New York University
3 models across 1 family · Latest: Cambrian-1 8B (2024-06)
Global Collaboration Driving AI Innovation
New York University's portfolio covers 3 active models across 1 current family, spanning general LLM work. Open a model detail page to compare provider routes and sourced benchmarks.
Covers 0 workload areas across 3 active tracked models; last verified 2026-05-19.
Use it for
- Teams evaluating general LLM work across this lab's releases
- Comparing model families before committing to a flagship
- Migration and pricing follow-ups across 3 tracked models
Do not use it for
- Choosing a hosting provider without opening a model page for price ladders
Active models
3
Current models from this lab, excluding deprecated ones
Active families
1
Current model families from this lab
Open catalog
3 open
0 open source / 3 open weights
Lowest output price
Not tracked
No provider output pricing linked yet
Latest dated release
2024-06-09
Cambrian-1 8B
Freshness
2026-05-19
Researched 60d ago
Information
Release cadence
Showing 3 recent dated releases (full timeline below). Latest: Cambrian-1 8B (2024-06-09).
Where this lab wins
Not enough capability or benchmark coverage yet to call strengths for this lab.
Flagship quality / price signal
Flagship: Cambrian-1 8B (best sourced coding quality-per-dollar in this portfolio).
Quality-per-dollar unavailable for this flagship — benchmark coverage or output token pricing is still missing.
New York University is an American AI research lab founded in 1831. Global Collaboration Driving AI Innovation. New York University ships 1 model family totaling 3 models, with the most recent release Cambrian-1 8B in 2024-06. Notable families include Cambrian. Use it as a stable reference for lab background, release coverage, and follow-up model pages as they are added. Researchers and evaluators. View official API endpoints, benchmark performance, and coding/agent fit for every New York University model.
About
New York University (NYU) has carved out a prominent niche in the realms of generative AI and large language models (LLMs), underlined by its extensive and dynamic research initiatives. The institution's reputation in AI is fortified by its interdisciplinary collaborations and a diverse faculty that specialize across various AI domains. NYU distinctly emphasizes ethical considerations and responsible AI use, ensuring the integration of AI technologies into educational frameworks benefits the community responsibly. A pivotal player in NYU's endeavors is the Computational Intelligence, Learning, Vision, and Robotics (CILVR) Lab. This lab plays a crucial role in pioneering advancements in machine learning and AI, spearheading research in areas ranging from computer vision to healthcare and natural language processing. Under the guidance of esteemed researchers like Yann LeCun, the lab's work is a testimony to its commitment to developing AI systems that are adept at continuous learning and adaptation, reflecting the real-world applicability of their machine learning innovations 234. NYU's faculty contributes significantly to a thriving research ecosystem, which is particularly evident in the Machine Learning for Language (ML²) group. This group focuses on pioneering machine learning methods tailored for natural language processing, scrutinizing the potential of LLMs to enhance writing diversity and content development. Researchers such as Samuel Bowman and Kyunghyun Cho are at the forefront, delving into practical applications of LLMs and their implications for diverse communication contexts 234. Ethical use of AI technologies is a core part of NYU’s philosophy. The university offers resources for both instructors and researchers aimed at managing the complexities of generative AI from an ethical standpoint. This includes comprehensive guidelines on its ethical implications as well as workshops and seminars designed to educate both faculty and students about the implications and responsibilities associated with AI tools 567. In addition to its robust internal initiatives, NYU places a strong emphasis on collaboration with external partners, including industry leaders and other academic institutions. These partnerships are designed to align research undertakings with tangible real-world applications. By engaging with a network of startups and established enterprises, NYU not only bolsters its research agenda but also provides its students with crucial opportunities for networking and practical exposure 8910. NYU's dedication to researching generative AI is marked by its commitment to interdisciplinary approaches, ethical responsibility, and collaborative efforts. As it continues to develop responsible AI technologies, NYU's efforts in educating future AI practitioners consolidate its status as a leading institution in the rapidly advancing field of artificial intelligence.
Featured models
| Model | Released | Context | Input price ($/1M) | Output price ($/1M) | License | Openness |
|---|---|---|---|---|---|---|
| Cambrian-1 8B | 2024-06-09 | 8k | - | - | Llama 3 Community | Open weights |
| Cambrian-1 13B | 2024-06-09 | 8k | - | - | Llama 3 Community | Open weights |
| Cambrian-1 34B | 2024-06-09 | 8k | - | - | Llama 3 Community | Open weights |
Model families
Recent releases
- Cambrian-1 8B- 2024-06-09
- Cambrian-1 13B- 2024-06-09
- Cambrian-1 34B- 2024-06-09
FAQ
Who founded New York University and when?
New York University was founded in 1831 and is associated with New York, NY, United States.
What models has New York University released?
New York University ships 3 models across 1 family: Cambrian.
Is New York University's technology open source?
All tracked models are released under Open Weights.
Where is New York University headquartered?
New York University is headquartered in New York, NY, United States.
What is New York University known for?
Global Collaboration Driving AI Innovation. Its most prominent tracked family is Cambrian.
How can I access New York University's models?
New York University's provider availability is tracked on model pages as API and hosting data is verified.
Explore related pages
Last reviewed: 2026-05-19. Data sourced from public lab announcements and provider documentation.