# Inkling-Small — Thinking Machines' 276B open model matches Inkling at 1/4 the size

> A 276B MoE with 12B active parameters that ships full Apache-2.0 weights and matches its 4x-larger sibling.

- Org: Thinking Machines Lab
- Date: 2026-07-30
- Category: model
- Source: https://thinkingmachines.ai/news/inkling-small/
- Canonical page: https://ai-tldr.dev/releases/thinking-machines-inkling-small/

## In short

Thinking Machines released Inkling-Small under Apache 2.0: a 276B-parameter MoE with 12B active, 1M-token context, and multimodal inputs. Scores 80.2% on SWE-bench Verified and 95.5% on AIME 2026.

## What it is

Inkling-Small is a mixture-of-experts model with 276B total parameters and 12B active per token, released under Apache 2.0 with full weights on Hugging Face. It handles text, image, and audio input, has a 1M-token context window, and reports variable thinking effort at inference time.

## How it works

The architecture uses a 42-layer decoder with 256 experts, routing each token to six specialised experts plus two shared ones. Encoder-free multimodal handling relies on dMel spectrograms for audio and 40×40 pixel patches for vision. Training used NVIDIA GB300 NVL72 systems, and weights ship in BF16 and NVFP4 formats.

## Why it matters

Inkling-Small closes most of the gap to the 975B Inkling — 80.2% vs 77.6% on SWE-bench Verified and 64.7% vs 63.8% on Terminal Bench 2.1 — at roughly a quarter of the size. That makes a frontier-class multimodal reasoning model runnable on a single 128 GB Mac Studio via Unsloth's GGUF quantisation, and cheap enough on Tinker ($1.20 per 1M output tokens) for teams to fine-tune on private data.

## Who it's for

developers wanting a top-tier open MoE they can fine-tune or self-host

## Try it

huggingface.co/thinkingmachines/Inkling-Small

## Quick facts

- Maker: Thinking Machines Lab
- Parameters: 276B total, 12B active (MoE)
- Architecture: 42-layer decoder, 256 experts (6+2 routing)
- Context window: 1M tokens
- Inputs: Text, image, audio
- License: Apache 2.0 (open weights)
- Availability: Hugging Face, Tinker Playground, Tinker API fine-tuning

## FAQ

### How does Inkling-Small compare to the full Inkling model?

Inkling-Small matches Inkling on most benchmarks despite having a quarter of the total parameters (276B vs 975B). It even edges Inkling on SWE-bench Verified (80.2% vs 77.6%) and Terminal Bench 2.1 (64.7% vs 63.8%), while trailing slightly on factuality. The full model still leads on the Artificial Analysis Intelligence Index (41 vs 40).

### Where can I run Inkling-Small locally?

Inkling-Small weights are on Hugging Face under `thinkingmachines/Inkling-Small` (BF16) and `thinkingmachines/Inkling-Small-NVFP4` (4-bit). Unsloth has published GGUF conversions that run locally on 128 GB RAM. Hugging Face Inference Endpoints achieves up to 160 tokens/sec on the reference stack.

### How much does the Tinker API cost for Inkling-Small?

Inkling-Small runs on the Tinker API at $1.20 per 1M output tokens. Fine-tuning on your own data is available through the same API, and chat with text, image, and audio inputs is free in the Tinker Playground.

### Is Inkling-Small really the strongest open model in its size class?

Thinking Machines and Artificial Analysis both call it the leading US open-weights release for its size — it scores 40 on the AA Intelligence Index and beats similarly-sized peers on SWE-bench, AIME, GPQA Diamond, and MMMU Pro. Larger open models like Kimi K3 (2.8T) still top the raw leaderboard.

## Links

- [Announcement](https://thinkingmachines.ai/news/inkling-small/)
- [Hugging Face](https://huggingface.co/thinkingmachines/Inkling-Small)
- [HF Blog](https://huggingface.co/blog/thinkingmachines-inkling)
- [Artificial Analysis](https://artificialanalysis.ai/articles/thinking-machines-has-released-inkling-the-new-leading-u-s-open-weights-model)