Model release #Open models Oct 7, 2026 · Originally published Oct 6

Mistral Large 4 enters public preview: 1T total, 49B active, $1.36 per million input tokens, weights by month end

Mistral's largest model to date, natively multimodal; the preview API is live on Mistral Studio and the weights are due by the end of the month.

Primary source Mistral AI blog · mistral.ai Read the original ↗

// Key points

  • A 1 trillion-parameter natively multimodal model with 49 billion active parameters, fluent in more than 160 languages including every official EU language.
  • Preview API pricing is $1.36 per million input tokens and $4.18 per million output tokens; Mistral says it will release the weights by the end of the month, after real-world red-teaming with security leaders and partners.
  • Coding: 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA and 28.3% on Terminal-Bench 4; 59.9% on AutomationBench, which covers 657 business workflows.
  • Security: 82% on the AA Cyber Index test that asks a model to reproduce and then patch a real vulnerability, and 93% of Cybench's 40 challenges; trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs.

Builder's takeAt $1.36 in and $4.18 out, it's worth running PandaClaws' long-form generation and multilingual rewriting against it. But this is a preview with no weights or license yet, so I'd benchmark it on my own eval set through the API now and only weigh self-hosting once the weights and license terms land at month end.

// Background · from #Open models

Full timeline →
  1. Oct 7 Google open-sources EmbeddingGemma 2: a 270M-to-740M multimodal embedding model, 14% better on code retrieval, Apache 2.0
  2. Oct 6 Aleph Alpha open-sources Kolibri: a 78B MoE with 3.46B active per token, validated to 1M-token context, Apache 2.0
  3. Oct 5 Ai2 open-sources AstaBrief 8B, which writes a cited research report in one pass, about 3.5x faster than its Claude-powered mode