> ## Documentation Index
> Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Models & Pricing

> Supported models and pricing for Hosted Training

Hosted Training supports a range of open-weights models. This page lists the currently available models, their pricing, and guidance on choosing the right model for your use case.

## Available Models

Prices are per million tokens, billed separately for input, output, and training.

| Model                                           | Input (\$ / 1M) | Output (\$ / 1M) | Train (\$ / 1M) |
| ----------------------------------------------- | --------------- | ---------------- | --------------- |
| `Qwen/Qwen3.5-0.8B`                             | 0.02            | 0.06             | 0.06            |
| `Qwen/Qwen3.5-2B`                               | 0.05            | 0.15             | 0.15            |
| `Qwen/Qwen3.5-4B`                               | 0.10            | 0.30             | 0.30            |
| `Qwen/Qwen3.5-9B`                               | 0.20            | 0.60             | 0.60            |
| `Qwen/Qwen3.5-35B-A3B`                          | 0.25            | 0.75             | 1.00            |
| `Qwen/Qwen3.5-122B-A10B`                        | 0.50            | 1.50             | 2.00            |
| `Qwen/Qwen3.5-397B-A17B`                        | 1.00            | 3.00             | 4.00            |
| `meta-llama/Llama-3.2-1B-Instruct`              | 0.02            | 0.06             | 0.06            |
| `meta-llama/Llama-3.2-3B-Instruct`              | 0.05            | 0.15             | 0.15            |
| `nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16`    | 0.15            | 0.45             | 0.60            |
| `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16` | 0.30            | 0.90             | 1.20            |
| `openai/gpt-oss-20b`                            | 0.10            | 0.30             | 0.40            |
| `openai/gpt-oss-120b`                           | 0.25            | 0.75             | 1.00            |

## Choosing a Model

### For Validation and Debugging

Start with a small, fast model to verify your environment and config work correctly before committing compute to a larger run.

**Recommended:** `Qwen/Qwen3.5-0.8B` or `meta-llama/Llama-3.2-1B-Instruct`

```toml theme={null}
model = "Qwen/Qwen3.5-0.8B"
max_steps = 50
batch_size = 128
rollouts_per_example = 8
```

### For Experimentation

MoE models with small active parameter counts give strong performance per token.

**Recommended:** `Qwen/Qwen3.5-35B-A3B`

```toml theme={null}
model = "Qwen/Qwen3.5-35B-A3B"
max_steps = 200
batch_size = 256
rollouts_per_example = 16
```

### For Production Training

For serious training runs where you want the strongest possible results, use the larger MoE models.

**Recommended:** `Qwen/Qwen3.5-397B-A17B` or `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16`

```toml theme={null}
model = "Qwen/Qwen3.5-397B-A17B"
max_steps = 500
batch_size = 512
rollouts_per_example = 16
```

### Thinking Mode

Qwen3.5 and Nemotron models support a thinking mode that produces extended chain-of-thought reasoning before the final answer. Toggle it via `[sampling].enable_thinking` in your config. Thinking mode tends to help on tasks that benefit from multi-step reasoning (math, code, logic) at the cost of longer outputs.

## Checking Available Models

Always use the CLI to check the current list of supported models:

```bash theme={null}
prime train models
```

Add `--output json` to get the live pricing alongside the model list. This may differ from this page as models are being added regularly.