Thinking Machines Releases Inkling - Their First Model with Open Weights

Thinking Machines has introduced Inkling, their first model with open weights, featuring 975 billion parameters and supporting text, images, and audio.
Thinking Machines Releases Inkling
Thinking Machines has launched its first model with open weights - Inkling. The model boasts 975 billion parameters, of which 41 billion are active, and can process text, images, and audio.
Access to the model is provided via the Tinker platform API, with a context window of 256,000 tokens. The model weights are available on Hugging Face, supporting context up to 1 million tokens.
Results and Ratings
In the Artificial Analysis Intelligence Index, Inkling scored 41 points, becoming the leading American model with open weights, surpassing Nemotron 3 Ultra, which scored 38 points.
Customization and Fine-tuning
Inkling is specifically designed for customization and fine-tuning through Tinker, allowing it to be adapted to individual products and workflows.
Pricing
- Cost per 1 million tokens with a context window of 64,000:
$1.87 for input tokens / $0.374 for cached / $4.68 for output - Cost per 1 million tokens with a context window of 256,000:
$3.74 for input tokens / $0.748 for cached / $9.36 for output
Why it matters
AnalysisInkling represents a significant advancement in the development of AI models with open weights, enabling developers to tailor technologies to their specific needs. This opens up new possibilities for customizing AI solutions.
Discuss in community
Share your questions and insights with developers
Related in Ecosystem
Practical guides, core terminology, and agent skills
MIT researchers show GPT-6 Astra can improve other AI models by editing their weights
In an experiment, Astra raised Qwen3.5-0.8B's accuracy on MATH-500 from 7.9% to 48.1% by changing only 0.016% of the model's weights.
Four Resume Tips from OpenAI’s Head of Applied AI Development
Wulfie Bain, OpenAI’s head of applied AI development, shared four resume rules: keep it short, remove irrelevant details, highlight outcomes, and lead with the strongest achievements.
JSON — Structured Data for Programs, APIs, and AI Agents
A complete hands-on guide to JSON: syntax specifications, serialization via parse and stringify, REST API consumption, JSON Schema validation, and structured outputs for LLMs.
DOCX and XLSX Formats: When AI Needs to Deliver Production-Ready Documents
A comprehensive hands-on guide to generating and processing DOCX documents and XLSX workbooks with AI: comparing against Markdown and CSV, prompt engineering, formula design, and code automation.