Mistral 7B
Mistral 7B is a 7.3-billion-parameter language model that outperforms larger models like Llama 2 13B across various benchmarks. It employs Grouped-Query Attention (GQA) for faster inference and Sliding Window Attention (SWA) to efficiently handle longer sequences. Released under the Apache 2.0 license, Mistral 7B is accessible for deployment across diverse platforms, including local environments and major cloud services. Additionally, a fine-tuned version, Mistral 7B Instruct, demonstrates enhanced performance in instruction-following tasks, surpassing models like Llama 2 13B Chat.
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Kimi K2
Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.
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Mistral Small 3.1
Mistral Small 3.1 is a state-of-the-art, multimodal, and multilingual AI model released under the Apache 2.0 license. Building upon Mistral Small 3, this enhanced version offers improved text performance, and advanced multimodal understanding, and supports an expanded context window of up to 128,000 tokens. It outperforms comparable models like Gemma 3 and GPT-4o Mini, delivering inference speeds of 150 tokens per second. Designed for versatility, Mistral Small 3.1 excels in tasks such as instruction following, conversational assistance, image understanding, and function calling, making it suitable for both enterprise and consumer-grade AI applications. Its lightweight architecture allows it to run efficiently on a single RTX 4090 or a Mac with 32GB RAM, facilitating on-device deployments. It is available for download on Hugging Face, accessible via Mistral AI's developer playground, and integrated into platforms like Google Cloud Vertex AI, with availability on NVIDIA NIM and
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Voxtral
Voxtral models are frontier open source speech‑understanding systems available in two sizes—a 24 B variant for production‑scale applications and a 3 B variant for local and edge deployments, both released under the Apache 2.0 license. They combine high‑accuracy transcription with native semantic understanding, supporting long‑form context (up to 32 K tokens), built‑in Q&A and structured summarization, automatic language detection across major languages, and direct function‑calling to trigger backend workflows from voice. Retaining the text capabilities of their Mistral Small 3.1 backbone, Voxtral handles audio up to 30 minutes for transcription or 40 minutes for understanding and outperforms leading open source and proprietary models on benchmarks such as LibriSpeech, Mozilla Common Voice, and FLEURS. Accessible via download on Hugging Face, API endpoint, or private on‑premises deployment, Voxtral also offers domain‑specific fine‑tuning and advanced enterprise features.
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