Cohere

Cohere

Provides NLP tools and LLMs via API

About Cohere

Simplify's Rating
Why Cohere is rated
A
Rated A on Competitive Edge
Rated A on Growth Potential
Rated A on Differentiation

Industries

AI & Machine Learning

Company Size

501-1,000

Company Stage

Series D

Total Funding

$940M

Headquarters

Toronto, Canada

Founded

2019

Overview

Cohere provides advanced Natural Language Processing (NLP) tools and Large Language Models (LLMs) through a user-friendly API. Their services cater to a wide range of clients, helping businesses improve content generation, summarization, and search functionalities. Cohere's business model focuses on offering scalable and affordable generative AI tools, generating revenue by granting API access to pre-trained models that can perform various tasks such as text classification, sentiment analysis, and semantic search in multiple languages. The platform is customizable, enabling businesses to create tailored solutions, and its multilingual support allows for effective use in international contexts.

Simplify Jobs

Simplify's Take

What believers are saying

  • Cohere's multilingual support aligns with rising demand in Asia, boosting expansion opportunities.
  • Dell's integration of Cohere models indicates a trend towards on-premises AI solutions.
  • Cohere's acquisition of Ottogrid enhances private AI deployments in finance and healthcare.

What critics are saying

  • Mistral's Codestral Embed model outperforms Cohere in code embedding, posing a competitive threat.
  • Cohere missed its 2024 revenue forecasts by 85%, affecting investor confidence.
  • The uncertain copyright case outcome could impact Cohere's operations and legal standing.

What makes Cohere unique

  • Cohere offers multilingual NLP tools, breaking language barriers for global applications.
  • The platform provides customizable AI solutions, enhancing content generation and semantic search.
  • Cohere's API access to pre-trained models supports diverse tasks like sentiment analysis and text classification.

Help us improve and share your feedback! Did you find this helpful?

Funding

Total Funding

$940M

Above

Industry Average

Funded Over

5 Rounds

Notable Investors:
Series D funding is typically for companies that are already well-established but need more funding to continue their growth. This round is often used to stabilize the company or prepare for an IPO.
Series D Funding Comparison
Above Average

Industry standards

$77M
$70M
Twilio
$80M
Handshake
$100M
Affirm
$500M
Cohere

Growth & Insights and Company News

Headcount

6 month growth

↑ 0%

1 year growth

↑ 4%

2 year growth

↑ 6%
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BetaKit
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VentureBeat
May 28th, 2025
Mistral Launches New Code Embedding Model That Outperforms Openai And Cohere In Real-World Retrieval Tasks

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreWith demand for enterprise retrieval augmented generation (RAG) on the rise, the opportunity is ripe for model providers to offer their take on embedding models.French AI company Mistral threw its hat into the ring with Codestral Embed, its first embedding model, which it said outperforms existing embedding models on benchmarks like SWE-Bench.The model specializes in code and “performs especially well for retrieval use cases on real-world code data.” The model is available to developers for $0.15 per million tokens.The company said the Codestral Embed “significantly outperforms leading code embedders” like Voyage Code 3, Cohere Embed v4.0 and OpenAI’s embedding model, Text Embedding 3 Large.Super excited to announce @MistralAI Codestral Embed, our first embedding model specialized for code.It performs especially well for retrieval use cases on real-world code data. pic.twitter.com/ET321cRNli — Sophia Yang, Ph.D. (@sophiamyang) May 28, 2025Codestral Embed, part of Mistral’s Codestral family of coding models, can make embeddings that transform code and data into numerical representations for RAG.“Codestral Embed can output embeddings with different dimensions and precisions, and the figure below illustrates the trade-offs between retrieval quality and storage costs,” Mistral said in a blog post. “Codestral Embed with dimension 256 and int8 precision still performs better than any model from our competitors. The dimensions of our embeddings are ordered by relevance

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