Apache 2.0
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OpenAI Releases gpt-oss-120b and gpt-oss-20b Open-Weight Models Under Apache 2.0
OpenAI is releasing two open-weight language models, gpt-oss-120b and gpt-oss-20b, under the Apache 2.0 license. The models are claimed to outperform similarly sized open models on reasoning tasks and feature strong tool use capabilities. They are optimized for efficient deployment on consumer hardware, positioning them as cost-effective alternatives in the open-weights ecosystem.
Mistral Releases Mistral 3 Family: Mistral Large 3 (675B MoE) and Ministral 3 Series (3B–14B), All Apache 2.0
Mistral AI has announced Mistral 3, a family of open-weight models including Mistral Large 3 (41B active / 675B total sparse MoE) and three dense Ministral 3 edge models (3B, 8B, 14B), all released under Apache 2.0. Mistral Large 3 debuts at #2 on LMArena's OSS non-reasoning leaderboard, supports image understanding, and was trained on 3,000 NVIDIA H200 GPUs; a reasoning variant is forthcoming. The Ministral 3 series includes base, instruct, and reasoning variants with multimodal and multilingual capabilities, with the 14B reasoning model achieving 85% on AIME '25. The release involves deep co-optimization with NVIDIA (Blackwell/Hopper kernels, NVFP4 format), vLLM, and Red Hat, and is available across major cloud and inference platforms.
OpenAI Releases gpt-oss-120b and gpt-oss-20b Open-Weight Reasoning Models
OpenAI has published model cards for gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models released under the Apache 2.0 license alongside a dedicated gpt-oss usage policy. This marks a significant move by OpenAI into the open-weights space, offering both a large 120B parameter model and a smaller 20B variant. The release signals a strategic shift for OpenAI, which has historically kept its frontier models proprietary.
Mistral AI Founding Manifesto and Mistral 7B Release
Mistral AI published its founding mission statement alongside the release of Mistral 7B, a 7-billion-parameter open-weights language model released under Apache 2.0. The model claims to outperform all available open models up to 13B parameters on standard English and code benchmarks, produced in three months from a standing start. The post articulates Mistral's strategic thesis: open-weight models will outcompete proprietary black-box APIs for most enterprise use cases, drawing analogies to Linux, WebKit, and Kubernetes. The company signals intent to release progressively larger frontier models while building a commercial offering around on-premise and VPC deployment.
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 Releases Mixtral 8x22B Under Apache 2.0
Mistral AI has released Mixtral 8x22B, a sparse Mixture-of-Experts model with 141B total parameters but only 39B active parameters, under the permissive Apache 2.0 license. The model features a 64K token context window, native function calling, multilingual support across five European languages, and strong math and coding performance. Mistral claims it outperforms all other open-weight models on standard benchmarks while being faster than dense 70B models due to sparse activation. An instructed version achieves 90.8% on GSM8K maj@8.
Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context
IBM released Granite Embedding Multilingual R2, an open-weights (Apache 2.0) multilingual embedding model with 32K context window, claiming best-in-class retrieval quality among sub-100M parameter models. The model is positioned for enterprise RAG and retrieval use cases across multiple languages. It is hosted and announced via Hugging Face.
Qwen3 Embedding: State-of-the-Art Text Embedding and Reranking Models Released
Alibaba's Qwen team has released the Qwen3 Embedding series, a set of open-weights text embedding and reranking models built on the Qwen3 foundation model. The models are designed for retrieval and reranking tasks and claim state-of-the-art performance across multiple benchmarks. They are released under the Apache 2.0 license and are available on Hugging Face and ModelScope.
Qwen2.5-VL-32B: Reinforcement-Learning-Optimized Vision-Language Model Released
Alibaba's Qwen team has released Qwen2.5-VL-32B-Instruct, a 32-billion-parameter vision-language model built on the Qwen2.5-VL series and further optimized with reinforcement learning. The model is open-sourced under the Apache 2.0 license and available on Hugging Face and ModelScope. It follows the January 2025 launch of the broader Qwen2.5-VL series, positioning the 32B scale as a balance between capability and deployment practicality.
Mistral Releases Voxtral Transcribe 2: State-of-the-Art Speech-to-Text with Sub-200ms Realtime Model
Mistral AI has released Voxtral Transcribe 2, a family of two speech-to-text models: Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for live applications. Voxtral Realtime features a novel streaming architecture with configurable latency down to sub-200ms, a 4B parameter footprint suitable for edge deployment, and is released as open weights under Apache 2.0. Voxtral Mini Transcribe V2 claims state-of-the-art word error rate on FLEURS at $0.003/min, outperforming GPT-4o mini Transcribe, Gemini 2.5 Flash, AssemblyAI, and Deepgram Nova on accuracy benchmarks. Both models support 13 languages with speaker diarization, word-level timestamps, and context biasing.
Data Points: NeurIPS-China Standoff, Anthropic Emotion Vectors, Gemma 4, Cursor 3, Microsoft MAI Models
This edition of The Batch covers five significant AI developments: NeurIPS reversed a sanctions-related submission policy after China's largest tech federation announced a boycott; Anthropic's interpretability team identified 171 emotion-related representations in Claude Sonnet 4.5 that causally influence model behavior including unsafe actions; Google released Gemma 4, a family of Apache 2.0-licensed open-weights models up to 31B parameters with strong benchmark performance; Cursor released version 3 with a redesigned multi-agent interface; and Microsoft announced three specialized MAI models for transcription, voice synthesis, and image generation. The NeurIPS incident highlights growing friction in international AI research access, while the Anthropic findings have direct implications for AI safety and interpretability research.
Codestral Mamba: Mistral AI Releases Apache 2.0 Mamba-Architecture Code Model
Mistral AI has released Codestral Mamba, a 7.3B-parameter code-focused language model built on the Mamba state-space architecture rather than the Transformer architecture. The model offers linear-time inference and theoretically infinite sequence length, tested up to 256k tokens in-context retrieval. Developed with Mamba co-creators Albert Gu and Tri Dao, it is released under Apache 2.0 and available via HuggingFace, mistral-inference SDK, TensorRT-LLM, and Mistral's la Plateforme API. Mistral positions it as a local code assistant that performs on par with state-of-the-art transformer-based code models.
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 AI Introduces Non-Production License (MNPL) for Codestral and Future Models
Mistral AI has announced the Mistral AI Non-Production License (MNPL), a new licensing framework that permits non-commercial and research use but restricts commercial deployment without a separate agreement. The license debuts with Codestral, Mistral's code-focused model released the same day. Mistral frames this as balancing openness principles with commercial sustainability, while affirming continued Apache 2.0 releases for a separate product family. The move signals a bifurcated licensing strategy as Mistral seeks to monetize high-capability models while maintaining a developer-friendly reputation.
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 AI Releases Mistral Small v24.09, Free API Tier, and Pixtral 12B Vision on le Chat with Broad Price Cuts
Mistral AI announced a multi-part release on September 17, 2024: a free tier for la Plateforme API, significant price reductions across its model family (up to 80% for Mistral Small and Codestral), an updated Mistral Small v24.09 (22B parameters, improved alignment and reasoning), and the availability of Pixtral 12B vision capabilities on le Chat. Pixtral 12B, released under Apache 2.0, supports images of any size without text performance degradation and is now accessible for free on le Chat. The pricing updates also apply to cloud partner deployments on Azure AI Studio, Amazon Bedrock, and Google Vertex AI.
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.
Mistral NeMo: 12B Open-Weights Model with 128k Context, Built with NVIDIA
Mistral AI and NVIDIA jointly release Mistral NeMo, a 12B parameter model under Apache 2.0 license featuring a 128k token context window and a new tokenizer called Tekken based on Tiktoken. The model is designed as a drop-in replacement for Mistral 7B, supports multilingual applications across 11+ languages, and was trained with quantization awareness enabling FP8 inference without performance loss. Benchmark comparisons show competitive performance against Gemma 2 9B and Llama 3 8B. Weights are available on HuggingFace and the model is also packaged as an NVIDIA NIM inference microservice.
Pixtral 12B: Mistral AI's First Multimodal Model (Now Deprecated)
Mistral AI released Pixtral 12B in September 2024 as their first natively multimodal model, combining a new 400M parameter vision encoder trained from scratch with a 12B multimodal decoder based on Mistral Nemo. The model supports variable image sizes and aspect ratios, a 128K token context window for multiple images, and achieved 52.5% on MMMU while maintaining strong text-only benchmark performance. The model is now deprecated and has been replaced by newer vision and multimodal models from Mistral. It was released under Apache 2.0 license.