RWKV Project
8 models across 2 families · Latest: RWKV-7 Goose 2.9B (2025-03)
Linear complexity language models combining the efficiency of RNNs with the parallelism of Transformers
RWKV Project's portfolio covers 8 active models across 2 current families, spanning long context. Open a model detail page to compare provider routes and sourced benchmarks.
Covers 1 workload area across 8 active tracked models; last verified 2026-06-29.
Use it for
- Teams evaluating long context across this lab's releases
- Comparing model families before committing to a flagship
- Migration and pricing follow-ups across 8 tracked models
Do not use it for
- Choosing a hosting provider without opening a model page for price ladders
Active models
8
Current models from this lab, excluding deprecated ones
Active families
2
Current model families from this lab
Open catalog
8 open
8 open source / 0 open weights
Lowest output price
Not tracked
No provider output pricing linked yet
Latest dated release
2025-03-18
RWKV-7 Goose 2.9B
Freshness
2026-06-29
Researched 10d ago
Information
Release cadence
Showing 5 recent dated releases (full timeline below). Latest: RWKV-7 Goose 2.9B (2025-03-18).
Where this lab wins
- Long-context: 8 tracked models with context-token or InfiniteBench-class signal.
Flagship quality / price signal
Flagship: RWKV-6 Finch 14B (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.
RWKV Project is an AI research organization. Linear complexity language models combining the efficiency of RNNs with the parallelism of Transformers. RWKV Project ships 2 model families totaling 8 models, with the most recent release RWKV-7 Goose 2.9B in 2025-03. Notable families include RWKV-7 Goose and RWKV-6 Finch. Use it as a stable reference for lab background, release coverage, and follow-up model. View official API endpoints, benchmark performance, and coding/agent fit for every RWKV Project model.
About
The RWKV Project, maintained under the Linux Foundation AI & Data Foundation and led by Bo Peng, develops the RWKV (Receptance Weighted Key Value) family of language models. RWKV is a pure recurrent architecture that achieves linear O(n) time complexity during training and O(1) constant-memory inference — unlike Transformers which require quadratic attention and growing KV caches. The architecture has progressed through major versions: RWKV-4 (Dove, 2023), RWKV-5 (Eagle), RWKV-6 (Finch, 2024), RWKV-7 (Goose, 2025), and experimental RWKV-8 (Heron). All production models are released under Apache 2.0. The World series models are trained on multilingual corpora covering 100+ languages.
Featured models
| Model | Released | Context | Input price ($/1M) | Output price ($/1M) | License | Openness |
|---|---|---|---|---|---|---|
| RWKV-7 Goose 2.9B | 2025-03-18 | Infinite | - | - | Apache 2.0 | Open source |
| RWKV-7 Goose 1.5B | 2025-03-18 | Infinite | - | - | Apache 2.0 | Open source |
| RWKV-7 Goose 0.4B | 2025-03-18 | Infinite | - | - | Apache 2.0 | Open source |
Model families
Recent releases
- RWKV-7 Goose 2.9B- 2025-03-18
- RWKV-7 Goose 1.5B- 2025-03-18
- RWKV-7 Goose 0.4B- 2025-03-18
- RWKV-7 Goose 0.1B- 2025-03-18
- RWKV-6 Finch 14B- 2024-09-03
FAQ
What models has RWKV Project released?
RWKV Project ships 8 models across 2 families: RWKV-7 Goose and RWKV-6 Finch.
Is RWKV Project's technology open source?
All tracked models are released under Apache 2.0.
Where is RWKV Project headquartered?
RWKV Project is headquartered in Global / Linux Foundation AI & Data.
What is RWKV Project known for?
Linear complexity language models combining the efficiency of RNNs with the parallelism of Transformers. Its most prominent tracked family is RWKV-7 Goose.
How can I access RWKV Project's models?
RWKV Project's provider availability is tracked on model pages as API and hosting data is verified.
Explore related pages
Last reviewed: 2026-06-29. Data sourced from public lab announcements and provider documentation.