Thinking Machines Launches Inkling Small: Open Source AI Model Delivers 1/4 Size with Near-Identical Performance

Thinking Machines Advances Open Source AI with Efficient Inkling Small Model

Just weeks after launching Inkling, its inaugural open source AI language model, Thinking Machines—the startup led by former OpenAI Chief Technology Officer Mira Murati—has already released a significant upgrade. The new Inkling Small model demonstrates that bigger isn't always better in the AI world, achieving near-identical performance to its predecessor while operating at just one-quarter the size.

This rapid iteration underscores the competitive pace of the open source AI movement and signals an important shift in how AI models are being developed and deployed across industries.

What This Development Means

The release of Inkling Small represents a breakthrough in model efficiency—a critical challenge in AI development. Traditionally, larger models have been associated with better performance, but Thinking Machines' achievement challenges this assumption. By reducing the model size to 25% of the original while maintaining comparable output quality, the company has created a more practical tool for real-world deployment.

This efficiency gain matters because smaller models offer tangible advantages:

• Lower computational costs: Reduced resource requirements mean cheaper API calls and server infrastructure
• Faster inference: Quicker response times for end users and applications
• Easier deployment: Models that can run on consumer-grade hardware and edge devices
• Reduced environmental impact: Less energy consumption during training and inference

Why This Matters for AI Tool Users

For developers, product teams, and organizations evaluating AI tools, Inkling Small presents a compelling option in an increasingly crowded marketplace. Open source models have democratized access to advanced AI capabilities, but they've traditionally required significant technical expertise and infrastructure investment. A compact model that doesn't sacrifice performance changes that equation.

Users considering AI solutions can now choose between proprietary closed-source models and increasingly capable open source alternatives. The Inkling Small release strengthens the open source case, particularly for budget-conscious teams, startups, and organizations prioritizing data privacy through self-hosted solutions.

The speed of innovation from Thinking Machines—launching a major improvement within weeks—also demonstrates the velocity of open source AI development compared to traditional software cycles.

Broader Implications for the AI Landscape

Thinking Machines' achievement reflects a larger trend: model efficiency is becoming competitive differentiation. As the AI tools market matures, companies can no longer rely solely on raw capability. Practical considerations like cost, speed, and deployability are moving to the forefront.

This development also highlights the importance of leadership in emerging AI companies. With former OpenAI CTO Mira Murati at the helm, Thinking Machines brings deep technical expertise and credibility, positioning the startup to compete effectively in the open source AI ecosystem dominated by projects from Meta, Mistral, and others.

The release signals that open source AI models are maturing into production-ready tools rather than experimental projects. Organizations can increasingly rely on open source solutions as genuine alternatives to commercial offerings from OpenAI, Google, and Anthropic.

The Bottom Line

Inkling Small represents more than just a technical achievement—it's a validation that efficient, capable open source AI models are both possible and practical. For AI tool users, this means more choices, lower costs, and greater flexibility in how they deploy language models. For the broader AI landscape, it reinforces that the future likely includes a healthy mix of commercial and open source solutions, with efficiency and performance as key battlegrounds.

Story sourced from VentureBeat