Mistral Large 4

SiliconANGLE AI·Oct 29, 2026

Mistral releases open weights for Mistral Large 4, providing sovereign control for the company’s ‘Le Chonk’ AI model

MarkTechPost·Oct 21, 2026

Mistral AI Releases Voxtral 3: Speech-to-Text, TTS and a Voice Agent SDK

Mistral AI has released Voxtral 3, a family of speech models and a voice agent SDK. The release includes three transcription models, one text-to-speech model, and an SDK integrated with LiveKit, designed for listening, reasoning, tool calls, and speaking. The Voxtral 3 family comprises Voxtral Small Transcribe 3, the flagship batch transcription and diarization model, which also supports translation, speech Q&A, summarization, and function calling. Voxtral Mini Transcribe 3 is a 4B parameter model for more economical batch jobs, while Voxtral Mini Transcribe Realtime 3 offers streaming ASR with end-of-turn detection. The Voxtral Speech 3 model, also referred to as Voxtral Mini TTS 3, handles text-to-speech. All transcription models feature diarization and context biasing capabilities, with batch transcription supporting 50 languages, including 24 official EU languages. Voxtral Mini Transcribe 3 has 4B parameters and a 32k context window. The Voxtral voice agent SDK operates as a three-stage streaming pipeline. It utilizes Realtime Transcribe 3 for listening and turn detection, a general LLM like Mistral Small 4 for reasoning and tool execution, and Voxtral Speech 3 for replies that match the conversation's tone. Each turn is completed in under one second. Performance benchmarks show Voxtral 3 models ranking highly on independent speech-to-text evaluations. Small Transcribe 3 achieved a 2.1% AA-WER, placing it third on Artificial Analysis. In real-time performance, it recorded a 7.4% WER on FLEURS 35 at 240 ms, compared to Gemini 3.5 Realtime's 4.2% WER.

MarkTechPost·Oct 19, 2026

Mistral AI Releases Timestral: A 209M Forecasting Model Family That Outperforms Google’s TimesFM-3

Mistral AI has released Timestral, its first family of time series foundation models. This new set includes four zero-shot forecasting models, with parameter counts ranging from 5 million to 209 million. The largest model in the Timestral family has achieved the top rank among pretrained, zero-shot models on the GIFT-Eval, TIME, and fev-bench benchmarks. Timestral models can perform probabilistic forecasting on standard server CPUs, eliminating the need for GPUs during inference. The models offer open weights and inference code for developers. The largest 209M parameter model requires 837 MB in fp32 and can forecast multivariate targets jointly, accepting past-only, known-future, and categorical covariates. The performance of the 209M Timestral model is notable, with Mistral reporting a fev-bench SQL skill score of 50.39, surpassing Google's TimesFM-3 score of 48.99. On GIFT-Eval, its MASE score of 0.658 leads t0-gamma by a narrow margin of 0.003. Timestral is a causal, patch-based transformer utilizing time attention and variate attention. Unlike classical tools that fit one series at a time or deep models that train from scratch on each dataset, Timestral transfers patterns across domains. Each model outputs 21 quantiles per time step. The 209M model features 24 layers and an embedding dimension of 768, with each patch covering 32 time steps.

MarkTechPost·Oct 6, 2026

Mistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model

Mistral AI announced the public preview release of Mistral Large 4 (ML4), internally codenamed 'Le Chonk,' on October 6, 2026. This new model is a granular Mixture of Experts (MoE) architecture, boasting a total of 1.05 trillion parameters. Specifically, it features 49 billion active parameters per token, a 1.6 billion parameter vision encoder for native image input, and an extensive 1 million token context window. The model was trained from scratch using 3,800 NVIDIA Grace Blackwell GPUs within Mistral's own European data centers. The API for ML4 is now accessible, with pricing set at $1.36 per 1 million input tokens and $4.18 per 1 million output tokens. However, the model weights are scheduled for release at the end of October, meaning self-hosting capabilities will not be available until then. Mistral AI highlights ML4's standout performance in cybersecurity, reporting a 93% score on the Cybench benchmark and 82% on CyberGym-E2E. The company noted that several closed frontier models achieved near-zero scores on these benchmarks due to task refusal. The architecture of ML4 is described as a hybrid instruct-and-reasoning MoE that natively accepts image inputs. The efficient activation of only approximately 4.7% of the total weights per token allows a model of this scale to be served at mid-tier pricing. While the full 1.05 trillion parameters require memory, the activation count dictates the compute resources needed, not the overall hardware expenditure for users. Mistral has not yet disclosed the specific expert count, top-k routing mechanisms, or layer layout, details that are expected to be provided upon the release of the model weights. The training data for ML4 encompassed over 160 languages, including all official European Union languages, indicating a broad linguistic capability. Mistral AI, founded in 2023 by former Google DeepMind and Meta AI researchers, is a European artificial intelligence company focused on developing open and efficient large language models. The company has previously released models like Mistral 7B and Mixtral 8x7B, which gained traction for their performance and accessibility. The development of ML4 signifies a significant step forward in the company's ambition to compete with larger, established AI labs by pushing the boundaries of model scale and multimodal capabilities while maintaining a focus on efficiency and European data sovereignty. The release of ML4 positions Mistral AI as a key player in the competitive landscape of advanced AI development, particularly with its emphasis on specialized benchmarks like cybersecurity.

MarkTechPost·Aug 8, 2026

Mistral AI Releases Shieldstral 1.0 3B: An Open-Weights Policy-Adaptive Multimodal Safety Classifier Matching Models 7× Its Size

Mistral AI has released Shieldstral 1.0 3B, an open-weights, policy-adaptive multimodal safety classifier designed to handle content moderation by framing it as a single yes/no question rather than relying on a predefined taxonomy of harm categories. Unlike many existing guardrail models that embed their category lists directly into their weights, requiring retraining for new deployment contexts, Shieldstral allows operators to define policies using plain-language questions at inference time. The model then returns a calibrated safety score after a single forward pass. This approach enables dynamic policy adaptation, allowing content that might be acceptable in one context, such as a cybersecurity research tool, to be flagged as harmful in another, like a mental-health platform. Shieldstral 1.0 3B is built upon the Ministral-3-3B-Base-2512 model and incorporates a native Pixtral vision encoder. It is released under the permissive Apache 2.0 license, permitting both commercial and non-commercial use. In performance evaluations, Shieldstral reports an 84.9% average F1 score for text safety, a performance level that matches the GPT-OSS-Safeguard-20B model. For multimodal safety, it achieves an 83.8% F1 score, outperforming all other baselines evaluated by Mistral. The model's architecture and size make it deployable locally. Shieldstral-1.0-3B requires 16GB of VRAM when using BF16 precision and can operate on a single GPU. Serving the model is facilitated through multiple established pathways, including vLLM (version 0.26.0 and later, recommended), llama.cpp via GGUF conversion with Q8_0, Q5_K_M, or Q4_K_M quantization, SGLang, and the Transformers library. Fine-tuning is also supported through the Axolotl framework. The classifier's output is a single token, which contributes to significantly lower latency and cost compared to reasoning-based guardrails like GPT-OSS-Safeguard-20B. The 16GB VRAM requirement positions Shieldstral 1.0 3B as an accessible solution for early-stage AI product teams that may not have the resources to contract with dedicated moderation vendors. Furthermore, its open license and self-hosting capabilities are advantageous for mid-market and enterprise organizations that require guardrails within their Virtual Private Cloud (VPC) or on-premises infrastructure to comply with data residency regulations and facilitate auditing. Multi-tenant SaaS providers can also benefit, as a single checkpoint can be configured to enforce distinct policies for each customer. The model is particularly relevant for industries such as consumer social media and user-generated content platforms, educational technology and child safety applications, healthcare and mental health services, fintech and insurance support automation, gaming and voice chat services, and marketing technology.