Olmo 3 introduces advanced open language models with 7B and 32B parameters, focusing on tasks like long-context reasoning and coding. The release details the complete model lifecycle, including all stages and dependencies. The standout model, Olmo 3 Think 32B, claims to be the most capable open thinking model available.

This article discusses a study analyzing over 100 trillion tokens of AI usage from OpenRouter. It highlights a shift towards multi-step, agentic workflows in AI applications, emphasizing the growing importance of reasoning and tool integration in developer practices.

Kimi K2 Thinking is a powerful open-source AI model with 1 trillion parameters designed for reasoning, coding, and writing tasks. It competes with top models like GPT-5 and Claude Sonnet 4.5, and can be integrated with any OpenAI client by changing the API key. The article includes usage examples and deployment information.

Qwen3-235B-A22B-Thinking-2507 showcases significant advancements in reasoning capabilities, achieving state-of-the-art performance in various tasks such as logical reasoning and coding. With enhanced long-context understanding and improved general capabilities, this model is recommended for complex reasoning tasks and supports ultra-long text processing through innovative techniques.

Kimi-VL is an open-source Mixture-of-Experts vision-language model that excels in multimodal reasoning and long-context understanding with only 2.8B activated parameters. It demonstrates superior performance in various tasks such as multi-turn interactions, video comprehension, and mathematical reasoning, competing effectively with larger models while maintaining efficiency. The latest variant, Kimi-VL-A3B-Thinking-2506, enhances reasoning and visual perception capabilities, achieving state-of-the-art results in several benchmarks.

XBai o4 is the latest fourth-generation open-source large model technology, showcasing enhanced complex reasoning capabilities that surpass OpenAI-o3-mini in Medium mode. It employs a novel reflective generative training form to significantly reduce inference costs and improve response quality. The repository includes training and evaluation code, along with instructions for setup and benchmarks.

InternVL3.5 introduces a new family of open-source multimodal models that enhance versatility, reasoning capabilities, and inference efficiency. A key innovation is the Cascade Reinforcement Learning framework, which improves reasoning tasks significantly while a Visual Resolution Router optimizes visual token resolution. The model achieves notable performance gains and supports advanced capabilities like GUI interaction and embodied agency, positioning it competitively against leading commercial models.