AI being cheaper should let us roam more agent clankers to help us with tasks and this is beautiful to see. To note MiniMax models are smaller and have about smaller context window, yet it’s really putting up some good numbers. MiniMax might just be one of the best value alternatives for coding intelligence. Matching GPT 5.4 on design arena with both their M2.5 & M2.7 models. M2.7 is also the first model that deeply participated in its own self evolution. This is the first model that helped build itself with self evolution with its own optimization loops and RL training. M2.7 vs Leading Models Strong Coding:
SWE Bench Pro: 56.2%, Beats Gemini 3.1 Pro (54.2%); on par with Claude Sonnet 4.6 (57.2%), Opus 4.6 (57.3%), GPT 5.4 (57.7%) Multi-SWE Bench: 52.7% (leading) Production: VIBE-Pro: 55.6%; Nearly ties Sonnet 4.6 (56.1%) and Opus 4.6 (55.6%) Strong Agentic Capabilities: MM-ClawBench (agent/tool use): 62.7%; Competitive with Sonnet 4.6 (64.2%) and Opus 4.6 (75.4%) Also seen significant improvements in ML MiniMax M2.7 is near Claude Opus 4.6 level performance and 20x more cost efficient in output. M2.7 vs Opus 4.6: Input: $0.3/M vs $5/M (16.7x cost difference) Output: $1.2/M vs $25/M (20.8x cost difference) Main distinction between them is Opus has nearly 5x the context window. Which one would you use? Sources for this post are from DesignArena, MiniMax & Commonstack submitted by /u/hexxthegon
Originally posted by u/hexxthegon on r/ArtificialInteligence