Mistral AI, an AI startup, announced today that it has secured $640 million in Series B funding, nearly six times the amount of the largest European seed round last year, which was $113 million.
This round of funding was led by General Catalyst, with participation from Lightspeed, Andreessen Horowitz, Bpifrance, and new and existing investors including IBM, Samsung, Salesforce, Cisco, and NVIDIA. This funding has increased Mistral's valuation from $240 million in June 2023 to nearly $6 billion.
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Mistral plans to use this funding to increase computational power, expand its team, accelerate its international commercialization process, and better compete with global AI competitors like OpenAI and Anthropic. OpenAI recently reached an agreement with Apple to become the first third-party LLM provider pre-installed in the new iOS18.
Since its launch last year, Mistral has gained industry attention by focusing on open-source models and releasing seed links for downloading and testing models in an unconventional way. The company introduced the Mistral7B and Mixtral8x7B "Mixture of Experts" models, followed by the Mixtral8x22B and Codestral, a 22B parameter open-weight generative AI model specifically designed for coding tasks. Additionally, Mistral launched the Mistral Large, a proprietary model that ranks behind GPT-4 and has native proficiency in five languages with a 32K token context window.
Mistral claims that its open-source models in public repositories have been downloaded over 27 million times and are widely used in complex applications such as finance, technology, and public sectors. In the past six months, Mistral has partnered with and received investments from several tech giants and emerging companies including Microsoft, Snowflake, Databricks, Capgemini, IBM, and Perplexity.
The capital-intensive generative AI market was worth only $40 billion in 2022 and is expected to grow to $1.3 trillion over the next decade at a compound annual growth rate of 42%. Mistral's latest funding will accelerate its commercialization process, adopt more distribution channels, and accelerate the application of its models in different markets.