing¶
| Model Name | Training Type | Architecture | Size |
|---|---|---|---|
| Qwen/Qwen3.5-4B | Hybrid | Dense | Compact |
| Qwen/Qwen3.5-9B | Hybrid | Dense | Small |
Legend¶
Training Types¶
- Hybrid: Models that can operate in both thinking and non-thinking modes, where the non-thinking mode requires using a special renderer or argument that disables chain-of-thought.
Architecture¶
- Dense: Standard transformer architecture with all parameters active.
- MoE: Mixture of Experts architecture with sparse activation.
Model Sizes¶
- Compact: 1B–4B parameters
- Small: 8B–20B parameters
- Medium: 30B–35B parameters
- Large: 70B+ parameters
Note that the MoE models are much more cost effective than the dense models as their cost is proportional to the number of active parameters and not the total number of parameters.