Murati's Thinking Machines launches Inkling, 975B-param model leading US labs but trailing China

Thinking Machines Lab's release of Inkling marks Mira Murati's first major product launch since leaving OpenAI. The model reflects a strategic choice to compete on efficiency and task-specific adaptation rather than raw performance—a positioning that acknowledges the reality of the open-weights landscape. On the Artificial Analysis Intelligence Index, Inkling's score of 41 places it as the leading U.S. open-weights model, ahead of Nemotron 3 Ultra (38), signaling that Murati's team has succeeded in building a competitive system for American labs. However, the benchmarks also reveal the ceiling: Chinese models like Kimi K2.6 and DeepSeek v4 Flash max outperform Inkling on agent-based knowledge-work tasks (GDPval-AA v2), and Inkling's accuracy score of +2 on AA Omniscience with a 63 percent hallucination rate suggests it is less suitable for applications requiring high factual reliability.

The company's business model—positioning Inkling as a foundation for fine-tuning through the Tinker platform rather than as an all-purpose contender—mirrors a broader market shift toward customizable models. That approach is underscored by the model's multimodal support and million-token context window, both designed to enable flexible adaptation. The pricing structure, at $1.87 per million input tokens for a 64K window, sits slightly above comparable Chinese open-source models but benefits from Inkling's efficiency: it averages just 25,000 output tokens per Intelligence Index task, substantially fewer than GLM-5.2 max (43,000) or Kimi K2.6 (38,000). Thinking Machines' preview of Inkling-Small, which beats the larger model on some benchmarks like GPQA Diamond, further suggests the team is prioritizing practical utility and efficiency over headline performance metrics.