# Multimodal Reasoning

Mistral released a public preview of Mistral Large 4, a natively multimodal model with 1 trillion total parameters and 49 billion active parameters, with weights promised by the end of the month. Open-weight models have generally trailed the strongest closed frontier models on enterprise tasks such as cybersecurity, finance and law; Mistral says ML4 is state of the art among open models in those areas and edges GPT-6-Astra on the Dense 200 visual-grounding test, 42% versus 41%. On one Artificial Analysis Cyber Index test that asks a model to reproduce a real vulnerability and then patch it, ML4 scores 82%, the highest of any model.

Mistral released a public preview of Mistral Large 4, a natively multimodal model with 1 trillion total parameters and 49 billion active parameters, with weights promised by the end of the month. Open-weight models have generally trailed the strongest closed frontier models on enterprise tasks such as cybersecurity, finance and law; Mistral says ML4 is state of the art among open models in those areas and edges GPT-6-Astra on the Dense 200 visual-grounding test, 42% versus 41%. On one Artificial Analysis Cyber Index test that asks a model to reproduce a real vulnerability and then patch it, ML4 scores 82%, the highest of any model.

Mistral AI has released the weights of its flagship Mistral Large 4 (Le Chonk), a trillion-parameter natively multimodal model improved by three more weeks of reinforcement learning after its public preview. On the Artificial Analysis Coding Agent Index it scores 48%, effectively tied with leading open-weight model GLM-5.3 while using 47% fewer tokens per task on average; on Terminal-Bench 4.0 it gains 13 percentage points over its own preview to 41% with 30% fewer output tokens. Three reasoning modes let developers trade performance against inference cost.

Mistral AI has released the weights of its flagship Mistral Large 4 (Le Chonk), a trillion-parameter natively multimodal model improved by three more weeks of reinforcement learning after its public preview. On the Artificial Analysis Coding Agent Index it scores 48%, effectively tied with leading open-weight model GLM-5.3 while using 47% fewer tokens per task on average; on Terminal-Bench 4.0 it gains 13 percentage points over its own preview to 41% with 30% fewer output tokens. Three reasoning modes let developers trade performance against inference cost.

Switzerland's Apertus project released 1.5 versions of its 8B and 70B models, continuing pretraining from 1.0 to add image and audio understanding, an optional thinking mode, and a context window of 262,144 tokens. Multimodality arrives by early fusion: images and audio become discrete tokens placed in the same sequence as text, with no separate vision encoder, so the architecture is unchanged and training runs at text-only speed. The models read images and audio but generate only text, speech handling remains experimental, and the project says the release still trails frontier open-weight models.

Switzerland's Apertus project released 1.5 versions of its 8B and 70B models, continuing pretraining from 1.0 to add image and audio understanding, an optional thinking mode, and a context window of 262,144 tokens. Multimodality arrives by early fusion: images and audio become discrete tokens placed in the same sequence as text, with no separate vision encoder, so the architecture is unchanged and training runs at text-only speed. The models read images and audio but generate only text, speech handling remains experimental, and the project says the release still trails frontier open-weight models.

NVIDIA released Nemotron 3.5 Super VL, an open, customizable multimodal model for long enterprise agent workflows that need reasoning, tool use, and text, image, document, and video understanding. It uses a 120B-parameter mixture-of-experts architecture that activates 12B parameters per token, and NVIDIA reports up to 5x higher throughput on agentic tasks than other open models in its size class on a single GPU. Teams can post-train it with NeMo on their own data, tools, and workflows, and NVIDIA also released agentic tool-use, finance reasoning, and 40 vision-language datasets.

NVIDIA released Nemotron 3.5 Super VL, an open, customizable multimodal model for long enterprise agent workflows that need reasoning, tool use, and text, image, document, and video understanding. It uses a 120B-parameter mixture-of-experts architecture that activates 12B parameters per token, and NVIDIA reports up to 5x higher throughput on agentic tasks than other open models in its size class on a single GPU. Teams can post-train it with NeMo on their own data, tools, and workflows, and NVIDIA also released agentic tool-use, finance reasoning, and 40 vision-language datasets.