7Mistral AI News·
Mistral releases Robostral Navigate: 8B single-camera robot navigation model with SOTA R2R-CE results
Mistral AI has released Robostral Navigate, an 8B vision-language model enabling autonomous robot navigation using only a single RGB camera and no depth sensors or LiDAR. The model achieves 76.6% success rate on the R2R-CE validation unseen benchmark, outperforming the best single-camera approach by 9.7 points and multi-sensor systems by 4.5 points. Built entirely in-house with simulation-generated data (~400K trajectories across 6,000 scenes), it uses a novel prefix-caching training algorithm that reduces training tokens by 22× and online reinforcement learning via CISPO for further gains. The model generalizes across wheeled, legged, and flying robots and represents Mistral's entry into embodied AI.
Related guides (3)
Related events (8)
Mistral releases Robostral Navigate, a robotics navigation model
Mistral AI has released Robostral Navigate, a model targeting robotics navigation tasks and claiming state-of-the-art performance in that domain. This represents Mistral's entry into embodied AI and robotics, a significant expansion beyond their core language model work. The announcement is drawing notable community attention on Hacker News with 366 points and 87 comments.
Pixtral Large: Mistral AI's 124B Open-Weights Multimodal Model
Mistral AI released Pixtral Large, a 124B open-weights multimodal model built on Mistral Large 2, featuring a 1B parameter vision encoder and 128K context window supporting at least 30 high-resolution images. The model claims state-of-the-art results on MathVista, DocVQA, and ChartQA, outperforming GPT-4o and Gemini-1.5 Pro on several benchmarks, and leads the LMSys Vision Leaderboard among open-weights models by ~50 ELO points. Simultaneously, Mistral updated its text model to Mistral Large 24.11 with improvements in long-context understanding, function calling, and RAG/agentic workflows. Note: the model has since been deprecated and replaced by newer Mistral vision models.
Mistral AI Releases Mathstral 7B: Math-Specialized Model with SOTA Reasoning in Size Category
Mistral AI has released Mathstral 7B, a math and STEM-specialized model built on Mistral 7B, developed in collaboration with Project Numina. The model achieves 56.6% on MATH and 63.47% on MMLU in standard evaluation, improving to 74.59% on MATH with a reward model over 64 candidates using inference-time compute scaling. Weights are open on HuggingFace and compatible with mistral-inference and mistral-finetune tooling.
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 AI Releases Devstral: Apache 2.0 Agentic Coding Model with SWE-Bench SOTA
Mistral AI, in collaboration with All Hands AI, releases Devstral, an agentic LLM specialized for software engineering tasks under the Apache 2.0 license. The model achieves 46.8% on SWE-Bench Verified, surpassing prior open-source state-of-the-art by over 6 percentage points and outperforming larger models like DeepSeek-V3-0324 (671B) and Qwen3 232B-A22B under the same OpenHands scaffold. Devstral is small enough to run on a single RTX 4090 or a Mac with 32GB RAM, and is available via Mistral's API at $0.1/M input tokens, as well as on HuggingFace, Ollama, and other platforms. Mistral indicates a larger agentic coding model is in development.
Mistral releases OCR 4 with bounding boxes, block classification, and SOTA benchmark scores
Mistral AI released OCR 4, a document intelligence model supporting 170 languages across 10 language groups, with structured output including bounding boxes, typed-block classification, and inline confidence scores. The model claims top scores on OlmOCRBench (85.20) and wins 72% of head-to-head human preference evaluations against competing OCR and document-AI systems. It is deployable in a single container for self-hosted, data-sovereign environments and is priced at $4 per 1,000 pages via API. OCR 4 is integrated with Mistral's open-source Search Toolkit as an ingestion component for RAG and enterprise search pipelines.
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 Launches Medium 3.5 (128B Open Weights), Remote Cloud Coding Agents in Vibe, and Work Mode in Le Chat
Mistral AI has released Mistral Medium 3.5, a 128B dense open-weights model with a 256k context window, configurable reasoning effort, and a vision encoder trained from scratch, scoring 77.6% on SWE-Bench Verified. Alongside the model, Mistral is launching remote cloud-based coding agents in its Vibe CLI and Le Chat interface, enabling async parallel coding sessions that run independently and notify users on completion. A new Work mode in Le Chat provides a multi-step agentic interface for cross-tool workflows including email, calendar, research, and issue tracking. Mistral Medium 3.5 replaces Devstral 2 as the default model in both Le Chat and the Vibe CLI, and is available for self-hosting on as few as four GPUs under a modified MIT license.