| Minimax M2.7, asking it to make a 3D GTA-like experience. GLM 5 still wins on aesthetics and adding detail without being asked, but when I asked Minimax to add trees and birds (with boids algo), it did a decent job! This was not even in an agentic scaffold, I usually just do initial testing like this in the openwebui artifacts window, but Minimax has also been kicking ass for me in OpenCode. I'm running it at IQ2_XXS for max speed, and it still is coherent and capable. Prompt 1: Prompt 2 The remaining prompts were mostly just getting it to reverse control directions. LLMs do not have an intuitive sense of direction :p [link] [comments] |
Local Minimax M2.7, GTA benchmark
Reddit r/LocalLLaMA / 4/13/2026
💬 OpinionSignals & Early TrendsTools & Practical UsageModels & Research
Key Points
- Minimax M2.7 is reported to produce a reasonably functional 3D GTA-like experience in a single web page, including player movement and enter/leave/drive car interactions.
- The user notes that GLM 5 can outperform on visual polish and adding detail, but Minimax performs well when tasks require additional environment elements.
- When prompted to add trees and bird flocks using a boids-style algorithm, Minimax reportedly delivered a decent result, indicating useful capability for structured 3D/game-scene generation.
- The benchmark was done without an “agentic scaffold” using OpenWebUI artifacts for quick testing, and the model is also described as performing strongly in OpenCode.
- Running Minimax M2.7 at an IQ2_XXS setting for maximum speed still yielded coherent, capable outputs, though the user highlights typical issues like reversed direction control during prompt-driven iteration.
Related Articles

Black Hat USA
AI Business

Black Hat Asia
AI Business

Agentic coding at enterprise scale demands spec-driven development
VentureBeat

How to build effective reward functions with AWS Lambda for Amazon Nova model customization
Amazon AWS AI Blog

How 25 Students Went from Idea to Deployed App in 2 Hours with Google Antigravity
Dev.to