Mistral Small 4
mistral-small-4-7bfe37f5·10 events·first seen 1mo agoAliases: Mistral Small 4, Mistral Small 3, Mistral Small, Mistral Small 3.1, mistral-small
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Mistral Small 4: Unified Multimodal, Reasoning, and Coding MoE Model Released Under Apache 2.0
Mistral AI has released Mistral Small 4, a 119B-parameter Mixture-of-Experts model (6B active per token) that unifies capabilities previously split across Magistral (reasoning), Pixtral (multimodal), and Devstral (coding agents) into a single open-weights model. The model features a 256k context window, configurable reasoning effort via a `reasoning_effort` parameter, native text and image input support, and is released under Apache 2.0. Mistral claims 40% latency reduction and 3x throughput improvement over Mistral Small 3, with benchmark results showing competitive performance against GPT-OSS 120B and Qwen models while producing significantly shorter outputs. The release includes day-0 availability as an NVIDIA NIM and support across vLLM, llama.cpp, SGLang, and Transformers.
Mistral Small 3.1: Multimodal, 128k Context, Apache 2.0 Open-Weight Model
Mistral AI releases Mistral Small 3.1, a ~24B parameter model with multimodal understanding, 128k token context window, and claimed best-in-class performance among small models, outperforming Gemma 3 and GPT-4o Mini on text, multimodal, and multilingual benchmarks. The model runs on a single RTX 4090 or 32GB RAM Mac at 150 tokens/second and is released under Apache 2.0 license with both base and instruct checkpoints. It is available on HuggingFace, Mistral's La Plateforme API, and Google Cloud Vertex AI, with NVIDIA NIM and Azure AI Foundry support coming soon. The release targets enterprise and on-device use cases including document verification, agentic workflows, and domain fine-tuning.
Mistral Small 3: 24B Latency-Optimized Open-Weight Model Released Under Apache 2.0
Mistral AI has released Mistral Small 3, a 24B-parameter instruction-tuned model optimized for low latency, achieving over 81% on MMLU at 150 tokens/s on a single GPU. The model is competitive with Llama 3.3 70B and Qwen 32B while being more than 3x faster on equivalent hardware, and is released under Apache 2.0 for both pretrained and instruction-tuned checkpoints. It is explicitly not trained with RL or synthetic data, positioning it as a base model for community fine-tuning and reasoning capability development. Deployment targets include local inference on consumer hardware (RTX 4090, MacBook 32GB RAM), agentic function calling, and domain-specific fine-tuning.
Mistral AI joins NVIDIA Nemotron Coalition as founding member, co-developing open frontier models
Mistral AI has announced a strategic partnership with NVIDIA as a founding member of the newly formed NVIDIA Nemotron Coalition, a multi-lab initiative to advance open-source frontier foundation models. The collaboration will combine Mistral's model architectures, multimodal capabilities, and fine-tuning expertise with NVIDIA's DGX Cloud compute and synthetic data pipelines. The coalition's first deliverable is a base model trained on DGX Cloud that will underpin the upcoming NVIDIA Nemotron 4 model family, to be open-sourced. Coinciding with the announcement, Mistral is also releasing Mistral Small 4.
Mistral AI Launches Model Customization Suite: Open-Source SDK, Managed Fine-Tuning, and Custom Training
Mistral AI has introduced three tiers of model customization on la Plateforme: an open-source LoRA-based fine-tuning SDK (mistral-finetune) for self-hosted use, serverless managed fine-tuning services via API initially supporting Mistral 7B and Mistral Small, and bespoke custom training services including continuous pretraining for enterprise customers. The managed fine-tuning uses LoRA adapters and claims cost and efficiency advantages over full fine-tuning while maintaining comparable performance. This positions Mistral as a full-stack customization provider competing with OpenAI's fine-tuning API and similar offerings.
Mistral AI Releases Mistral Large, Claims Second-Best API Model After GPT-4
Mistral AI has released Mistral Large, its most capable model to date, claiming second place among API-accessible models behind GPT-4 on standard benchmarks including MMLU, HellaSwag, and coding/math evals. The model features a 32K context window, native fluency in five European languages, function calling, and constrained output mode. Simultaneously, Mistral is launching a new Mistral Small optimized for latency, restructuring its endpoint lineup, and announcing Microsoft Azure as its first major distribution partner. This marks Mistral's first significant commercial partnership and expansion beyond its own infrastructure.
Mistral AI Launches Le Chat Conversational Assistant
Mistral AI has released Le Chat, a multilingual conversational assistant built on its own models including Mistral Large, Mistral Small, and a new prototype called Mistral Next. The product serves as both a public-facing demo of Mistral's capabilities and a business offering via Le Chat Enterprise, which includes self-deployment and fine-grained moderation. The assistant is currently in beta and lacks internet access. A tunable moderation mechanism is included to flag sensitive content.
Mistral AI Releases Voxtral: Open-Weight Speech Understanding Models in 24B and 3B Sizes
Mistral AI has released Voxtral, a family of two open-weight speech understanding models (Voxtral Small at 24B and Voxtral Mini at 3B) under the Apache 2.0 license. Both models support long-form audio up to 30-40 minutes, native multilingual transcription, built-in Q&A and summarization, and function-calling directly from voice, built on the Mistral Small 3.1 language model backbone. Benchmarks show Voxtral outperforms Whisper large-v3 across all tasks and is competitive with GPT-4o mini and Gemini 2.5 Flash on audio understanding, while pricing starts at $0.001/minute via API. Models are available on Hugging Face and through Mistral's API, with a transcription-optimized variant (Voxtral Mini Transcribe) also offered.
Mixtral 8x7B: Mistral AI Releases Sparse Mixture-of-Experts Open-Weight Model
Mistral AI has released Mixtral 8x7B, a sparse mixture-of-experts (SMoE) model with 46.7B total parameters but only 12.9B active parameters per token, enabling inference speed and cost equivalent to a 12.9B model. Licensed under Apache 2.0, Mixtral outperforms Llama 2 70B on most benchmarks and matches or exceeds GPT-3.5, with support for 32k context, five European languages, and strong code generation. An instruction-tuned variant (Mixtral 8x7B Instruct) achieves 8.3 on MT-Bench, claimed best among open-source models at release. The model is deployed behind Mistral's mistral-small API endpoint and supported via vLLM with Megablocks CUDA kernels.
Mistral Saba: 24B Regional Language Model for Middle East and South Asia
Mistral AI has released Mistral Saba, a 24B parameter model specialized for Arabic and South Asian languages, with particular strength in South Indian languages such as Tamil. The model is trained on curated datasets from the Middle East and South Asia, and claims to outperform models more than 5x its size on regional tasks while running on single-GPU systems at over 150 tokens/second. It is available via API and for local on-premises deployment, targeting enterprise use cases in conversational support, domain-specific expertise, and cultural content creation. Mistral also announced a custom private model training offering for strategic enterprise customers.