Inkling
Inkling is an open-weights multimodal Mixture-of-Experts model that reasons over text, images, and audio. It supports controllable thinking effort and a context window of 256K tokens. Call Inkling on AI Gateway with thinkingmachines/inkling. Your use is subject to Thinkingmachines's Terms & Privacy Policies.
import { streamText } from 'ai'
const result = streamText({ model: 'thinkingmachines/inkling', prompt: 'Why is the sky blue?'})About Inkling
Inkling became available on AI Gateway on July 15, 2026. Inkling is a decoder-only transformer with a sparse Mixture-of-Experts (MoE) backbone: 66 layers, 975 billion total parameters, and 41 billion active per token, with each token routed to 6 of 256 experts plus 2 shared experts. Attention mixes local and global layers, and the context window is 256K tokens. Thinkingmachines released the weights under the Apache 2.0 license.
Multimodality is native rather than bolted on. Images enter through a hierarchical patch encoder and audio through discrete token encoding, and both are processed jointly with text by the same decoder. Inkling accepts pixel-based images with each dimension between 40px and 4096px, and WAV audio sampled at 16kHz. Inkling transcribes speech, follows spoken instructions, and answers questions about recordings, scoring 91.4% on VoiceBench, 77.2% on MMAU, and 56.6% on Audio MC. On vision, Inkling scores 73.5% on MMMU Pro and 78.1% on CharXiv reasoning questions, rising to 82.0% when it uses a Python tool to zoom into and crop the image.
On agentic and reasoning evaluations at maximum effort, Inkling scores 77.6% on SWE-bench Verified, 54.3% on SWE-bench Pro (public), 63.8% on Terminal-Bench 2.1, 76.0% on MCP Atlas, and 45.5% on Toolathlon Verified. Reasoning results include 87.2% on GPQA Diamond, 97.1% on AIME 2026, and 29.7% on Humanity's Last Exam text-only, which rises to 46.0% with tools. Inkling scores 79.8% on IFBench for instruction following.
Controllable thinking effort is the setting you tune most. Raising effort spends more thinking tokens for higher scores, and lowering it returns answers sooner for less. Inkling reaches a given score at fewer thinking tokens than the open-weights models Thinkingmachines compared it against, so sweep the effort setting across a representative slice of your traffic before you fix a value.
Inkling also aims for calibrated confidence. It hedges or says it doesn't know rather than guessing, which helps in forecasting and in any workflow where a confident wrong answer costs more than an uncertain one.
Set the model to thinkingmachines/inkling in the AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python. AI Gateway serves Inkling through Baseten, Together AI, with retries and failover, and mirrors provider pricing with no markup and no platform fee on inference, including on Bring Your Own Key (BYOK) requests.