reasoning model

5 articles tagged with reasoning model

August 11, 2026
model releaseSakana Ai

Sakana AI Releases Namazu, a Japanese-Specialized Reasoning Model Built on Kimi K2.6

Sakana AI has released Namazu, a reasoning model built on Kimi K2.6 and fine-tuned for Japanese language and business contexts. The model offers a 262K token context window at $0.95 per 1M input tokens and $4 per 1M output tokens.

August 3, 2026
model release

Alibaba Releases Qwen3.8 Max, a Multimodal Reasoning Model with 1M Token Context

Alibaba has moved Qwen3.8 Max out of preview into general availability, positioning it as the flagship of the Qwen3.8 series with a 1 million token context window and multimodal input support. The model is priced at $2.00 per million input tokens and $6.00 per million output tokens via OpenRouter.

July 31, 2026
model releaseThinking Machines

Thinking Machines Releases Inkling Small, a 12B-Active-Parameter Model That Beats Its Larger Predecessor on Key Benchmar

Thinking Machines has released Inkling Small, an open-weights reasoning model with 276 billion total parameters but only 12 billion active. According to Artificial Analysis, it scores nearly as high as the company's larger Inkling model while using roughly a third of the parameters and far fewer output tokens per task.

July 30, 2026
model release

AMD Releases Instella-MoE-16B-A3B-Think, a Fully Open Mixture-of-Experts Model Trained Entirely on AMD GPUs

AMD has released Instella-MoE-16B-A3B-Think, a 16-billion-parameter Mixture-of-Experts language model trained entirely from scratch on AMD Instinct MI300X and MI325X GPUs. The release includes every checkpoint from pre-training through reinforcement learning, along with full training recipes, under a research-only license.

June 12, 2026
model releaseMoonshot AI

Moonshot AI releases Kimi K2.7 Code with 1T parameters, 256K context window, 30% lower thinking token usage

Moonshot AI has released Kimi K2.7 Code, a 1 trillion parameter Mixture-of-Experts model designed for long-horizon coding tasks. The model features a 256K context window and reduces thinking token usage by approximately 30% compared to its predecessor K2.6.