# Inkling-Small: 12B Active Beats Full Inkling on Coding—Lab Tables, Open Weights

Times of AI Desk · 2026-07-30 · Models

[https://timesof.ai/2026/07/thinking-machines-inkling-small](https://timesof.ai/2026/07/thinking-machines-inkling-small)

> Thinking Machines released Inkling-Small (276B/12B active open MoE) claiming SWE-Bench Verified 80.2% and ARC-AGI-2 40.1%—ahead of full Inkling—with vendor-cited output pricing of $1.20 per million tokens. Efficiency play for US open weights; treat coding wins as lab-reported without implying independent replication.

Open-weight labs are racing on **performance-per-active-parameter**, not only total-parameter headlines. Beating a 975B sibling on coding with **12B active** is a sharp efficiency signal—and a direct answer to Chinese open MoE cost pressure.

**Thinking Machines Lab** released **Inkling-Small** as **full open weights**—the efficient sibling previewed with **Inkling** on **July 15**. Specs: **276B total / 12B active** MoE, native multimodal (text, images, audio), up to **1M** context, variable thinking effort. Lab claims it **beats** full **Inkling** on agentic coding (**SWE-Bench Verified 80.2%** vs **77.6%**) and **ARC-AGI-2** (**40.1%** vs **36.5%**) while lagging on factual recall (**SimpleQA 20.6%** vs **43.9%**). The launch’s cost comparison uses **$1.20 per million output tokens**, against **$4.05** for Inkling. Weights on **Hugging Face**; fine-tune/chat on **Tinker**.

## What Inkling-Small is

| Spec | Detail (Thinking Machines primary) |
|------|-------------------------------------|
| **Architecture** | MoE transformer; encoder-free multimodal (images via hierarchical patch encoding; audio via dMel spectrograms) |
| **Scale** | **276B total / 12B active** (Inkling: 975B / 41B) |
| **Context** | Up to **1M** tokens |
| **Training note** | On-policy distillation from Inkling + ~two weeks extra **agentic-coding RL** after the Jul 15 preview checkpoint |
| **Hardware** | Trained on **NVIDIA GB300 NVL72** |
| **Access** | [Hugging Face weights](https://huggingface.co/thinkingmachines/Inkling-Small); Tinker playground + fine-tuning |

### Selected lab benchmarks (effort 0.99, temperature 1; primary tables)

| Benchmark | Inkling-Small | Inkling |
|-----------|---------------|---------|
| **SWE-Bench Verified** | **80.2%** | 77.6% |
| **ARC-AGI-2** | **40.1%** | 36.5% |
| **HLE text-only** | **31.6%** | 29.7% |
| **SimpleQA Verified** | **20.6%** | 43.9% |
| **AA Index v4.1** (lab table) | **40.0%** | 41.0 (vendor table) |
| **Terminal Bench 2.1** (internal harness) | **64.7%** | — |

Safety: StrongREJECT **98.4%**; FORTRESS adversarial **71.6%** / benign **96.9%** at effort 0.99 (primary table).

## Claims vs checks

The benchmark figures above are vendor model-card results. The card’s AA Index row is not an independent, dated AA placement, and this article does not establish a historical Arena rank. The smaller model’s coding and reasoning gains sit alongside a substantial SimpleQA Verified regression against Inkling.

Inkling-Small is a **new product-line tier** from the same lab: a small open MoE optimized for coding/agent cost curves.

## Limits

- Coding and ARC wins are vendor tables; no independent launch ranking is established here.
- SimpleQA regression (20.6% vs 43.9%) is the honest factuality trade.
- Terminal Bench figure uses an internal harness.

**Correction (October 14, 2026):** Removed unverified input pricing, launch-infrastructure and historical ranking claims; retained vendor benchmarks with their source and caveats.

## Sources

- [Thinking Machines: Inkling-Small launch (July 30, 2026)](https://thinkingmachines.ai/news/inkling-small/)
- [Thinking Machines: Inkling-Small model card (accessed October 14, 2026)](https://huggingface.co/thinkingmachines/Inkling-Small)