Inkling
inkling- 🏢 Thinking Machines Lab's first open-weights model.
- 🧠 Sparse Mixture-of-Experts: 975B total, 41B active parameters.
- 📏 Up to 1M-token context window.
- 👁️ Natively accepts text, image, and audio inputs; outputs text.
- 🔧 66 layers with hybrid local/global attention.
- ⚡ Controllable "thinking effort" balances speed against reasoning depth.
- 🎯 Built for chat, coding, tool use, and agentic workflows.
- 🔒 Apache 2.0 license.
Thinking Machines is an AI research organization whose work centers on large language models. Its focus lies in building capable text-based systems, contributing to the growing field of generative AI.
Inkling is Thinking Machines Lab's debut model — released with open weights under an Apache 2.0 license. Because it is a first-generation model, it has no same-family predecessors to compare against; the lab positions it as a broad, general-purpose multimodal generalist meant to serve as a customizable base for a range of tasks.
Architecturally, Inkling is a 66-layer sparse Mixture-of-Experts model. Routing activates only a fraction of its experts per token, giving 975B total parameters but roughly 41B active per token, which keeps inference more efficient than a comparably sized dense model. Attention alternates between local and global layers, and a quantized NVFP4 variant is also published.
Multimodality is native: the model accepts text, image, and audio inputs and generates text, with all modalities processed jointly by the decoder. On Venice, video input is not supported, per the catalog description. The 1M-token context window suits long documents, extended conversations, and agentic workflows.
A defining feature is variable, controllable thinking effort, letting developers trade latency for reasoning depth depending on task complexity. The model is aimed at chat, coding, tool use, and function calling, and the lab frames it as a foundation intended for domain adaptation and fine-tuning rather than a narrowly optimized specialist.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Inkling · Organic sellers
Top sellers · Inkling · Organic
- ▲ Apex Ant39.9%
- Infersmith36.0%
- Fire Ant 🔥🐜17.8%
- JOI SEED6.2%
- Sapiens0.1%
| Seller | Rep↓ | Sales | Tokens | Routing | Input $/M | Cached $/M | Output $/M | Save |
|---|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | 81.71 | $0.03 | 29.09k | #1 | $0.72 | $0.12 | $2.23 | −28% |
| Infersmith 0xac9f…7a6c | 0.21 | $0.03 | 48.99k | gated | $0.95 | $0.16 | $3.89 | −5% |
| Joi Gateway 0x8a90…7007 | 0.00 | — | — | gated | $0.488 | $0.488 | $1.9764 | −51% |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.