AI21 Labs

AI21 Labs is a Tel Aviv company that builds large language models, the text-predicting neural networks behind chatbots and writing tools. It was founded in 2017 by Stanford computer scientist Yoav Shoham, Ori Goshen and Amnon Shashua, the Hebrew University professor who also founded Mobileye. Shoham and Goshen have served as co-CEOs and Shashua as chairman, according to Calcalist. The company is notable for two things: it was one of the first firms outside the largest US labs to offer a GPT-3-scale model to developers, and it was the first to ship a production model that combines Transformer attention layers with Mamba state-space layers.

The company's first product was Wordtune, a consumer writing assistant that suggested alternative phrasings for a sentence. VentureBeat reported in August 2021 that AI21 then had about 40 employees and $34.5 million in venture funding. That same month AI21 opened AI21 Studio, a developer platform, and released Jurassic-1. The largest version, Jurassic-1 Jumbo, had 178 billion parameters (the adjustable numbers a model learns during training), about 3 billion more than GPT-3. AI21's announcement highlighted a 250,000-token vocabulary, at least five times larger than most vocabularies of the time, which included multi-word tokens such as named entities and common phrases. A token is the unit of text a model reads and writes; packing more meaning into each token means fewer tokens per document, which lowers latency and cost. VentureBeat reported the Jumbo model was trained on 300 billion tokens of English web text, including Wikipedia, news, StackExchange and OpenSubtitles.

Funding followed the generative AI boom. A Series C round closed at $208 million in November 2023, valuing AI21 at $1.4 billion and bringing total funding to $336 million, according to SiliconANGLE. Investors in that round included Google and Nvidia, with Intel Capital and Comcast Ventures joining for the extension. By then the product line included the Jurassic-2 models, task-specific models for jobs such as summarization, Contextual Answers for question answering over a company's own documents, and Wordtune Read for summarizing long texts.

The company's most important technical contribution is Jamba, described in a paper posted to arXiv in March 2024. A standard Transformer uses attention, which compares every token with every earlier token and stores a key-value cache (the KV cache) of past tokens in memory. That cache grows linearly with the length of the input, so very long documents become expensive. Mamba is a selective state-space model: it reads tokens one at a time and carries a fixed-size summary state forward, so its memory does not grow with context length, but on its own it can be weaker at precisely recalling earlier text. Jamba interleaves the two. Each block has eight layers, one attention layer to seven Mamba layers, and every second layer swaps the usual feed-forward network for a mixture-of-experts layer with 16 experts, of which two are used per token. The result is a model with 52 billion total parameters but only 12 billion active for any single token.

The efficiency numbers in the paper are concrete. At a 256,000-token context in 16-bit precision, Jamba needs a 4 GB KV cache, against 32 GB for Mixtral and 128 GB for Llama-2 70B. It fits on a single 80 GB GPU with about 140,000 tokens of text, and on long contexts the authors measured roughly three times the throughput of Mixtral. Quality was competitive rather than leading: Jamba scored 67.4 on the MMLU knowledge benchmark against 70.6 for Mixtral. The first release was a base model with no instruction tuning or safety alignment, published under the Apache 2.0 license, and the authors warned it was not meant for end users without further adaptation.

Jamba-1.5, published in August 2024, scaled the design up. Jamba-1.5-Large has 398 billion total and 94 billion active parameters; Jamba-1.5-Mini has 52 billion total and 12 billion active. Both were released under the Jamba Open Model License. On the RULER long-context test the paper reports an effective context of 256,000 tokens for both, with Large averaging 95.7. The KV cache at 256,000 tokens is 9 GB for Large, compared with 80 GB for Llama-3.1 70B and 252 GB for Llama-3.1 405B. On general benchmarks the models trail the strongest peers in places: Jamba-1.5-Large scores 80.0 on MMLU against 83.6 for Llama-3.1 70B. In October 2025 AI21 added Jamba Reasoning 3B, a small hybrid model that IEEE Spectrum reported runs at more than 17 tokens per second with its full 250,000-token context.

Compared with peers, AI21 occupied a middle position. It never matched the scale or benchmark leadership of OpenAI, Anthropic or Google, and it did not have the distribution of Meta's open Llama models. Its argument was efficiency on long documents, which matters for enterprise retrieval, contract review and summarization. The hybrid idea itself spread: the Jamba paper is widely cited as evidence that a small share of attention layers can restore the recall that pure state-space models lack.

The business side has been difficult. Calcalist reported annual revenue of roughly $50 million that barely changed after 2023, and headcount falling from 260 in 2023 to about 200 at the end of 2025. AI21 shut down Wordtune development in April 2025. In late December 2025 Calcalist reported that Nvidia was in advanced talks to acquire the company for $2 billion to $3 billion, mainly for its staff, but no sale happened. In May 2026 AI21 announced it would cut 110 of its 180 employees, halt development of the Jamba family and concentrate on Maestro, its agent orchestration platform, under a commercial agreement with the cloud provider Nebius. Nebius described the arrangement as a technology license, while Calcalist reported that a large part of the engineering team was expected to move to Nebius.

In practice, using AI21's work today means one of two things. The open Jamba weights remain available and can be served with common inference software; the hardware floor ranges from a laptop for Jamba Reasoning 3B, to one 80 GB GPU for the original Jamba, to an eight-GPU server for Jamba-1.5-Large. Teams choosing Jamba should weigh its long-context memory savings against the fact that its developer has stopped training new versions, so no further updates or fixes should be expected.