# AI21 Labs

> Source: https://aiwiki.ai/wiki/ai21_labs
> Updated: 2026-06-21
> Categories: AI Companies, Large Language Models, Natural Language Processing
> From AI Wiki (https://aiwiki.ai), a free encyclopedia of artificial intelligence. Quote with attribution.

AI21 Labs is an Israeli [artificial intelligence](/wiki/artificial_intelligence) company, founded in 2017 by Yoav Shoham, Ori Goshen, and Amnon Shashua, that develops [large language models](/wiki/large_language_model) (LLMs) and AI orchestration systems for enterprise use. Headquartered in Tel Aviv, it is best known for the [Jamba](/wiki/jamba) model family, which in March 2024 became the first production-grade language model to scale a hybrid [Mamba](/wiki/mamba) [state space model](/wiki/state_space_model) (SSM) and [Transformer](/wiki/transformer) architecture (the name Jamba stands for "Joint Attention and Mamba").[3][4] Its earlier Jurassic series and the Wordtune consumer writing assistant established the company before its architectural pivot.[1][11] AI21 Labs last closed funding in November 2023, an oversubscribed $208 million Series C that brought total funding to $336 million at a $1.4 billion valuation.[9] In late 2025, [NVIDIA](/wiki/nvidia) entered advanced talks to acquire the company for a reported $2 billion to $3 billion.[12]

## What does AI21 Labs do?

AI21 Labs builds foundation models and enterprise AI infrastructure with a focus on accuracy, grounding, and long-context reasoning. Its portfolio spans three layers: foundation models (the Jurassic and Jamba families), an AI planning and orchestration system (Maestro), and developer and consumer products (AI21 Studio, Task-Specific APIs, and Wordtune). The company positions itself against general-purpose providers like [OpenAI](/wiki/openai) and [Anthropic](/wiki/anthropic) by emphasizing reliability and deployment flexibility for mission-critical enterprise workflows.

## History

### Founding and early years (2017-2020)

AI21 Labs was founded in November 2017 in Tel Aviv, Israel, by three co-founders with deep expertise in artificial intelligence and technology entrepreneurship.

**Yoav Shoham** is a professor emeritus of computer science at [Stanford University](/wiki/stanford_university) and a former Principal Scientist at [Google](/wiki/google). He has made notable contributions to AI, game theory, and multi-agent systems over a career spanning several decades.

**Ori Goshen** is a technology entrepreneur with over 15 years of experience in product leadership roles. Before co-founding AI21 Labs, Goshen co-founded Crowdx, a network analytics company, and led the development of VoIP products.

**Amnon Shashua** is a professor of computer science at the Hebrew University of Jerusalem and a prominent Israeli entrepreneur. He co-founded [Mobileye](/wiki/mobileye), the autonomous driving technology company that Intel acquired in 2017 for approximately $15.3 billion. Shashua also co-founded OrCam, a company focused on assistive technology for the visually impaired, and the digital bank One Zero.

The company's name, AI21, refers to "artificial intelligence for the 21st century." The founders started with a broad vision of building AI systems that could genuinely understand and generate natural language, rather than targeting a specific product from the outset. In January 2019, AI21 Labs raised $9.5 million in a seed funding round. Through its early years, the company operated in stealth mode while developing its core [natural language processing](/wiki/natural_language_processing) technology.

### Wordtune launch and coming out of stealth (2020)

On October 27, 2020, AI21 Labs emerged from stealth with the launch of Wordtune, its first consumer product.[11] Wordtune is an AI-powered writing assistant that can understand the context and meaning of text and suggest paraphrases, rewrites, and alternative phrasings. The product launched as a Chrome browser extension and quickly gained traction among individual users and professionals.[11]

### Scaling language models (2021-2023)

In 2021, AI21 Labs began to expand rapidly. The company completed a $25 million Series A round led by Pitango First in November 2021. That same year, the company launched AI21 Studio and its Jurassic-1 family of language models, entering the competitive landscape alongside [OpenAI](/wiki/openai) and other foundation model developers.[1]

In July 2022, AI21 Labs raised $64 million in a Series B round led by Ahren Innovation Capital, bringing its total funding to $118 million at a valuation of $664 million.[10] The funds were directed toward research and development, as well as expanding the company's sales and marketing teams.[10]

In March 2023, the company released Jurassic-2, a significant upgrade to its model family.[2] By August 2023, AI21 Labs had raised $155 million in the first tranche of its Series C round, reaching a valuation of $1.4 billion and achieving unicorn status. Investors in this round included Walden Catalyst, Pitango, SCB10X, b2venture, Samsung Next, and co-founder Amnon Shashua, with [Google](/wiki/google) and [NVIDIA](/wiki/nvidia) also participating.[8]

In November 2023, AI21 Labs extended its Series C with an additional $53 million, bringing the total Series C to $208 million. New investors Intel Capital and Comcast Ventures joined the round, and the company's total funding reached $336 million.[9]

### The Jamba era (2024-present)

In March 2024, AI21 Labs released Jamba, a model that marked a significant departure from the pure Transformer architecture that had dominated the LLM landscape. Jamba introduced a hybrid architecture combining Mamba [state space model](/wiki/state_space_model) (SSM) layers with Transformer attention layers. This made Jamba the first production-grade model to scale the Mamba architecture beyond small experimental sizes.[3]

In August 2024, the company released the Jamba 1.5 model family, scaling the hybrid architecture further with Jamba 1.5 Large (398B total parameters) and Jamba 1.5 Mini (52B total parameters).[5]

In July and August 2025, AI21 Labs released the Jamba 1.7 models (Large on July 7, 2025 and Mini on August 8, 2025), incremental updates that improved grounding and instruction-following while retaining the 256K context window and adding Arabic and Hebrew to the supported languages.[16]

In January 2026, AI21 Labs released Jamba 2, a pair of compact models (3B and Mini) focused on enterprise reliability, grounding, and instruction-following, released under the Apache 2.0 license.[6]

### What happened with the reported Series D and NVIDIA talks (2025-2026)?

Through much of 2025, AI21 Labs was widely reported to be raising, and in some accounts to have closed, a $300 million Series D backed by [Google](/wiki/google) and [NVIDIA](/wiki/nvidia). According to later reporting by Calcalist, that round was never completed, never formally announced, and never reflected in the company's capital structure; AI21's last actual fundraising remained the November 2023 Series C extension to $208 million at a $1.4 billion valuation.[17]

In late December 2025, multiple reports indicated that NVIDIA was in advanced talks to acquire AI21 Labs for $2 billion to $3 billion. The reported deal was described as primarily an acqui-hire, with NVIDIA seeking to secure AI21's team of roughly 200 employees, most of whom hold advanced degrees and possess specialized expertise in AI research. If completed, the acquisition would represent NVIDIA's fourth significant purchase in Israel and its second-largest after the $7 billion Mellanox acquisition in 2020.[12] Co-founder Amnon Shashua publicly acknowledged the discussions while playing them down, saying: "There are talks with Nvidia, there are talks with others, but nothing even close to talking about it in the press."[18] As of early 2026, neither company had officially confirmed a transaction.[12]

## Language Models

### Jurassic-1 (2021)

Jurassic-1 (J1) was AI21 Labs' first family of large language models, announced alongside the AI21 Studio developer platform on August 4, 2021.[1] The family consisted of two autoregressive models:

- **J1-Jumbo**: 178 billion parameters, making it one of the largest publicly accessible language models at the time of release.
- **J1-Large**: 7 billion parameters, offering a smaller and faster alternative.

A distinguishing feature of Jurassic-1 was its vocabulary of approximately 250,000 tokens, roughly five times larger than most existing vocabularies at the time.[14] This vocabulary included multi-word tokens such as common expressions, phrases, and named entities, which improved computational efficiency, reduced latency, and allowed more text to fit within a fixed context window. The larger vocabulary also meant that Jurassic-1 could pack more examples into a prompt for [few-shot learning](/wiki/few-shot_learning) compared to similarly sized models like [GPT-3](/wiki/gpt-3).[14]

Jurassic-1 was trained on a broad corpus spanning web text, academic publications, legal documents, and source code. The architecture diverged from the standard Transformer design used by GPT-3; AI21 Labs optimized the depth-to-width ratio based on theoretical work on expressivity tradeoffs in self-attention networks.[14]

### Jurassic-2 (2023)

Jurassic-2 (J2) was released on March 9, 2023, as the successor to Jurassic-1. The model family offered several improvements over its predecessor, including better quality, lower latency (up to 30% faster inference), multilingual support, and zero-shot instruction-following capabilities.[2]

The Jurassic-2 lineup included three tiers:

- **J2-Ultra** (originally J2-Jumbo): The largest and most capable model in the family.
- **J2-Mid** (originally J2-Grande): A mid-sized model balancing quality and cost.
- **J2-Light** (originally J2-Large): The smallest and fastest model, optimized for cost efficiency.

Both J2-Ultra and J2-Mid were available in instruction-tuned variants that could follow natural language instructions without requiring task-specific [fine-tuning](/wiki/fine_tuning). Jurassic-2 also added support for multiple European languages, including Spanish, French, German, Portuguese, Italian, and Dutch.[2]

Jurassic-2 models were made available through AI21 Studio, [Amazon Bedrock](/wiki/amazon_bedrock), and [Amazon SageMaker](/wiki/amazon_sagemaker), expanding AI21 Labs' reach to enterprise customers already using AWS infrastructure.[2]

### What is Jamba? (March 2024)

Jamba, released on March 28, 2024, represented a fundamental architectural shift for AI21 Labs. Rather than building on the standard [Transformer](/wiki/transformer) architecture, Jamba introduced a hybrid design combining Transformer attention layers with [Mamba](/wiki/mamba) structured state space model (SSM) layers, augmented by a [Mixture of Experts](/wiki/mixture_of_experts) (MoE) mechanism.[3] AI21 Labs described Jamba as "the world's first production-grade Mamba based model," enhancing the SSM with Transformer elements to compensate for the limitations of a pure SSM design.[3]

The Mamba architecture, introduced by Albert Gu and Tri Dao in December 2023, uses selective state spaces to process sequences in linear time rather than the quadratic time complexity of standard Transformer attention.[7] This makes it particularly efficient for long sequences but, prior to Jamba, Mamba-based models had not been scaled beyond approximately 3 billion parameters in production settings.[4]

Jamba's architecture interleaves blocks of Transformer and Mamba layers, with each block containing either an attention or a Mamba layer followed by a multi-layer perceptron (MLP). The overall design uses a ratio of one Transformer attention layer for every eight total layers. MoE is applied to selected layers to increase total model capacity while keeping the number of active parameters manageable during inference.[4]

Key specifications of the original Jamba model:

| Specification | Value |
|---|---|
| Total Parameters | 52 billion |
| Active Parameters | 12 billion |
| Context Window | 256K tokens |
| Architecture | Hybrid Mamba-Transformer + MoE |
| License | Apache 2.0 |
| Max Context on Single 80GB GPU | ~140K tokens |

Jamba demonstrated approximately 3x throughput on long contexts compared to [Mixtral](/wiki/mixtral) 8x7B, a leading open MoE model at the time.[3] It also matched or outperformed comparable models on standard language benchmarks and long-context evaluations. The model was released as open weights on [Hugging Face](/wiki/hugging_face) under the Apache 2.0 license, making it freely available for commercial use.[3]

Jamba's release was significant because it demonstrated that the hybrid SSM-Transformer approach could be scaled to production quality, challenging the assumption that pure Transformer architectures were the only viable path for building competitive large language models.

### Jamba 1.5 (August 2024)

On August 22, 2024, AI21 Labs released the Jamba 1.5 model family, scaling the hybrid Mamba-Transformer architecture to larger sizes.[5] The family included two models:

- **Jamba 1.5 Large**: 398 billion total parameters with 94 billion active parameters, representing the first time a hybrid SSM-Transformer model had been scaled to this size.
- **Jamba 1.5 Mini**: 52 billion total parameters with 12 billion active parameters, matching the original Jamba's size but with improved training and instruction-following.

Both models retained the 256K context window and hybrid architecture of the original Jamba. They added support for function calling, [retrieval-augmented generation](/wiki/retrieval_augmented_generation) (RAG) optimizations, structured JSON output, and multilingual capabilities.[5]

Jamba 1.5 Large was positioned as AI21's most advanced model, capable of handling complex reasoning tasks such as financial analysis and legal document review. Jamba 1.5 Mini was designed for speed and efficiency in tasks like customer support, document summarization, and general text generation.

The Jamba 1.5 models were released as open weights under the Jamba Open Model License and made available through AI21 Studio, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Azure AI.[5]

### Jamba 1.7 (2025)

The Jamba 1.7 update arrived in two parts, with Jamba 1.7 Large released on July 7, 2025 and Jamba 1.7 Mini on August 8, 2025.[16] The 1.7 generation kept the same parameter counts and 256K context window as Jamba 1.5 (398B total / 94B active for Large; 52B total / 12B active for Mini) and focused on improvements in grounding and instruction-following. The 1.7 models broadened multilingual coverage to include Arabic and Hebrew alongside English, Spanish, French, Portuguese, Italian, Dutch, and German.[16]

### Jamba 2 (January 2026)

Jamba 2 was released on January 8, 2026, as a pair of compact models focused on enterprise reliability and steerability.[6] The family includes:

- **Jamba 2 3B**: A dense (non-MoE) model with 3 billion parameters.
- **Jamba 2 Mini**: A MoE model with 52 billion total parameters and 12 billion active parameters.

Both models feature a 256K context window and were released under the Apache 2.0 license. Jamba 2 was mid-trained on 500 billion carefully curated tokens with increased representation of math, code, high-quality web data, and long documents. The training process included a state passing phase for the Mamba layers, cold-start supervised fine-tuning, direct preference optimization (DPO), and multi-phase on-policy [reinforcement learning](/wiki/reinforcement_learning).[6]

The Jamba 2 models were designed for enterprise workflows that demand accuracy and grounding, producing answers that stay closely tied to source material across technical manuals, research papers, company policies, and internal knowledge bases. In benchmarks, Jamba 2 led on instruction-following tests ([IFBench](/wiki/ifbench), IFEval, Collie) and grounding evaluations (FACTS).[6]

### Model Comparison Table

| Model | Release Date | Total Parameters | Active Parameters | Context Window | Architecture | License |
|---|---|---|---|---|---|---|
| Jurassic-1 Jumbo | August 2021 | 178B | 178B | 2,048 tokens | Transformer | Proprietary |
| Jurassic-1 Large | August 2021 | 7B | 7B | 2,048 tokens | Transformer | Proprietary |
| Jurassic-2 Ultra | March 2023 | Undisclosed | Undisclosed | 8,192 tokens | Transformer | Proprietary |
| Jurassic-2 Mid | March 2023 | Undisclosed | Undisclosed | 8,192 tokens | Transformer | Proprietary |
| Jurassic-2 Light | March 2023 | Undisclosed | Undisclosed | 8,192 tokens | Transformer | Proprietary |
| Jamba | March 2024 | 52B | 12B | 256K tokens | Hybrid Mamba-Transformer + MoE | Apache 2.0 |
| Jamba 1.5 Mini | August 2024 | 52B | 12B | 256K tokens | Hybrid Mamba-Transformer + MoE | Jamba Open Model License |
| Jamba 1.5 Large | August 2024 | 398B | 94B | 256K tokens | Hybrid Mamba-Transformer + MoE | Jamba Open Model License |
| Jamba 1.7 Mini | August 2025 | 52B | 12B | 256K tokens | Hybrid Mamba-Transformer + MoE | Jamba Open Model License |
| Jamba 1.7 Large | July 2025 | 398B | 94B | 256K tokens | Hybrid Mamba-Transformer + MoE | Jamba Open Model License |
| Jamba 2 3B | January 2026 | 3B | 3B | 256K tokens | Hybrid Mamba-Transformer (dense) | Apache 2.0 |
| Jamba 2 Mini | January 2026 | 52B | 12B | 256K tokens | Hybrid Mamba-Transformer + MoE | Apache 2.0 |

## Products and Services

### Maestro: AI planning and orchestration

Maestro is AI21 Labs' AI planning and orchestration system for the enterprise, introduced on March 10, 2025 and described by the company as "the world's first AI Planning and Orchestration System built for the enterprise."[19] Rather than being a single model, Maestro is a framework (which AI21 calls the AI Planning and Orchestration System, or AIPOS) that wraps around large language models to analyze a task, plan a multi-step solution, execute it, and validate the result against requirements.[20]

AI21 Labs reported that Maestro boosts the instruction-following accuracy of paired models by up to 50%, raising the accuracy of models such as GPT-4o and Claude 3.5 Sonnet, and enabling reasoning models like o3-mini to exceed 95% accuracy on its internal benchmarks.[19][20] The company framed the system as a response to the gap between AI demos and production deployment, citing an AWS finding that only 6% of organizations had a generative AI application in deployment.[20] Maestro was made available initially through early access, with broader SaaS and virtual private cloud (VPC) availability planned for later in 2025.[20]

### AI21 Studio

AI21 Studio is the company's developer platform, providing API access to AI21's language models and task-specific capabilities. Launched alongside Jurassic-1 in August 2021, the platform allows developers to build text-based applications including virtual assistants, chatbots, content generation tools, text classifiers, and more.[1]

The platform offers several access tiers and includes features such as custom model training (requiring as few as 50 to 100 training examples for fine-tuning), a web-based interactive playground for testing prompts, and comprehensive API documentation. AI21 Studio supports both the Jurassic and Jamba model families.

### Task-Specific APIs

Alongside Jurassic-2, AI21 Labs introduced a set of Task-Specific Models (TSMs): pre-built, highly optimized APIs designed for specific natural language processing tasks. Unlike general-purpose language model APIs, these endpoints require no [prompt engineering](/wiki/prompt_engineering) or fine-tuning and can be integrated with a single API call.[2]

The Task-Specific APIs include:

| API | Function |
|---|---|
| Paraphrase | Generates alternative phrasings of input text while preserving meaning, with adjustable tone and length |
| Summarize | Condenses long documents into concise summaries, optimized for financial reports, legal documents, and technical papers |
| Contextual Answers | Answers user questions based on a provided knowledge base or document context |
| Grammatical Error Correction | Identifies and corrects grammatical errors in text |
| Text Improvements | Suggests improvements to text for clarity, fluency, and engagement |

AI21 Labs has reported that its Summarize API achieved a faithfulness rate 19% higher than OpenAI's Davinci-003 model and an acceptance rate 18% higher on the same benchmark.[2]

### Wordtune

Wordtune is AI21 Labs' consumer-facing AI writing assistant, launched on October 27, 2020.[11] The product was the company's first public offering and served as the vehicle through which AI21 emerged from stealth.[11]

Wordtune is available as a Chrome browser extension, a web application, an iOS app, and an integration within Google Docs. It offers several core features:

- **Rewriting and paraphrasing**: Users can highlight text and receive multiple alternative phrasings that preserve the original meaning.
- **Tone adjustment**: The tool can make text more formal or more casual, and adjust sentence length.
- **Wordtune Spices**: A feature that generates contextual suggestions tailored to specific professional domains (for example, legal, healthcare, or marketing writing), with source citations for factual claims.
- **Wordtune Read**: Launched in May 2021, this feature analyzes and summarizes documents and web articles, with a Spotlight feature that allows users to re-summarize content from different perspectives. It supports PDF uploads, web links, and YouTube video transcripts.[15]

Wordtune has grown to tens of millions of users since its launch. Google named it one of its favorite Chrome extensions of 2021. Enterprise customers include Monday.com, eBay, UiPath, and Transmit Security. The product offers a free tier with basic rewriting capabilities and a premium subscription at $9.99 per month that unlocks advanced features.

### Cloud Platform Integrations

AI21 Labs models are available through multiple cloud platforms, broadening access for enterprise customers:

| Platform | Available Models |
|---|---|
| [Amazon Bedrock](/wiki/amazon_bedrock) | Jurassic-2, Jamba 1.5 |
| Amazon SageMaker | Jurassic-2 |
| [Google Cloud](/wiki/google_cloud_terms) Vertex AI | Jamba 1.5 |
| Microsoft Azure AI | Jamba 1.5 |
| NVIDIA API Catalog | Jamba |

These integrations allow enterprises to deploy AI21 models within their existing cloud infrastructure without managing separate model hosting.

## How does the hybrid SSM-Transformer architecture work?

The most distinctive technical contribution from AI21 Labs is the hybrid Mamba-Transformer architecture used in the Jamba model family. Understanding this approach requires some background on the two architectures it combines.

### Transformer Attention

The standard [Transformer](/wiki/transformer) architecture, introduced in 2017, uses self-attention mechanisms to process input sequences. [Self-attention](/wiki/self_attention) allows every token in a sequence to attend to every other token, capturing long-range dependencies effectively. However, the computational cost of self-attention scales quadratically with sequence length (O(n^2)), which makes processing very long sequences expensive in terms of both time and memory.

### State Space Models and Mamba

Structured [state space models](/wiki/state_space_model) (SSMs) offer an alternative approach to sequence modeling. SSMs process sequences by maintaining a hidden state that is updated as each new token arrives, similar to [recurrent neural networks](/wiki/rnn) but with a mathematically structured state transition. This allows SSMs to process sequences in linear time (O(n)), making them far more efficient for long inputs.

The Mamba architecture, introduced by Albert Gu and Tri Dao in their December 2023 paper "Mamba: Linear-Time Sequence Modeling with Selective State Spaces," improved upon earlier SSMs by adding a selective mechanism that allows the model to focus on relevant parts of the input.[7] This selectivity addressed a key weakness of previous SSMs, which struggled with content-based reasoning tasks.[7]

### How Jamba Combines Both

Jamba's architecture interleaves Mamba and Transformer layers in a structured pattern. Each block contains either a Mamba SSM layer or a Transformer attention layer, followed by a standard multi-layer perceptron. The ratio of Mamba layers to Transformer layers is approximately 7:1, meaning that only one out of every eight layers uses the computationally expensive attention mechanism.[4]

This hybrid approach offers several benefits:

1. **Memory efficiency**: The Mamba layers maintain a compact recurrent state rather than storing full key-value caches for all past tokens, dramatically reducing memory requirements for long contexts.
2. **Throughput**: The linear-time processing of Mamba layers delivers higher throughput on long sequences compared to pure Transformer models.
3. **Quality preservation**: The interspersed Transformer attention layers provide the content-based reasoning and long-range dependency modeling capabilities that pure SSM models can lack.
4. **MoE scaling**: The Mixture of Experts mechanism allows the model to have a large total parameter count while keeping the active parameter count (and thus inference cost) low.

The result is a model that can process 256K token contexts efficiently on standard hardware, fitting up to 140K tokens on a single 80GB GPU, something that would require significantly more resources with a pure Transformer model of comparable quality.[4]

## How much funding has AI21 Labs raised?

AI21 Labs has raised $336 million in total funding through its seed, Series A, Series B, and Series C rounds, with its last completed raise in November 2023.[9] A reported $300 million Series D backed by Google and NVIDIA, widely described in 2025, was never closed.[17]

| Round | Date | Amount | Valuation | Lead Investors |
|---|---|---|---|---|
| Seed | January 2019 | $9.5M | Undisclosed | Undisclosed |
| Series A | November 2021 | $25M | Undisclosed | Pitango First |
| Series B | July 2022 | $64M | $664M | Ahren Innovation Capital |
| Series C (Tranche 1) | August 2023 | $155M | $1.4B | Walden Catalyst, Pitango, SCB10X, b2venture, Samsung Next |
| Series C (Tranche 2) | November 2023 | $53M | $1.4B | Intel Capital, Comcast Ventures |
| **Total** | | **$336M** | **$1.4B** | |

Notable investors across all rounds include Google, NVIDIA, Intel Capital, Comcast Ventures, Pitango, Walden Catalyst, Ahren Innovation Capital, TPY Capital, Samsung Next, SCB10X, b2venture, and co-founder Amnon Shashua.[8]

## How does AI21 Labs compare to its competitors?

AI21 Labs operates in a highly competitive market for large language models and enterprise AI services. Its primary competitors include several well-funded companies:

| Company | Headquarters | Key Models | Focus |
|---|---|---|---|
| [OpenAI](/wiki/openai) | San Francisco, USA | [GPT-4](/wiki/gpt-4), GPT-4o | General-purpose AI, consumer and enterprise |
| [Anthropic](/wiki/anthropic) | San Francisco, USA | [Claude](/wiki/claude) | AI safety, enterprise |
| [Cohere](/wiki/cohere) | Toronto, Canada | Command R | Enterprise NLP |
| [Mistral AI](/wiki/mistral_ai) | Paris, France | [Mistral](/wiki/mistral), Mixtral | Open-weight models, enterprise |
| [Google DeepMind](/wiki/google_deepmind) | London, UK | [Gemini](/wiki/gemini) | General-purpose AI, research |
| [Meta AI](/wiki/meta_ai) | Menlo Park, USA | [LLaMA](/wiki/llama) | Open-source models |

AI21 Labs differentiates itself in several ways. First, its hybrid Mamba-Transformer architecture gives the Jamba models an efficiency advantage for long-context processing compared to pure Transformer competitors. Second, the company has maintained a strong enterprise focus, offering deployment flexibility through SaaS, cloud partnerships (AWS, Google Cloud, Azure), virtual private cloud (VPC), and on-premises options. Third, its Task-Specific APIs and the Maestro orchestration layer provide specialized, ready-to-use solutions that emphasize accuracy and reliability, which appeals to enterprises seeking dependable integration.

AI21 Labs co-founder Ori Goshen has publicly stated that the company "usually wins" when competing directly with OpenAI for enterprise business, citing advantages in accuracy, deployment flexibility, and customer support.[13]

Compared to open-source competitors like Meta's LLaMA and Mistral AI's models, AI21 Labs occupies a middle ground: the Jamba base models are released with open weights under permissive licenses, while the company also offers proprietary, optimized versions through its commercial API platform.

## Company Profile

| Detail | Information |
|---|---|
| Founded | November 2017 |
| Headquarters | 124 Shlomo Ibn Gabirol Street, Tel Aviv, Israel |
| Co-Founders | Yoav Shoham (Co-CEO), Ori Goshen (Co-CEO), Amnon Shashua (Chairman) |
| Employees | Approximately 200-250 |
| Total Funding | $336 million |
| Valuation | $1.4 billion (as of November 2023) |
| Key Products | AI21 Studio, Jamba models, Maestro, Wordtune, Task-Specific APIs |

## References

1. AI21 Labs. "Announcing AI21 Studio and Jurassic-1 Language Models." AI21 Blog, August 4, 2021. https://www.ai21.com/blog/announcing-ai21-studio-and-jurassic-1/
2. AI21 Labs. "Announcing Jurassic-2 and Task-Specific APIs." AI21 Blog, March 9, 2023. https://www.ai21.com/blog/introducing-j2/
3. AI21 Labs. "Introducing Jamba: AI21's Groundbreaking SSM-Transformer Model." AI21 Blog, March 28, 2024. https://www.ai21.com/blog/announcing-jamba/
4. Lieber, Opher, et al. "Jamba: A Hybrid Transformer-Mamba Language Model." arXiv:2403.19887, March 2024. https://arxiv.org/abs/2403.19887
5. AI21 Labs. "The Jamba 1.5 Open Model Family: The Most Powerful and Efficient Long Context Models." AI21 Blog, August 22, 2024. https://www.ai21.com/blog/announcing-jamba-model-family/
6. AI21 Labs. "Introducing Jamba2: The Open Source Model Family for Enterprise Reliability and Efficiency." AI21 Blog, January 8, 2026. https://www.ai21.com/blog/introducing-jamba2/
7. Gu, Albert and Tri Dao. "Mamba: Linear-Time Sequence Modeling with Selective State Spaces." arXiv:2312.00752, December 2023. https://arxiv.org/abs/2312.00752
8. TechCrunch. "[Generative AI](/wiki/generative_ai) startup AI21 Labs lands $155M at a $1.4B valuation." August 30, 2023. https://techcrunch.com/2023/08/30/generative-ai-startup-ai21-labs-lands-155m-at-a-1-4b-valuation/
9. AI21 Labs. "AI21 Completes $208 Million Oversubscribed Series C Round." Press Release, November 21, 2023. https://www.ai21.com/blog/ai21-completes-208-million-oversubscribed-series-c-round/
10. PR Newswire. "AI21 Labs Raises $64 Million to Change the Way People Read and Write Using Artificial Intelligence." July 12, 2022. https://www.prnewswire.com/news-releases/ai21-labs-raises-64-million-to-change-the-way-people-read-and-write-using-artificial-intelligence-301584831.html
11. BusinessWire. "AI21 Labs Comes out of Stealth and Launches First Deep-Tech Writing Assistant, Wordtune." October 27, 2020. https://www.businesswire.com/news/home/20201027005162/en/AI21-Labs-Comes-out-of-Stealth-and-Launches-First-Deep-Tech-Writing-Assistant-Wordtune
12. Calcalist Tech. "Nvidia in advanced talks to acquire AI21 in $2-3 billion deal focused on talent." December 30, 2025. https://www.calcalistech.com/ctechnews/article/rkbh00xnzl
13. VentureBeat. "AI21 Labs co-founder says 'we usually win' when competing with OpenAI for enterprise business." https://venturebeat.com/ai/ai21-labs-co-founder-says-we-usually-win-when-competing-with-openai-for-enterprise-business
14. Lieber, Opher, et al. "Jurassic-1: Technical Details and Evaluation." AI21 Labs White Paper, 2021. https://www.ai21.com/research/jurassic-1-technical-details-evaluation/
15. AI21 Labs. "Introducing Wordtune Read." AI21 Blog, May 2021. https://www.ai21.com/blog/introducing-wordtune-read/
16. AI21 Labs. "Jamba Foundation Models." AI21 Documentation, 2025. https://docs.ai21.com/docs/jamba-foundation-models
17. Calcalist Tech. "AI21 never closed its reported $300 million round as Nvidia weighs an acquisition." 2025. https://www.calcalistech.com/ctechnews/article/rjswz37eze
18. Calcalist Tech. "'There are talks with Nvidia and others': Shashua confirms AI21 acquisition discussions." 2026. https://www.calcalistech.com/ctechnews/article/h1e8rttn11x
19. PR Newswire. "AI21 Introduces Maestro, the World's First AI Planning and Orchestration System Built for the Enterprise." March 10, 2025. https://www.prnewswire.com/news-releases/ai21-introduces-maestro-the-worlds-first-ai-planning-and-orchestration-system-built-for-the-enterprise-302397075.html
20. AI21 Labs. "Meet Maestro: The AI System for Automating Data-Intensive Enterprise Tasks." AI21 Blog, March 10, 2025. https://www.ai21.com/blog/maestro-ai-planning-orchestration/