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Latest Cohere AI news and updates. Model releases, announcements, benchmarks, and developments. Updated daily.
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Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0
Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions
[AINews] Everything is CLI
a quiet day lets us reflect on the growing trend of CLIs for ~everything~ agents
Cohere launches an open-source voice model specifically for transcription
Relatively light at just 2 billion parameters, the model is meant for use with consumer-grade GPUs for those who want to self-host it. It currently supports 14 languages.
Cohere launches a family of open multilingual models
Cohere's Tiny Aya models support over 70 langauges
Cohere’s $240M year sets stage for IPO
Cohere surpassed $240 million in annual recurring revenue in 2025, highlighting strong enterprise AI demand as the Canadian startup positions itself for a potential IPO amid intensifying competition from OpenAI and Anthropic.
[AINews] Qwen Image 2 and Seedance 2
Strong generative media showings from China
Chart your AI and agent strategy with Microsoft Marketplace
A new category of organization is emerging that embeds AI across every layer of their operations—accelerating delivery, scaling efficiently, and unlocking new business potential. The post Chart your AI and agent strategy with Microsoft Marketplace appeared first on Microsoft Azure Blog .
[State of Evals] LMArena's $1.7B Vision — Anastasios Angelopoulos, LMArena
We are reupping this episode after LMArena announced their fresh Series A (https://www.theinformation.com/articles/ai-evaluation-startup-lmarena-valued-1-7-billion-new-funding-round?rc=luxwz4), raising $150m at a $1.7B valuation, with $30M annualized consumption revenue (aka $2.5m MRR) after their September evals product launch.
Move Beyond Chain-of-Thought with Chain-of-Draft on Amazon Bedrock
This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations.
Mistral: Mistral Medium 3.1 (mistralai/mistral-medium-3.1)
Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Mistral: Mistral Medium 3 (mistralai/mistral-medium-3)
Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.
Cohere: Command A (cohere/command-a)
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary and open-weights models Command A delivers maximum performance with minimum hardware costs, excelling on business-critical agentic and multilingual tasks.
Cohere: Command R7B (12-2024) (cohere/command-r7b-12-2024)
Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps. Use of this model is subject to Cohere's Usage Policy and SaaS Agreement .
Cohere: Command R+ (08-2024) (cohere/command-r-plus-08-2024)
command-r-plus-08-2024 is an update of the Command R+ with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same. Read the launch post here . Use of this model is subject to Cohere's Usage Policy and SaaS Agreement .
Cohere: Command R (08-2024) (cohere/command-r-08-2024)
command-r-08-2024 is an update of the Command R with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model. Read the launch post here . Use of this model is subject to Cohere's Usage Policy and SaaS Agreement .
Best practices for deploying language models
Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
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