---
title: "Mistral Medium 3.5"
description: ""
---
[Mistral Medium 3.5](https://huggingface.co/mistralai) is Mistral AI's
flagship **128B dense** model that merges instruction-following, reasoning,
and coding into a single checkpoint with a configurable reasoning mode.
It unifies the lineage of *Mistral Medium 3.1*, *Magistral Medium*, and
*Devstral 2* into one model, and ships natively in FP8 (per-tensor
`weight_scale_inv`) so the full model fits inside an H200 node or 2 ×
H100 nodes — a notable footprint advantage over comparably-capable
Mixture-of-Experts (MoE) systems.

<Info>

| | |
|---|---|
| **Task** | Image-Text-to-Text |
| **Architecture** | `Mistral3ForConditionalGeneration` (Pixtral vision tower + dense Ministral-3 text decoder) |
| **Parameters** | 128B (dense, FP8 on disk) |
| **Context Window** | 256k tokens |
| **Languages** | 40+ (English, French, Spanish, German, Russian, Chinese, Japanese, Italian, Portuguese, Arabic, Hindi, Korean, plus Indic / Nordic / Eastern European tail) |
| **License** | Modified MIT (open-weights, ≤ $20M annual revenue threshold) |
| **HF Org** | [mistralai](https://huggingface.co/mistralai) |

</Info>

## Architecture

Mistral Medium 3.5 is a **dense** transformer — no MoE routing — built on
the same text backbone as
[`mistralai/Devstral-2-123B-Instruct-2512`](https://huggingface.co/mistralai/Devstral-2-123B-Instruct-2512):
88 Ministral-3 decoder layers (hidden 12288, 96 attention heads,
8 KV heads, GQA) with the standard llama-style RoPE + RMSNorm + SwiGLU
MLP layout. The multimodal variant adds a Pixtral vision tower and
multi-modal projector on top, making it an
`AutoModelForImageTextToText` checkpoint.

Compared with MoE models of similar capability, the dense layout
trades sparse-activation throughput for a substantially smaller
deployment footprint — relevant when you want to fine-tune or serve
the model on a single node.

## Key Strengths

- **Compactness.** Dense 128B fits in fewer GPUs than the comparable
  MoE class — a single H200 node or 2 × H100 nodes for inference.
- **Configurable reasoning mode.** One checkpoint covers chat,
  agentic, and reasoning workloads; the reasoning mode is toggled at
  inference time.
- **Strong agentic performance.** Competitive on tool-use and
  decision-making benchmarks; suitable as a base for connector-driven
  agent workflows.
- **Long context.** 256k-token window for document parsing and
  research-assistant use cases.

Trade-offs disclosed in the model card: weaker non-agentic benchmark
performance and more verbose outputs than some closed-source
competitors.

## Use Cases

- Agentic workflows with connectors
- Cloud and local async coding
- Document parsing (multimodal — text + image)
- Research assistants
- General chat
- Base model for downstream fine-tuning

## Available Models

- **Mistral-Medium-3.5 128B**

## Class

- HF: `Mistral3ForConditionalGeneration`
- NeMo AutoModel custom: `Mistral3FP8VLMForConditionalGeneration`
  ([source](https://github.com/NVIDIA-NeMo/Automodel/blob/main/nemo_automodel/components/models/mistral3_vlm/model.py))

The custom class extends HF's `Mistral3ForConditionalGeneration` and
attaches a `Mistral3FP8StateDictAdapter.for_vlm_full()` so the FP8
checkpoint dequantizes per-shard inside the standard DCP load — the
full BF16 model is never materialized on a single rank, allowing TP+PP
training to fit on H100-80GB.

## Example HF Models

| Model | HF ID |
|---|---|
| Mistral Medium 3.5 128B | [`mistralai/Mistral-Medium-3.5`](https://huggingface.co/mistralai) |

## Example Recipes

| Recipe | Dataset | Description |
|---|---|---|
| [mistral3p5_128b_medpix.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/mistral3p5/mistral3p5_128b_medpix.yaml) | MedPix-VQA | SFT — Mistral Medium 3.5 128B on MedPix, 8 nodes × 8 GPUs (TP=8 PP=8) |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

<Note>
This recipe was validated on **8 nodes × 8 GPUs (64 H100s)** with
TP=8 PP=8 DP=1. See the [Launcher Guide](/job-launchers/slurm-cluster)
for multi-node setup. Inference / single-node fine-tune fits in
**1 × H200** or **2 × H100** nodes thanks to the dense + FP8 layout.

</Note>

**3. Run the recipe** via Slurm (see the
[fine-tuning guide](/recipes-e2e-examples/mistral-medium-3-5) for a
complete launch script):

```bash
sbatch your_slurm_script.sub
```

<Accordion title="Run with Docker">
**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.02.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/vlm_finetune/mistral3p5/mistral3p5_128b_medpix.yaml
```
</Accordion>

See the [Installation Guide](/get-started/installation) and the
[Mistral Medium 3.5 Fine-Tuning Guide](/recipes-e2e-examples/mistral-medium-3-5).

## Fine-Tuning

See the [Mistral Medium 3.5 Fine-Tuning Guide](/recipes-e2e-examples/mistral-medium-3-5).

## Hugging Face Model Cards

- [mistralai](https://huggingface.co/mistralai)
- Related architecture: [`mistralai/Devstral-2-123B-Instruct-2512`](https://huggingface.co/mistralai/Devstral-2-123B-Instruct-2512)
