Ornith AI Releases Open-Source LLM Family Ornith-1.5, Spanning 9B, 35B, and 397B Parameters

Summary:Ornith AI has released Ornith-1.5, an open-source family of large language models (LLMs) that includes 9B dense, 35B Mixture-of-Experts (MoE), and 397B MoE models. Trained with self-improving strategies, Ornith-1.5 achieves state-of-the-art performance in several benchmarks, such as Terminal-Bench 2.1 (86.1), SWE-Bench (86 verified, 65.1 pro, 79.6 multilingual), DeepSWE (56), HLE (44.6), ClawEval (81.4), and Tool Decathlon (71.2). Its performance is comparable to Claude Opus 4.8, showcasing stro

---

A New Breakthrough in Open-Source LLMs

Ornith AI has introduced Ornith-1.5, a family of open-source large language models (LLMs) that includes:

• 9B Dense Model: Suitable for applications requiring high inference speed.
• 35B Mixture-of-Experts (MoE): Balances computational efficiency with enhanced model performance.
• 397B MoE Model: Designed for tasks demanding maximum model performance.

Technical Highlights

1. Self-Improving Training Strategy: Ornith-1.5 employs advanced self-improving training methods to continuously optimize model performance.
2. Leading in Multiple Benchmarks: The model excels in benchmarks such as Terminal-Bench 2.1, SWE-Bench, DeepSWE, HLE, ClawEval, and Tool Decathlon, particularly in multilingual processing and complex tasks.
3. Performance on Par with Top Models: Its overall performance is comparable to Claude Opus 4.8, showcasing significant advantages in reasoning, agency, and coding tasks.

Industry Impact

The release of Ornith-1.5 provides a powerful tool for the open-source community, especially for applications requiring high performance and multilingual support. For developers, the model family offers flexible options to choose the appropriate model version based on specific needs. Additionally, the release of Ornith-1.5 marks a new level of performance for open-source LLMs, providing new momentum for the adoption and application of AI technology.

Developer Recommendations

• Model Selection: Choose the appropriate model version based on the requirements of the application scenario, such as selecting the 9B dense model for applications requiring high inference speed.
• Continuous Optimization: Utilize the self-improving training strategy to continuously optimize model performance.
• Multilingual Support: Leverage the multilingual processing capabilities of Ornith-1.5 to expand application scope.

Conclusion

The release of Ornith-1.5 not only demonstrates Ornith AI's strong capabilities in the open-source LLM domain but also opens up new possibilities for the development and application of AI technology.

---

Source: Reddit r/LocalLLaMA (2026-08-19)

— END —

Tags: #Ornith AI #Open-Source Model #MoE Architecture #Large Language Model #Self-Improving Training