Command R+
Command R+ is Cohere’s flagship RAG-optimised model, designed for enterprise retrieval workloads with strong tool use and multilingual support.
Released
April 4, 2024
Type
llm
License
open-weight
Context
128,000 tokens
Pricing
Input
$2.50 / 1M tokens
Output
$10.00 / 1M tokens
Capabilities
Architecture
Parameters: 104B
Links
Command R+ in the news
MarkTechPost · Sep 11, 2026
Cohere Releases North Small Translate: A 218B MoE Translation Model That Scores 83.6 on WMT26 Across 50 Languages
Cohere has released North Small Translate, an open-weight machine translation model developed by Cohere and Cohere Labs. This model is a sparse Mixture-of-Experts (MoE) architecture, featuring a total of 218 billion parameters, with 25 billion active parameters per token. North Small Translate supports translation across 50 languages, ranging from Albanian to Vietnamese. In evaluations conducted by Cohere on the WMT26 benchmark, the model achieved an average score of 83.6 across all supported languages. Cohere claims this performance surpasses that of commercial translation services like DeepL and Google Translate, as well as other open-source models such as GLM 5.2 and Mistral Large 3. The model is accessible through various deployment options: it can be used freely via Cohere's API up to specified rate limits, self-hosted for non-commercial use, or licensed for commercial applications. The development of North Small Translate represents a return to the foundational problem of machine translation, which was a key focus of the original Transformer architecture introduced by Google researchers in 2017 with the paper "Attention Is All You Need." The Transformer's initial breakthroughs were demonstrated on WMT 2014 English-to-German and English-to-French translation tasks. Cohere frames the release of this dedicated translation model as a matter of digital sovereignty, asserting that organizations unable to communicate globally may struggle to maintain their sovereignty. North Small Translate is the inaugural model within Cohere's "North" family of models and follows previous multilingual efforts such as Tiny Aya and Command A Translate. The model's real-world translation quality was shaped through collaboration with RWS, an organization whose Language Weaver scientists and language experts contributed to its development. Architecturally, North Small Translate is a decoder-only sparse MoE Transformer. It incorporates 128 experts, with 8 experts activated per token, alongside shared experts applied universally. The routing mechanism employs a sigmoid function over expert logits, normalized across the selected top-k experts. Its attention mechanism includes interleaved sliding-window layers (with a window size of 4096 and RoPE embeddings) and global layers that do not utilize positional embeddings, in a 3:1 ratio. This specific attention configuration was previously introduced in Cohere's Command A model. The model supports an input and output context window of 16,000 tokens, exclusively for text. It underwent post-training specifically to enhance translation quality. Approximately 11.5% of the model's weights are active for each token processed, and the per-token compute load corresponds to the 25 billion active parameters, although the full 218 billion parameters must be held in memory.
MarkTechPost · Aug 27, 2026
Cohere Releases Parse 5 (parse-v5.0): A 2.3B Vision Language Model That Turns Enterprise Documents Into Markdown
Cohere has released Parse 5 (parse-v5.0), a 2.3 billion-parameter vision language model specifically engineered for high-volume enterprise document ingestion. This model is built upon Cohere Labs’ North-Micro-Vision-Instruct architecture and features an 8,192-token context window with a footprint of approximately 4.6 gigabytes. Parse 5 is designed to process PDF, PPT, or JPEG pages, which are submitted as base64-encoded data URIs. The output is structured Markdown that includes text arranged in reading order, tables rendered in HTML format, identified lists, extracted form key-value pairs, descriptions of images, and bounding box coordinates for elements within the documents. Notably, the model does not employ a separate Optical Character Recognition (OCR) stage, integrating this functionality directly. Cohere is pricing the Parse API at $1.50 per 1,000 pages. The company positions Parse 5 on a basis of price-performance rather than solely on peak accuracy. To support this claim, Cohere reports a ParseBench score of 79.2. This score, as detailed by the company, measures three out of the five dimensions of that particular benchmark. The model is available for production deployment and is generally accessible through the Cohere Parse API, Microsoft Foundry, and AWS SageMaker, as well as via single-tenant Model Vault. There is no waitlist or research license required for access. Mid-market teams already operating a Retrieval-Augmented Generation (RAG) stack can begin using the service with metered API calls and a free trial key. Larger enterprises with specific residency or air-gap requirements can opt for direct deployment through Model Vault or private installations. While seed-stage startups can also utilize Parse 5, the economic benefits become significant for those processing upwards of approximately 100,000 pages per month. Cohere is targeting document-heavy verticals such as financial services, insurance, healthcare and life sciences, the public sector, telecommunications, energy, and manufacturing. These industries commonly deal with scanned forms and dense tables, making Parse 5 a relevant tool for their operations. Potential applications for the model include RAG ingestion, intelligent document processing, streamlining claims and invoice pipelines, enhancing contract and filing search capabilities, and providing document context to AI agents. The model's architecture, North-Micro-Vision-Instruct, is a key component enabling its vision and language processing capabilities. The 8,192-token context window allows for the processing of extensive document content in a single pass, improving efficiency for complex documents. The approximately 4.6GB model size indicates a balance between performance and deployability for enterprise environments. The integrated approach, eliminating a separate OCR step, simplifies the document processing workflow and potentially reduces latency and cost for users.
MarkTechPost · Jul 23, 2026
Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared
The landscape of open Automatic Speech Recognition (ASR) models has evolved beyond a single dominant player, with several models now competing on performance metrics and licensing. In March 2026, Cohere released Transcribe, a 2B parameter model under the Apache 2.0 license, which initially led the Hugging Face Open ASR Leaderboard with an average Word Error Rate (WER) of 5.42%. This was quickly followed by IBM's Granite Speech 4.1 2B, which achieved a WER of 5.33%. More recently, ARK-ASR-3B and MOSS-Transcribe-preview-2B have posted even lower WER scores, narrowing the gap at the top of the leaderboard to less than one WER point. This intense competition means that rank alone is no longer the primary factor for users selecting an ASR model. Instead, critical considerations now include the model's license, the breadth of languages it supports, its streaming capabilities for real-time processing, and the cost per audio hour. These factors are becoming increasingly important for developers and businesses integrating ASR technology into their applications. However, a closer examination of the Open ASR Leaderboard reveals nuances in how the reported average WER is calculated. The average WER cited is not a fixed quantity, and models have been scored using different test sets. For instance, Cohere's 5.42% WER is an average across eight English test sets, including TED-LIUM. In contrast, ARK-ASR-3B's 5.04% is an average across seven sets, excluding TED-LIUM. The MOSS-Transcribe-preview-2B card explicitly states that TED-LIUM is not part of its current leaderboard run. Since TED-LIUM is considered one of the easier test sets, its exclusion can artificially inflate the reported average WER. When Cohere's published per-dataset scores are recomputed using the same seven test sets that ARK reports, Cohere's average WER increases to 5.84% from the headline 5.42%. Similarly, IBM's Granite Speech 4.1 2B moves from 5.33% to 5.65% when evaluated on the same basis. This suggests that, on a like-for-like comparison, ARK's lead over other models might be larger than initially indicated by the headline leaderboard figures. The focus is shifting towards transparent and consistent evaluation methodologies to accurately compare these rapidly advancing open ASR models.
Hugging Face · Jul 16, 2026
NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
NVIDIA's Nemotron 3 Embed model achieved the top overall score on the Retrieval-Augmented Generation (RAG) Evaluation Benchmark (RTEB) this week, marking a significant advancement in agentic retrieval capabilities. The model outperformed previous leaders, including models from Cohere and Google, across the benchmark's diverse set of tasks designed to assess retrieval-augmented generation performance. The RTEB, developed by researchers at Peking University and the National University of Singapore, evaluates models on their ability to retrieve relevant information and integrate it into generated text. Nemotron 3 Embed's success demonstrates its enhanced capacity for understanding context and accurately sourcing information, crucial for building more capable AI agents. This benchmark is critical for the development of AI systems that can reliably access and utilize external knowledge bases. Nemotron 3 Embed's superior performance on RTEB suggests a leap forward in the development of AI agents that can perform complex tasks requiring both information retrieval and sophisticated language generation. The benchmark's comprehensive evaluation covers various aspects of RAG, including retrieval accuracy, relevance, and the seamless integration of retrieved information into coherent and contextually appropriate responses. NVIDIA's achievement highlights the growing importance of specialized embedding models in powering advanced AI applications. This ranking underscores the competitive landscape of AI model development, with Nemotron 3 Embed now setting a new standard for retrieval-augmented generation. The benchmark's results are expected to influence future research and development in the field, pushing for more robust and reliable AI systems. The ongoing evolution of benchmarks like RTEB is essential for tracking progress and identifying leading technologies in the rapidly advancing domain of artificial intelligence.
CoinTelegraph · Jun 18, 2026
HIVE secures $220M AI infrastructure contract with Bell and Cohere
HIVE Blockchain Technologies Ltd. secured a significant AI infrastructure contract valued at $220 million with Bell Canada and Cohere on May 28, 2024. This agreement is projected to contribute approximately $70 million in annual recurring revenue for HIVE as it expands its artificial intelligence-focused operations. The contract involves HIVE providing cloud computing infrastructure to support the AI development and deployment needs of both Bell and Cohere. Bell Canada, a major telecommunications company, is leveraging advanced AI for its services, while Cohere, an AI company, focuses on developing large language models. The partnership underscores the growing demand for specialized AI infrastructure and HIVE's strategic positioning to meet this demand. The company's investment in high-performance computing is expected to accelerate its growth in the AI sector.