---
title: "Inkling-Small"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/1758bdf0-23f9-438b-82f4-177b5f1b0873"
updated: "2026-11-12"
---

# Inkling-Small

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Reads text, images and audio and replies in text, for developers building assistants, coding tools and agents. A sparse mixture-of-experts model with 276B parameters in total and 12B active per token.

Inkling-Small is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by thinkingmachines

## About

### What it is

Inkling-Small is an open-weights multimodal model from Thinking Machines. It takes text, images and audio as input and writes its answers as text, in English and other languages and in several programming languages.

It is a sparse mixture-of-experts transformer with 42 layers. Of its 276B parameters, 12B are active for each token.

### Who it suits

Developers who build AI applications such as coding assistants, chatbots, agents that use tools and retrieval-augmented generation systems, and teams that fine-tune open models.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers of coding assistants | Run a general model for code and chat | Answer questions about a code file |
| Product teams | Builders of agents and chatbots | Use tool calling and instruction following | Run a support chatbot that reads screenshots |
| Data teams | Builders of RAG systems | Answer questions over retrieved text | Combine search results with a prompt |
| Research | Fine-tuning researchers | Adapt open weights to a domain | Fine-tune on in-house data |

Skip it if you have no server-class hardware, since the weights are about 532 GB in BF16 and about 133 GB at 4-bit by estimate.

### Where to use it

- Tinker Playground, a hosted page to try the model
- The Tinker Cookbook, which the card points to for API access
- Local serving with SGLang, vLLM, TokenSpeed, Unsloth or Hugging Face, each with its own recipe
- Third-party inference providers, which the card says also offer API access
- Windows, macOS or Linux with Python, to download the files

| Use case | Who | What they get back |
| --- | --- | --- |
| Coding help | Developers | Text answers and code in several programming languages |
| Image questions | App builders | Text answers about a picture you supply |
| Audio understanding | Product teams | Text output based on a WAV recording |

### How to set it up

- Python and pip to run the download commands
- Server-class GPU memory for local serving, or an account with a provider
- One of the serving libraries the card lists, if you run it yourself

1. Install the Hugging Face tools.
2. Download the model files, or pick the NVFP4 build for a smaller footprint.
3. Choose a serving library from the card and follow its recipe.
4. Or skip local hosting and try the Playground or an inference provider.

```bash
pip install -U huggingface_hub
hf download thinkingmachines/Inkling-Small
```

### How to use it

Send text, images or audio to the served model and read back text. The card gives size and length limits for images and audio that give the best results.

| Input | Format and limits |
| --- | --- |
| Text | UTF-8 text |
| Image | Any pixel-based image, with each side between 40 and 4096 pixels for best results |
| Audio | WAV at 16 kHz, ideally under 2 minutes |

### Example

You serve the model with one of the listed libraries and send a photo of a chart together with a question about it. The model replies in text with its reading of the chart.

### For newcomers and experts

If you are new to large models, try the hosted Playground first to see how it handles text and images, before planning any local setup. Local serving needs a lot of GPU memory.

If you know the field, a router sends each token to 6 of 256 experts, and 2 shared experts run on every token. Attention mixes local and global layers, images go through a hierarchical patch encoder, and audio is encoded as discrete tokens, all sharing one hidden space.

Text, image or audio input → Modality encoders feed one shared hidden space → Sparse MoE decoder routes each token → Text output

### Good to know

- The model type is a multimodal autoregressive transformer, and it generates text only
- The numerics supported are BF16 and NVFP4
- Hugging Face lists 54 quantized versions made by other people
- The shown part of the card has a benchmark table against open and closed weights models, but its numbers are cut off here

Parameters in billions

| Label | Value |
| --- | --- |
| Total parameters | 276 billion |
| Active per token | 12 billion |

- License: Apache 2.0, and the card also links an acceptable use policy
- Cost to run: none named for the weights, though the Playground and provider APIs are separate services and need your own server-class hardware if you self-host
- Data it reaches: the text, images and audio you send it
- Not for: output other than text, since it generates text only

## README

````markdown
# Inkling-Small

Reads text, images and audio and replies in text, for developers building assistants, coding tools and agents. A sparse mixture-of-experts model with 276B parameters in total and 12B active per token.

Made by thinkingmachines on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: Inkling-Small is an open-weights multimodal model from Thinking Machines. It takes text, images and audio as input and writes its answers as text, in English and other languages and in several programming languages.

It is a sparse mixture-of-experts transformer with 42 layers. Of its 276B parameters, 12B are active for each token.

Who it suits: Developers who build AI applications such as coding assistants, chatbots, agents that use tools and retrieval-augmented generation systems, and teams that fine-tune open models.

```table
Industry | Who uses it | How they use it | Example
Software | Developers of coding assistants | Run a general model for code and chat | Answer questions about a code file
Product teams | Builders of agents and chatbots | Use tool calling and instruction following | Run a support chatbot that reads screenshots
Data teams | Builders of RAG systems | Answer questions over retrieved text | Combine search results with a prompt
Research | Fine-tuning researchers | Adapt open weights to a domain | Fine-tune on in-house data
```

Skip it if you have no server-class hardware, since the weights are about 532 GB in BF16 and about 133 GB at 4-bit by estimate.

Where to use it:

- Tinker Playground, a hosted page to try the model
- The Tinker Cookbook, which the card points to for API access
- Local serving with SGLang, vLLM, TokenSpeed, Unsloth or Hugging Face, each with its own recipe
- Third-party inference providers, which the card says also offer API access
- Windows, macOS or Linux with Python, to download the files

```table
Use case | Who | What they get back
Coding help | Developers | Text answers and code in several programming languages
Image questions | App builders | Text answers about a picture you supply
Audio understanding | Product teams | Text output based on a WAV recording
```

How to set it up:

- Python and pip to run the download commands
- Server-class GPU memory for local serving, or an account with a provider
- One of the serving libraries the card lists, if you run it yourself

1. Install the Hugging Face tools.
2. Download the model files, or pick the NVFP4 build for a smaller footprint.
3. Choose a serving library from the card and follow its recipe.
4. Or skip local hosting and try the Playground or an inference provider.

```bash
pip install -U huggingface_hub
hf download thinkingmachines/Inkling-Small
```

How to use it: Send text, images or audio to the served model and read back text. The card gives size and length limits for images and audio that give the best results.

```table
Input | Format and limits
Text | UTF-8 text
Image | Any pixel-based image, with each side between 40 and 4096 pixels for best results
Audio | WAV at 16 kHz, ideally under 2 minutes
```

Example: You serve the model with one of the listed libraries and send a photo of a chart together with a question about it. The model replies in text with its reading of the chart.

For newcomers and experts: If you are new to large models, try the hosted Playground first to see how it handles text and images, before planning any local setup. Local serving needs a lot of GPU memory.

If you know the field, a router sends each token to 6 of 256 experts, and 2 shared experts run on every token. Attention mixes local and global layers, images go through a hierarchical patch encoder, and audio is encoded as discrete tokens, all sharing one hidden space.

```flow
Text, image or audio input
Modality encoders feed one shared hidden space
Sparse MoE decoder routes each token
Text output
```

Good to know:

- The model type is a multimodal autoregressive transformer, and it generates text only
- The numerics supported are BF16 and NVFP4
- Hugging Face lists 54 quantized versions made by other people
- The shown part of the card has a benchmark table against open and closed weights models, but its numbers are cut off here

```chart
Title: Parameters in billions
Total parameters: 276 billion
Active per token: 12 billion
```

- License: Apache 2.0, and the card also links an acceptable use policy
- Cost to run: none named for the weights, though the Playground and provider APIs are separate services and need your own server-class hardware if you self-host
- Data it reaches: the text, images and audio you send it
- Not for: output other than text, since it generates text only

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 266B
- Weights: about 532 GB in BF16; about 133 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 1,114,076
- Likes: 456
- License: apache-2.0
- Quantized versions: 54
- Fine-tunes: 7
- Tags: transformers, safetensors, inkling_mm_model, image-text-to-text, conversational, audio-text-to-text

## Links

- Model: https://huggingface.co/thinkingmachines/Inkling-Small
- Files: https://huggingface.co/thinkingmachines/Inkling-Small/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download thinkingmachines/Inkling-Small
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[thinkingmachines/Inkling-Small on Hugging Face](https://huggingface.co/thinkingmachines/Inkling-Small)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| Inkling-Small (this page) | 1,114,076 | 456 | 266B | apache-2.0 | 2026-07-31 |
| [Inkling](https://mindket.com/p/bfdceccb-bf8a-4f03-a2a8-5e5a771a7f2b) | 1,583,506 | 1,847 | 952B | apache-2.0 | 2026-07-23 |
| [Inkling-Small-NVFP4](https://mindket.com/p/adb50413-d354-4cc4-b25b-ece0feab8a50) | 367,272 | 93 | 156B | apache-2.0 | 2026-07-30 |
| [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 |
| [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 |
| [GLM-5.3-Flash](https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1) | 6,397,774 | 3,138 | 321B | mit | 2026-10-15 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · meetpateltech · 2026-07-30](https://news.ycombinator.com/item?id=49113287): Inkling-Small
- [Bluesky · philpax · 2026-07-30](https://bsky.app/profile/philpax.me/post/3mruy2arhu22e): new Thinky drop huggingface.co/thinkingmach...
- [Hacker News · Philpax · 2026-07-30](https://news.ycombinator.com/item?id=49113099): thinkingmachines/Inkling-Small
- [Bluesky · 🚀 olud.ai · 2026-08-02](https://bsky.app/profile/oludai.bsky.social/post/3ms3pzyxrnl2d): 🧬 Just added to the tracker: Inkling Small (Thinkingmachines) Context: 524.3k tokens · $0.5 in / $1.2 out per 1M · open weights All the latest models, tracked hourly: https://olud.ai/latest.html \#AI \#LLM \#OpenSource
- [Hacker News · tosh · 2026-07-30](https://news.ycombinator.com/item?id=49113771): Thinking Machines: Inkling Small
- [Bluesky · 🚀 olud.ai · 2026-08-06](https://bsky.app/profile/oludai.bsky.social/post/3msgjdaxv5w25): Inkling Small (Thinkingmachines) offers 524.3k tokens with open weights at $0.5 in/$1.2 out—pricing that challenges closed alternatives for long-context tasks. https://olud.ai/latest.html \#AI \#LLM \#OpenSource
- [Hacker News · nekitamo · 2026-10-24](https://news.ycombinator.com/item?id=50220395): Root shell on Samsung TV: The Qwen3.8-Flash-Next model is ok for reverse engineering. It can definitely solve simple tasks, but it gets easily confused, and burns tokens like crazy. I've been sick with stomach flu the past few days, so I put together a small benchmark for all the top AI models where I ask them to reverse…
- [Bluesky · @hossamudin.bsky.social · 2026-11-09](https://bsky.app/profile/hossamudin.bsky.social/post/3mzv7p3jb5r2z): موديل حجمه 594 مليار معلمة… بس بيشتغل بـ 17 مليار بس في كل خطوة 🤯 Thinking Machines Lab، شركة ميرا موراتي اللي كانت مديرة التكنولوجيا في OpenAI، أطلقت موديلها الجديد Inkling 2 Small. \#خبير\_اونلاين \#InklingSmall \#ThinkingMachines \#الذكاء\_الاصطناعي \#مفتوح\_المصدر \#AIAgents \#برمجة
- [Hacker News · SlavikCA · 2026-07-30](https://news.ycombinator.com/item?id=49115315): Inkling-Small: About the same size as DeepSeek Flash 4, but also supports audio and image input: - 276B parameters - 12B active parameters Looks like not yet available on Open Router: https://openrouter.ai/thinkingmachines/inkling-small
- [Bluesky · 🚀 olud.ai · 2026-08-10](https://bsky.app/profile/oludai.bsky.social/post/3msq5svm2nd2p): 📦 Inkling Small (Thinkingmachines) hits the leaderboard: 524k context, open weights, $0.45/$1.2 per 1M. That context at that price stands out. See the hourly updated leaderboard for how it stacks up. https://olud.ai/latest.html \#AI \#LLM \#OpenSource
- [Hacker News · wxw · 2026-07-15](https://news.ycombinator.com/item?id=48926769): Inkling: Our Open-Weights Model: \> Inkling is not the strongest overall model available today, open or closed. Instead, a combination of qualities makes it a good open-weights base for customization: multimodal capabilities, efficient thinking, and availability on Tinker for fine-tuning. Open base models that can be fine tuned on…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-08-06](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3msfynfguya2a): 今日のHuggingFaceトレンド thinkingmachines/Inkling-Small Inklingはテキスト、画像、音声を入力しテキストを出力する汎用マルチモーダルモデルです。 AIエージェント、コーディングアシスタント、チャットボットなどのAIアプリケーション開発を目的としています。 オープンウェイトで提供されており、研究やファインチューニング、外部製品への統合に利用可能です。

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-07-31
- First published: 2026-07-27
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open thinkingmachines/Inkling-Small on Hugging Face](https://huggingface.co/thinkingmachines/Inkling-Small)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1758bdf0-23f9-438b-82f4-177b5f1b0873)
- [Its page on Mindket](https://mindket.com/p/1758bdf0-23f9-438b-82f4-177b5f1b0873)