Cohere Release Notes

We're pleased to announce the release of Embed 5, Cohere's most powerful embeddings family yet.

Embed 5 delivers frontier retrieval quality on complex enterprise data, with major gains over Embed 4 on visually rich documents, financial filings, parsed PDFs, code, and multilingual retrieval.

Key features

• Two model variants available:
  • embed-v5.0-pro: Optimized for the highest retrieval quality, particularly for offline indexing and quality-critical retrieval
  • embed-v5.0-fast: Optimized for low latency and high throughput, particularly for interactive search, agent loops, and high-volume query traffic
• Shared embedding space: Pro and Fast share an embedding space, so a corpus indexed with one model can be queried with the other. We recommend indexing with Pro and querying with Fast.
• Multimodal inputs: Embed text, images, and mixed text-and-image inputs (e.g. PDF pages) in a single vector
• Multilingual support: Supports over 100 languages
• Extended context length: 128k token context window
• Flexible storage: Matryoshka embeddings in the following dimensions: [256, 512, 768, 1024, 1536, 2048], with float, int8, and binary output types

Availability …

We're pleased to announce the release of North Small Translate, an open-weights mixture-of-experts model purpose-built for machine translation across more than 50 languages.

North Small Translate is designed to give researchers, developers, and enterprises flexible ways to evaluate and deploy machine translation while retaining control over their data and infrastructure.

Key features

• Purpose-built translation: Optimized for machine translation across more than 50 languages and locale variants.
• Efficient MoE architecture: 218 billion total parameters with 25 billion active parameters.
• Flexible deployment: Available through the free-tier Chat V2 API and as open weights in W4A16, FP8, and BF16 for non-commercial use.
• Private deployment: Suggested deployment hardware by quantization format:
  • W4A16: Two H100s or one B200
  • FP8: Four H100s or two B200s
  • BF16: Eight H100s or four B200s

Technical details

• Model name: north-small-translate-1-0
• Context length: 16K
• License: Creative Commons Attribution-NonCommercial 4.0
• Open-weights formats: W4A16, FP8, and BF16

Availability …

Today we are releasing Cohere Parse.

Parse (model ID: parse-v5.0) turns complex documents into clean, structured Markdown ready for downstream AI workflows. The 2.3B-parameter multimodal model extracts text in reading order, tables, lists, forms, images and captions, page boundaries, and visual element locations.

Outputs include Markdown/HTML content, HTML-formatted tables, bounding boxes, and image descriptions — preserving both document structure and layout for easier rendering and processing.

Key specs: 8K context window · ~4.6GB model size · Markdown output

Availability

Cohere Parse is available through the Parse API, as well as Microsoft Foundry and AWS SageMaker. For single-tenant deployment, Parse is also available in Model Vault.

For more details, see the model documentation.

Today we are releasing Cohere Transcribe Arabic.

This open-source speech-to-text model is a fine-tune of Cohere Transcribe using Arabic speech data. It lets Arabic speakers transcribe their voice with unmatched accuracy and support for regional dialects or speech patterns.

It is currently the most accurate open-source Arabic ASR model available today and is optimized for production inference and throughput.

Technical Details

• Model Name: cohere-transcribe-arabic-07-2026
• Size: 2B
• Architecture: conformer-based encoder-decoder
• Languages supported: Arabic (all major dialects), English (including English spoken with an Arabic accent)
• License: Apache 2.0

Availability

Cohere Transcribe Arabic is available through the V2 Audio Transcriptions API and as open weights on Hugging Face. For production use, Model Vault deployment is also supported.

For more details, see the model documentation.

We're pleased to announce the release of North Mini Code, Cohere's first agentic coding model. It is a 30 billion total / 3 billion active parameter Mixture of Experts model trained specifically for agentic coding, with a small enough active footprint to run on local hardware.

Technical Details

• Model Name: north-mini-code-1-0
• Context Length: 256K input, 64K output
• License: Apache 2.0

Availability

North Mini Code is available through the Chat V2 API and as open weights on Hugging Face. For production use, Model Vault deployment is also supported.

For more details, see the model documentation.

We're pleased to announce the release of Command A+, the last model in the Command A family of models, combining support for vision inputs, reasoning capabilities, translation capabilities, and agentic tasks all within the same model. It is also notably our first Mixture of Experts (MoE) model with 25 billion active parameters ands 218 billion total parameters.

Key Features

• Agentic Applications: With notable performance increases in tool use and agentic tasks, Command A+ is the strongest agentic model in the Command family.
• Expanded Multilingual Support: With 48 languages supported, including all official EU languages, this more than doubles the support of languages from our prior models.
• Efficient & Fast: With as few as 1 x B200 or 2 x H100s required to deploy the model, and up to 110% throughput increase and 30% decrease in latency over Command A Reasoning, the model is designed for production-grade deployments.

Technical Details

• Model Name: command-a-plus-05-2026
• Context Length: 128K input, 64K output …

Retirement notice

Effective April 4, 2026, the following models are no longer available. Requests using these model IDs will fail.

Retired models:

• embed-english-v2.0
• embed-english-light-v2.0
• embed-multilingual-v2.0
• c4ai-aya-expanse-8b
• c4ai-aya-vision-8b

We recommend these replacements:

Embedding tasks

• embed-english-v3.0
• embed-multilingual-v3.0
• embed-v4.0

Chat tasks

• command-r7b-12-2024
• command-a-03-2025
• command-a-reasoning-08-2025

For the full announcement and lifecycle context, see the Deprecations page. For questions or assistance, contact support@cohere.com.

We're pleased to announce the release of Cohere Transcribe, our first transcription model. Cohere Transcribe specializes in audio-in, text-out, automatic speech recognition (ASR).

Technical details

• Model name: cohere-transcribe-03-2026
• Input: Audio waveform
• Output: Text
• Languages covered: English, German, French, Italian, Spanish, Portuguese, Greek, Dutch, Polish, Vietnamese, Chinese, Arabic, Japanese, Korean.
• License: Apache 2.0
• API endpoint: Audio Transcriptions API

Getting started

The model is available immediately through Cohere's Audio Transcriptions API endpoint. You can start transcribing audio using the following example query:

import cohere

co = cohere.ClientV2()

response = co.audio.transcriptions.create(
    model="cohere-transcribe-03-2026",
    language="en",
    file=open("./sample.wav", "rb"),
)

print(response)

Availability

You can access Cohere Transcribe via our API for free, low-setup experimentation subject to rate limits. See the Different Types of API Keys and Rate Limits page for usage details and integration guidance. …

We're pleased to announce the release of Rerank 4.0 our newest and most performant foundational model for ranking.

Technical Details

• Two model variants available:
  • rerank-v4.0-pro: Optimized for state-of-the-art quality and complex use-cases
  • rerank-v4.0-fast: Optimized for low latency and high throughput use-cases
• Multilingual support: Re-rank both English and non-English documents
• Semi-structured data support: Re-rank JSON documents
• Extended context length: 32k token context window

Example Query

import cohere

co = cohere.ClientV2()

query = "What is the capital of the United States?"
docs = [
    "Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274.",
    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that are a political division controlled by the United States. Its capital is Saipan.",
    "Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas.", …

As part of our ongoing commitment to delivering advanced AI solutions, we are deprecating the following models, features, and API endpoints:

Deprecated Models:

• command-r-03-2024 (and the alias command-r)
• command-r-plus-04-2024 (and the alias command-r-plus)
• command-light
• command
• summarize (Refer to the migration guide for alternatives).

For command model replacements, we recommend you use command-r-08-2024, command-r-plus-08-2024, or command-a-03-2025 (which is the strongest-performing model across domains) instead.

Retired Fine-Tuning Capabilities: All fine-tuning options via dashboard and API for models including command-light, command, command-r, classify, and rerank are being retired. Previously fine-tuned models will no longer be accessible.

Deprecated Features and API Endpoints:

• /v1/connectors (Managed connectors for RAG)
• /v1/chat parameters: connectors, search_queries_only
• /v1/generate (Legacy generative endpoint)
• /v1/summarize (Legacy summarization endpoint)
• /v1/classify
• Slack App integration
• Coral Web UI (chat.cohere.com and coral.cohere.com)

For questions, reach out to support@cohere.com

We're excited to announce the release of Command A Translate, Cohere's first machine translation model. It achieves state-of-the-art performance at producing accurate, fluent translations across 23 languages.

Key Features

• 23 supported languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Chinese, Arabic, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian
• 111 billion parameters for superior translation quality
• 16K token context length (8K input + 8K output) for handling longer texts
• Optimized for deployment on 1-2 GPUs (A100s/H100s)
• Secure deployment options for sensitive data translation

Getting Started

The model is available immediately through Cohere's Chat API endpoint. You can start translating text with simple prompts or integrate it programmatically into your applications.

from cohere import ClientV2

co = ClientV2(api_key="<YOUR API KEY>")

response = co.chat(
    model="command-a-translate-08-2025",
    messages=[
        {
            "role": "user",
            "content": "Translate this text to Spanish: Hello, how are you?",
        }
    ],
)

Availability …

We’re excited to announce the release of Command A Reasoning, a hybrid reasoning model designed to excel at complex agentic tasks, in English and 22 other languages. With 111 billion parameters and a 256K context length, this model brings advanced reasoning capabilities to your applications through the familiar Command API interface.

Key Features

• Tool Use: Provides the strongest tool use performance out of the Command family of models.
• Agentic Applications: Demonstrates proactive problem-solving, autonomously using tools and resources to complete highly complex tasks.
• Multilingual: With 23 languages supported, the model solves reasoning and agentic problems in the language your business operates in.

Technical Specifications

• Model Name: command-a-reasoning-08-2025
• Context Length: 256K tokens
• Maximum Output: 32K tokens
• API Endpoint: Chat API

Getting Started

Integrating Command A Reasoning is straightforward using the Chat API. Here’s a non-streaming example:

Customization Options

You can enable and disable thinking capabilities using the thinking parameter, and steer the model's output with a flexible user-controlled thinking budget; for more details on token budgets, advanced configurations, and best practices, refer to our dedicated Reasoning documentation.

We're excited to announce the release of Command A Vision, Cohere's first commercial model capable of understanding and interpreting visual data alongside text. This addition to our Command family brings enterprise-grade vision capabilities to your applications with the same familiar Command API interface.

Key Features

Multimodal Capabilities

• Text + Image Processing: Combine text prompts with image inputs
• Enterprise-Focused Use Cases: Optimized for business applications like document analysis, chart interpretation, and OCR
• Multiple Languages: Officially supports English, Portuguese, Italian, French, German, and Spanish

Technical Specifications

• Model Name: command-a-vision-07-2025
• Context Length: 128K tokens
• Maximum Output: 8K tokens
• Image Support: Up to 20 images per request (or 20MB total)
• API Endpoint: Chat API

What You Can Do

Command A Vision excels in enterprise use cases including:

• 📊 Chart & Graph Analysis: Extract insights from complex visualizations
• 📋 Table Understanding: Parse and interpret data tables within images
• 📄 Document OCR: Optical character recognition with natural language processing
• 🌐 Image Processing for Multiple Languages: Handle text in images across multiple languages
• 🔍 Scene Analysis: Identify and describe objects within images

💻 Getting Started …

We are thrilled to announce that the Oracle Cloud Infrastructure (OCI) Generative AI service now supports Cohere Command A, Rerank v3.5, Embed v3.0 multimodal. This marks a major advancement in providing OCI's customers with enterprise-ready AI solutions.

Command A 03-2025 is the most performant Command model to date, delivering 150% of the throughput of its predecessor on only two GPUs.

Embed v3.0 is a cutting-edge AI search model enhanced with multimodal capabilities, allowing it to generate embeddings from both text and images.

Rerank 3.5, Cohere's newest AI search foundation model, is engineered to improve the precision of enterprise search and retrieval-augmented generation (RAG) systems across a wide range of data formats (such as lengthy documents, emails, tables, JSON, and code) and in over 100 languages.

Check out Oracle's announcement and documentation for more details.

We’re thrilled to announce the release of Embed 4, the most recent entrant into the Embed family of enterprise-focused large language models (LLMs).

Embed v4 is Cohere’s most performant search model to date, and supports the following new features:

1. Matryoshka Embeddings in the following dimensions: '[256, 512, 1024, 1536]'
2. Unified Embeddings produced from mixed modality input (i.e. a single payload of image(s) and text(s))
3. Context length of 128k

Embed v4 achieves state of the art in the following areas:

1. Text-to-text retrieval
2. Text-to-image retrieval
3. Text-to-mixed modality retrieval (from e.g. PDFs)

Embed v4 is available today on the Cohere Platform, AWS Sagemaker, and Azure AI Foundry. For more information, check out our dedicated blog post.

We're thrilled to announce the release of Command A, the most recent entrant into the Command family of enterprise-focused large language models (LLMs).

Command A is Cohere's most performant model to date, excelling at real world enterprise tasks including tool use, retrieval augmented generation (RAG), agents, and multilingual use cases. With 111B parameters and a context length of 256K, Command A boasts a considerable increase in inference-time efficiency -- 150% higher throughput compared to its predecessor Command R+ 08-2024 -- and only requires two GPUs (A100s / H100s) to run.

Command A is available today on the Cohere Platform, HuggingFace, or through the SDK with command-a-03-2025. For more information, check out our dedicated blog post.

Today, Cohere Labs, Cohere’s research arm, is proud to announce Aya Vision, a state-of-the-art multimodal large language model excelling across multiple languages and modalities. Aya Vision outperforms the leading open-weight models in critical benchmarks for language, text, and image capabilities.

Technical Details

Built as a foundation for multilingual and multimodal communication, this groundbreaking AI model supports tasks such as image captioning, visual question answering, text generation, and translations from both texts and images into coherent text.

Refer to Aya Vision for more information.

Cohere is thrilled to announce the release of Command R7B Arabic (c4ai-command-r7b-12-2024). This is an open weights release of an advanced, 8-billion parameter custom model optimized for the Arabic language (MSA dialect), in addition to English. As with Cohere's other command models, this one comes with context length of 128,000 tokens; it excels at a number of critical enterprise tasks -- instruction following, length control, retrieval-augmented generation (RAG), minimizing code-switching -- and it demonstrates excellent general purpose knowledge and understanding of the Arabic language and culture.

Try Command R7B Arabic

If you want to try Command R7B Arabic, it's very easy: you can use it through the Cohere playground or in our dedicated Hugging Face Space.

Alternatively, you can use the model in your own code. To do that, first install the transformers library from its source repository:

pip install 'git+https://github.com/huggingface/transformers.git'

Then, use this Python snippet to run a simple text-generation task with the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereForAI/c4ai-command-r7b-12-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id) …

In December 2024, Cohere released Rerank v3.5 model. It demonstrates SOTA performance on multilingual retrieval, reasoning, and tasks in domains as varied as finance, eCommerce, hospitality, project management, and email/messaging retrieval.

This model has been available through the Cohere API, but today we’re pleased to announce that it can also be utilized through Microsoft Azure's AI Foundry!

You can find more information about using Cohere’s embedding models on AI Foundry in the Cohere on the Microsoft Azure Platform section.