# Mistral Large 4

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 released Mistral Large 4 as a Research Public Preview, a 1T-parameter model with 49B active parameters whose weights are planned for release at the end of October. It scores 38 on the Artificial Analysis Intelligence Index, comparable to GPT-6 Luna (max, 38) and just below DeepSeek V4.1 Flash (max, 39), making it the highest-scoring model from outside the US and China. The same preview scores 50 on the Cyber Index, level with GLM-5.3-Flash, while costing $1.13 per Intelligence Index task at standard pricing, above similar-intelligence open-weights models.

Mistral released Mistral Large 4 as a Research Public Preview, a 1T-parameter model with 49B active parameters whose weights are planned for release at the end of October. It scores 38 on the Artificial Analysis Intelligence Index, comparable to GPT-6 Luna (max, 38) and just below DeepSeek V4.1 Flash (max, 39), making it the highest-scoring model from outside the US and China. The same preview scores 50 on the Cyber Index, level with GLM-5.3-Flash, while costing $1.13 per Intelligence Index task at standard pricing, above similar-intelligence open-weights models.

Mistral released Mistral Large 4, codenamed Le Chonk, and expanded its enterprise platform Mistral Studio with managed agents and apps plus new workflow, enterprise-context, agent-observability and evaluation capabilities. Previously teams had to build the infrastructure an agent runs on; Studio now hosts execution, and developers connect the model to enterprise data and systems through connectors and MCP servers, then set boundaries on the tools, data and actions it can reach. Mistral says an incident-investigation agent built by its engineering team saves hundreds of hours a month, and a product-operations workflow saves about 35 hours a week.

Mistral released Mistral Large 4, codenamed Le Chonk, and expanded its enterprise platform Mistral Studio with managed agents and apps plus new workflow, enterprise-context, agent-observability and evaluation capabilities. Previously teams had to build the infrastructure an agent runs on; Studio now hosts execution, and developers connect the model to enterprise data and systems through connectors and MCP servers, then set boundaries on the tools, data and actions it can reach. Mistral says an incident-investigation agent built by its engineering team saves hundreds of hours a month, and a product-operations workflow saves about 35 hours a week.

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.

vLLM added same-day inference support for Mistral Large 4, a 1-trillion-parameter sparse mixture-of-experts multimodal model with 49 billion parameters active per token. The work spans breakable CUDA graphs, hand-written fusions, decode and prefill context parallelism, DSpark speculative decoding, NIXL disaggregated serving and host-memory KV offload. On TP8, NVFP4 decodes 1.08–1.19x faster per token than FP8 and holds about 1.5x the KV cache, while staying within 2.1 points of FP8 on multimodal and long-context evaluations.

vLLM added same-day inference support for Mistral Large 4, a 1-trillion-parameter sparse mixture-of-experts multimodal model with 49 billion parameters active per token. The work spans breakable CUDA graphs, hand-written fusions, decode and prefill context parallelism, DSpark speculative decoding, NIXL disaggregated serving and host-memory KV offload. On TP8, NVFP4 decodes 1.08–1.19x faster per token than FP8 and holds about 1.5x the KV cache, while staying within 2.1 points of FP8 on multimodal and long-context evaluations.