Inkling

1. General Information

Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.

Languages: English, with general multilingual capabilities across other languages.

2. Getting Started

Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook.

Inkling supports local deployment using the following open-source libraries:

• SGLang (recipe)
• vLLM (recipe)
• TokenSpeed (recipe)
• Huggingface (recipe)

API access is also available through third party inference providers.

3. Model Properties

Model type

Multimodal autoregressive transformer

Architecture type

A 66-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) feed-forward backbone: each token is routed to 6 of 256 experts, plus 2 shared experts active on every token. Attention is a hybrid of local and global layers. The model is natively multimodal — images and video are encoded via a hierarchical patch encoder, and audio via discrete token encoding — with all modalities projected into a shared hidden space and processed jointly by the decoder.

Parameters

975B total, 41B active

Numerics support

BF16 and NVFP4

Input modalities

Inkling accepts text input in UTF-8 encoding, image input in any pixel-based format (with each dimension ideally between 40px and 4096px for optimal performance), and audio input in WAV format sampled at 16kHz (ideally under 20 minutes in length for optimal performance).

Output modalities

Inkling generates output as UTF-8 encoded text.

4. Training

Training data includes a broad variety of content types, including text, images, audio, video. Training data for the model was drawn from publicly available sources, acquired from third-parties, or synthetically generated or augmented. Publicly available data includes content from the public internet and publicly accessible repositories.

The training data curation process includes cleaning, processing, and modifying datasets. These processing steps, which vary by data type, may include deduplication and filtering to remove junk or other low-quality data, or to advance safety or other objectives.

5. Evaluations

Inkling results are reported at effort=0.99. Comparison scores are generated Jul 14, 2026. Nemotron 3 Ultra, Kimi K2.5, Kimi K2.6, GLM 5.2, and DeepSeek V4 Pro are open weights models; Gemini 3.1 Pro, Claude Fable 5, and GPT 5.6 Sol are closed weights models.