Descriptions:
Fahd Mirza puts Inclusion AI’s latest flagship model, Ling-2.6-1T, through a practical hands-on review. The model is a one-trillion-parameter open-source release targeting complex real-world workloads — coding, multi-step reasoning, and agentic tasks — with a claimed ‘fast-thinking mechanism’ that reportedly uses roughly a quarter of the tokens comparable models need to reach the same answer. It supports up to 262K tokens of context and is currently available at no cost via OpenRouter’s free tier.
The primary test involves prompting Ling to build a working GPU memory leak detector — a live CLI application for Ubuntu that polls VRAM consumption and identifies both sudden drops (process kill leaks) and gradual growth leaks. Mirza runs the generated code against a real GPU on Vast Compute with Ollama loaded, testing both scenarios. The model produces well-formatted, commented Python code; the detector successfully identifies memory release events and flags growth patterns, though alert messaging for the growth leak case needed some refinement.
Additional tests cover multilingual output across dozens of languages — Mirza flags uneven quality and asks viewers to validate their native languages in the comments — and token-per-second benchmarking. He is upfront about treating Inclusion AI’s own benchmark claims skeptically, preferring direct testing. The overall verdict: Ling-2.6-1T performs respectably for coding tasks, particularly given free-tier access, and is worth evaluating for developers exploring capable open-weight alternatives.
📺 Source: Fahd Mirza · Published May 04, 2026
🏷️ Format: Review
