OpenAssistant
1 model across 1 family · Latest: Open-Assistant SFT-1 12B (2023-04)
Open-source collaboration: democratizing AI.
OpenAssistant's portfolio covers 1 active model 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 1 active tracked model; 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 1 tracked models
Do not use it for
- Choosing a hosting provider without opening a model page for price ladders
Active models
1
Current models from this lab, excluding deprecated ones
Active families
1
Current model families from this lab
Open catalog
1 open
1 open source / 0 open weights
Lowest output price
Not tracked
No provider output pricing linked yet
Latest dated release
2023-04-15
Open-Assistant SFT-1 12B
Freshness
2026-05-19
Researched 60d ago
Information
Release cadence
Showing 1 recent dated release (full timeline below). Latest: Open-Assistant SFT-1 12B (2023-04-15).
Where this lab wins
Not enough capability or benchmark coverage yet to call strengths for this lab.
Flagship quality / price signal
Flagship: Open-Assistant SFT-1 12B (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.
OpenAssistant is an AI research lab founded in 2022. Open-source collaboration: democratizing AI. OpenAssistant ships 1 model family totaling 1 model, with the most recent release Open-Assistant SFT-1 12B in 2023-04. Notable families include Open-Assistant. Use it as a stable reference for lab background, release coverage, and follow-up model pages as they are added. Researchers and evaluators can scan counts, links, release. View official API endpoints, benchmark performance, and coding/agent fit for every OpenAssistant model.
About
OpenAssistant is a pioneering open-source initiative focused on democratizing both access to and research within the sphere of large language model (LLM) alignment. As opposed to proprietary models like ChatGPT, OpenAssistant is designed with a foundation of community engagement and full transparency during its development process. This commitment enables the creation of a conversational AI assistant that is not only powerful and accessible but also ethically developed, aspiring to meet and exceed the capabilities of its closed-source counterparts. The OpenAssistant project uniquely distinguishes itself through a massive crowdsourcing effort involving over 13,500 volunteers from around the globe. This collaborative approach resulted in the compilation of OpenAssistant Conversations, an extensive dataset comprising over 161,000 human-generated conversation messages across 35 languages. Each message is annotated with quality ratings, ensuring the highest standards and offering a stark contrast to projects that often rely on synthetic data or smaller, less varied datasets. While the open process for data collection poses certain challenges in managing biases, it simultaneously fosters inclusivity and model diversity, setting OpenAssistant apart in the landscape of AI development. OpenAssistant's training methodologies are among its most innovative features. The project employs supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), approaches that are akin to those used in advanced models like InstructGPT and ChatGPT. However, what sets OpenAssistant apart is its reliance on a robust and diverse dataset composed entirely of human-generated input. This is complemented by the use of self-critiquing models, which enhance the ability of human evaluators to identify and rectify flaws in the model's outputs through an iterative feedback process that continuously improves the AI's quality and accuracy. A critical aspect of OpenAssistant's methodology is its fully open-source nature. All components of the project, including the code, dataset, and trained models, are accessible under a permissive license. This openness not only supports collaboration within the research community but also further advancement by developers worldwide. Unlike many LLMs encased within proprietary constraints, OpenAssistant stands as a beacon of transparency, allowing researchers and developers deep insights into its architecture and datasets. While it may not yet match the performance levels of the most advanced proprietary models in every aspect, its dedication to openness and community-driven development signals significant potential for future innovation and expanded accessibility in the world of generative AI.
Featured models
| Model | Released | Context | Input price ($/1M) | Output price ($/1M) | License | Openness |
|---|---|---|---|---|---|---|
| Open-Assistant SFT-1 12B | 2023-04-15 | 2k | - | - | Apache 2.0 | Open source |
Model families
Recent releases
- Open-Assistant SFT-1 12B- 2023-04-15
FAQ
Who founded OpenAssistant and when?
OpenAssistant was founded in 2022 and is associated with N/A.
What models has OpenAssistant released?
OpenAssistant ships 1 model across 1 family: Open-Assistant.
Is OpenAssistant's technology open source?
All tracked models are released under Open Source.
Where is OpenAssistant headquartered?
OpenAssistant is headquartered in N/A.
What is OpenAssistant known for?
Open-source collaboration: democratizing AI. Its most prominent tracked family is Open-Assistant.
How can I access OpenAssistant's models?
OpenAssistant's models are available via Replicate API.
Explore related pages
Last reviewed: 2026-05-19. Data sourced from public lab announcements and provider documentation.