feat(inference): add mistral large 3 to model catalog (#6045)

* feat(inference): add mistral large 3 to model catalog

* Update pages/managed-inference/reference-content/model-catalog.mdx

---------

Co-authored-by: Rowena Jones <36301604+RoRoJ@users.noreply.github.com>

Author: fpagny

pages/managed-inference/reference-content/model-catalog.mdx

@@ -28,6 +28,7 @@ A quick overview of available models in Scaleway's catalog and their core attrib
 | [`deepseek-r1-distill-70b`](#deepseek-r1-distill-llama-70b) | Deepseek | 128k | Text | H100 (13k), H100-2 | [MIT](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/blob/main/LICENSE) and [Llama 3.3 Community](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE) |
 | [`deepseek-r1-distill-8b`](#deepseek-r1-distill-llama-8b) | Deepseek | 128k | Text | L4 (90k), L40S, H100, H100-2 | [MIT](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/blob/main/LICENSE) and [Llama 3.1 Community](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE) |
 | [`mistral-7b-instruct-v0.3`](#mistral-7b-instruct-v03) | Mistral | 32k | Text | L4, L40S, H100, H100-2 | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
+| [`mistral-large-3-675b-instruct-2512`](#mistral-large-3-675b-instruct-2512) | Mistral | 250k | Text, Vision | H100-SXM-8 (180k) | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
 | [`mistral-small-3.2-24b-instruct-2506`](#mistral-small-32-24b-instruct-2506) | Mistral | 128k | Text, Vision | H100, H100-2 | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
 | [`mistral-small-3.1-24b-instruct-2503`](#mistral-small-31-24b-instruct-2503) | Mistral | 128k | Text, Vision | H100, H100-2 | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
 | [`mistral-small-24b-instruct-2501`](#mistral-small-24b-instruct-2501) | Mistral | 32k | Text | L40S (20k), H100, H100-2 | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
@@ -64,6 +65,7 @@ A quick overview of available models in Scaleway's catalog and their core attrib
 | `deepseek-r1-distill-llama-70B` | Yes | Yes | English, Chinese |
 | `deepseek-r1-distill-llama-8B` | Yes | Yes | English, Chinese |
 | `mistral-7b-instruct-v0.3` | Yes | Yes | English |
+| `mistral-large-3-675b-instruct-2512` | Yes | Yes | English, French, German, Spanish, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic |
 | `mistral-small-3.2-24b-instruct-2506` | Yes | Yes | English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi |
 | `mistral-small-3.1-24b-instruct-2503` | Yes | Yes | English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi |
 | `mistral-small-24b-instruct-2501` | Yes | Yes | English, French, German, Dutch, Spanish, Italian, Polish, Portuguese, Chinese, Japanese, Korean |
@@ -111,6 +113,21 @@ google/gemma-3-27b-it:bf16
 
 - Pan & Scan is not yet supported for Gemma 3 images. This means that high resolution images are currently resized to 896x896 resolution that may generate artifacts and lead to a lower accuracy.
 
+### Mistral-large-3-675b-instruct-2512
+Mistral-large-3-675b-instruct-2512 is a frontier model, performing among the best open-weight models as of December 2025. It is ideal for agentic workflows and image understanding.
+
+| Attribute | Value |
+|-----------|-------|
+| Supports parallel tool calling | Yes |
+| Supported images formats | PNG, JPEG, WEBP, and non-animated GIFs |
+| Maximum image resolution (pixels) | 1540x1540 |
+| Token dimension (pixels)| 28x28 |
+
+#### Model names
+```
+mistral/mistral-large-3-675b-instruct-2512:fp4
+```
+
 ### Mistral-small-3.2-24b-instruct-2506
 Mistral-small-3.2-24b-instruct-2506 is an improved version of Mistral-small-3.1 which performs better on tool calling.
 This model was optimized to have a dense knowledge and faster tokens throughput compared to its size.