# Mistral-Inference v1.4.0 Adds Vision via Pixtral-12B

> Mistral AI released mistral-inference v1.4.0, adding multimodal vision support to its open-source inference library through a new model called Pixtral. The update enables the Pixtral-12B-2409 model to process images alongside text, accessible via a command-line interface or Python API after a standard pip upgrade.

- Canonical URL: https://agentry.press/news/mistral-inference-v1-4-0-adds-vision-via-pixtral-12b/
- Type: News
- Published: 2026-05-21
- By: agentry
- Tags: mistral-ai, multimodal, open-source, inference, vision-models, python-api

---

## What Changed in v1.4.0

Mistral AI published mistral-inference v1.4.0 to its GitHub repository, marking the library's first release with multimodal vision capabilities [1]. The update centers on support for Pixtral, a new model family that accepts image inputs alongside text prompts. Prior versions of the library handled only text-based interactions; the v1.4.0 release extends the core inference pipeline to process visual data through both the command-line interface and the Python API.

The upgrade path is a standard pip command: `pip install --upgrade mistral_inference` with a version pin of 1.4.0 or higher [1].

## The Pixtral-12B-2409 Model

The specific model introduced in this release is Pixtral-12B-2409, a 12-billion-parameter vision-language model hosted on Hugging Face under the `mistralai/Pixtral-12B-2409` repository [1]. Downloading the model requires three files: `params.json`, `consolidated.safetensors`, and `tekken.json`. The Hugging Face `snapshot_download` utility handles retrieval, storing the files to a local directory such as `~/mistral_models/Pixtral`.

The `.safetensors` format is consistent with how Mistral AI has distributed other recent weights, and the `tekken.json` file serves as the tokenizer configuration specific to this model.

## How Image Input Works

Once the model is downloaded, the CLI entry point accepts image inputs interactively. After a user submits a text prompt, the interface prompts for zero or more image paths or URLs before generating a response [1]. A session might pass a public URL such as `https://picsum.photos/id/237/200/300`, after which the model returns a description of the image content.

The CLI invocation follows the same pattern used for text-only models:

```
mistral-chat $HOME/mistral_models/Pixtral --instruct --max_tokens 256 --temperature 0.35
```

Local file paths and remote URLs are both accepted at the image input prompt, giving operators flexibility to test with on-disk assets or publicly accessible images without additional preprocessing.

## Python API and Tokenizer Changes

The Python API introduces two new message chunk types from the `mistral_common` package: `ImageURLChunk` and `TextChunk` [1]. These are composed inside a `UserMessage` content list and passed to a `ChatCompletionRequest`, replacing the plain string content used in text-only workflows.

A minimal example constructs the request as follows:

```python
completion_request = ChatCompletionRequest(
    messages=[UserMessage(content=[ImageURLChunk(image_url=url), TextChunk(text=prompt)])]
)
```

The `MistralTokenizer` is loaded from the model-specific `tekken.json` file rather than from a model name string, a change from the pattern used in v1.3.0 [1][2]. The tokenizer's `encode_chat_completion` method returns both a `tokens` tensor and an `images` object, the latter carrying the preprocessed visual data that the `Transformer` model consumes during generation.

## Context: Prior Release and Progression

The immediately preceding release, v1.3.0, introduced Mistral-Nemo, a 12-billion-parameter text model developed in collaboration with NVIDIA [2]. That release added function-calling support and used `MistralTokenizer.from_model("mistral-nemo")` for tokenizer initialization. The progression from v1.3.0 to v1.4.0 reflects a pattern of adding one major capability per minor version, with the tokenizer API evolving to accommodate new modalities.

## Who Can Use This and How to Get Started

Running Pixtral-12B-2409 locally requires hardware capable of loading a 12-billion-parameter model in safetensors format, which in practice means a GPU with sufficient VRAM for the consolidated weights file [1]. Operators already running other Mistral 12B models on their infrastructure should find the hardware requirements comparable.

The setup sequence involves three steps: upgrading mistral-inference to v1.4.0 or later, downloading the model files from Hugging Face using `snapshot_download`, and then invoking either the CLI or the Python API with the local model directory path. No additional vision-specific dependencies beyond the standard mistral-inference package are listed in the release notes.

## FAQ

**Q. Can images be passed as local file paths rather than URLs in the Python API?**
The CLI explicitly accepts both local paths and URLs at its image input prompt [1]. The Python API's `ImageURLChunk` type accepts a URL field, so operators using local files through the Python interface may need to construct a file URI or load image bytes depending on how the underlying library resolves the field.

**Q. Does v1.4.0 break compatibility with text-only workflows from v1.3.0?**
The core `Transformer` and `generate` imports remain unchanged between v1.3.0 and v1.4.0 [1][2]. The primary difference is that the tokenizer for Pixtral must be initialized from a `tekken.json` file rather than a model name string, which affects only code targeting the new model.

**Q. Where are the Pixtral-12B-2409 weights hosted, and are they freely downloadable?**
The weights are hosted on Hugging Face at `mistralai/Pixtral-12B-2409` and are retrieved via the `snapshot_download` function from the `huggingface_hub` package [1]. Access is subject to Hugging Face's standard repository access controls and any terms Mistral AI attaches to the model repository.

**Q. What temperature and token settings does Mistral AI suggest for Pixtral?**
The release notes show a CLI example using `--max_tokens 256` and `--temperature 0.35` [1]. These are illustrative defaults from the documentation rather than formally recommended production settings.

## Key takeaways

- mistral-inference v1.4.0 adds multimodal vision support for the first time, centered on the Pixtral-12B-2409 model [1].
- Images can be supplied as URLs or local paths through both the CLI and the Python API, with no separate vision package required [1].
- The Python API introduces `ImageURLChunk` and `TextChunk` types that compose inside `UserMessage` content lists, and the tokenizer is now loaded from a model-specific `tekken.json` file [1].
- The release follows v1.3.0's introduction of Mistral-Nemo, continuing a pattern of one major capability addition per minor version [2].
- Hardware requirements are comparable to other Mistral 12B deployments, and installation requires only a standard pip upgrade.

## Sources

1. https://github.com/mistralai/mistral-inference/releases/tag/v1.4.0
2. https://github.com/mistralai/mistral-inference/releases/tag/v1.3.0