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What is Inkling-Small?

Inkling-Small is an efficient multimodal AI model built to deliver strong reasoning and coding performance at a fraction of Inkling’s size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters. The model was trained on NVIDIA GB300 NVL72 systems and is designed to combine high capability with more efficient inference. Inkling-Small supports native reasoning across text, images, and audio, allowing it to work across multimodal tasks without relying on separate encoders. Its context window supports up to one million tokens, making it useful for long-form reasoning, large-scale code understanding, document analysis, and agentic workflows. Users can adjust reasoning effort from minimal to extra high depending on whether they need faster responses or deeper computation. The model’s training process includes improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These techniques helped Inkling-Small outperform its larger counterpart on reasoning and coding benchmarks. The model performs well in coding and tool-use harnesses and exceeds 80% on SWE-bench Verified. Its encoder-free architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE design, one-million-token context, adjustable reasoning effort, multimodal processing, coding strength, and tool-use performance, Inkling-Small is designed for developers and teams that need capable AI with lower active compute requirements.

What is Baichuan-13B?

Baichuan-13B is a powerful language model featuring 13 billion parameters, created by Baichuan Intelligent as both an open-source and commercially accessible option, and it builds on the previous Baichuan-7B model. This new iteration has excelled in key benchmarks for both Chinese and English, surpassing other similarly sized models in performance. It offers two different pre-training configurations: Baichuan-13B-Base and Baichuan-13B-Chat. Significantly, Baichuan-13B increases its parameter count to 13 billion, utilizing the groundwork established by Baichuan-7B, and has been trained on an impressive 1.4 trillion tokens sourced from high-quality datasets, achieving a 40% increase in training data compared to LLaMA-13B. It stands out as the most comprehensively trained open-source model within the 13B parameter range. Furthermore, it is designed to be bilingual, supporting both Chinese and English, employs ALiBi positional encoding, and features a context window size of 4096 tokens, which provides it with the flexibility needed for a wide range of natural language processing tasks. This model's advancements mark a significant step forward in the capabilities of large language models.

Media

Media

Integrations Supported

Model Context Protocol (MCP)
Tinker

Integrations Supported

APIPark
C
C#
C++
CSS
Clojure
Elixir
F#
HTML
JavaScript
Julia
Kotlin
Python
R
Rust
Scala
TypeScript
Visual Basic

API Availability

Has API

API Availability

Pricing Information

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Thinking Machines Lab

Date Founded

2025

Company Location

United States

Company Website

thinkingmachines.ai/news/inkling-small/

Company Facts

Organization Name

Baichuan Intelligent Technology

Date Founded

1998

Company Location

China

Company Website

github.com/baichuan-inc/Baichuan-13B

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

AI Vision Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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