Inkling 2 Small opens on Tinker; Ripple offers downloadable coding weights
Thinking Machines launched Inkling 2 Small through Tinker with weights still planned, while Ricursive’s Apache-2.0 Ripple-65B Preview offers an immediately inspectable alternative for self-hosting experiments.
Inkling 2 Small arrived November 5 with image input, variable reasoning effort and a mixture-of-experts architecture of 594 billion total and 17 billion active parameters.
It is available for sampling and fine-tuning on Tinker. Thinking Machines says full Apache-2.0 weights are intended for the following week after external safety testing; downloadable fine-tuned checkpoints depend on that later release. That promise should not be entered into a deployment plan as a completed weight release. [1][2]
Ricursive’s Ripple-65B Preview already has an Apache-2.0 model repository, including instruction-tuned BF16 and NVFP4 options and a separate base checkpoint. Its dense 65-billion-parameter architecture offers a smaller total model to evaluate. Ricursive’s coding comparisons mix published peer scores with its own evaluations, and its card acknowledges harness-related reproduction difficulties. Total parameter count alone cannot rank capability or serving cost. [3][4]
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- Thinking Machines — Inkling 2 Small
November 5 hosted release and contingent planned Apache-2.0 weight release.
- Thinking Machines — model card
Model scope and limitations; same developer, no independent performance replication.
- Ricursive — Ripple launch
Developer's architecture, access and self-reported development-cost claims.
- Ricursive — Ripple model card
Public artifact, Apache-2.0 label, checkpoint formats and explicit harness-comparability caveat.
- Artificial Analysis — Inkling 2 Small evaluation
Independent evaluator confirms proprietary hosted launch with future weights; task results show uneven coding strengths and output-token costs.
Images & figures
Inkling 2 Small is available through Tinker while its weight release remains planned. Ripple-65B Preview already provides Apache-2.0 weight artifacts for self-hosting experiments.
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