Inkling-Small

Inkling-Small is a general-purpose multimodal model from Thinking Machines. It accepts text, image and audio inputs and generates text. It is a sparse Mixture-of-Experts model with 276B total parameters and 12B activated per token, built for developers making agentic and tool-use systems, coding assistants, chatbots and retrieval-augmented generation applications.

Overview

Inkling-Small is a 42-layer decoder-only transformer with a sparse Mixture-of-Experts feed-forward backbone. Each token is routed to 6 of 256 experts, plus 2 shared experts that are active on every token. Attention is a hybrid of local and global layers. The model is natively multimodal: images are encoded with a hierarchical patch encoder and audio through discrete token encoding, and all modalities are projected into the same model. This template serves the official NVFP4 checkpoint published by Thinking Machines.

Key Features

• Text, image and audio input with text output
• Sparse MoE routing that activates 12B of 276B parameters per token
• Hybrid local and global attention with a context window of up to 1M tokens
• Reasoning and tool calling for agentic workflows
• English, with general multilingual capability and broad coverage of programming languages

Inputs

Images can be any pixel-based input; Thinking Machines recommends keeping each dimension between 40 and 4096 pixels. Audio is WAV at 16 kHz, ideally under two minutes per clip.

Benchmarks

Thinking Machines reports Inkling-Small at 80.2% on SWE-bench Verified (bash-only harness), 55.9% on SWE-Bench Pro (public), 64.7% on Terminal-Bench 2.1, 79.6% on MCP Atlas (public) and 77.4% on BrowseComp with context management. On reasoning it scores 89.5% on GPQA Diamond, 95.5% on AIME 2026, 90.2% on HMMT February 2026 and 31.6% on Humanity's Last Exam (text only, 47.8% with tools). On instruction following it reaches 82.2% on IFBench. Multimodal results include 74.0% on MMMU Pro, 77.0% on MMAU and 90.1% on VoiceBench. The model card compares these results against open-weight models including Qwen3.5 397B-A17B, MiMo V2.5, MiniMax M2.7 and DeepSeek V4 Flash.

Use Cases

• Coding assistants and autonomous software engineering agents
• Tool-calling and browsing agents
• Chat and retrieval-augmented generation over long documents
• Understanding images, charts and screenshots
• Answering questions about speech and other audio

For full model details, evaluation notes and safety information, see the model card on Hugging Face.