MiniMax (official)

I gave the same Go MMRPG backend spec to two coding agents and then asked Claude to judge the performance. CC (with Sonnet 4.6) 4.6 shipped 1,409 lines of clean, readable code. CyOps with @MiniMax_AI M2.7 shipped 5,271 lines of denser, layered code.

We have also partnered with @MiniMax_AI to provide *free access to agents with MiniMax-M2.7* for a limited time! This is a great opportunity to test out how to mix in cost-effective models into your coding workflows. Try it out here: app.all-hands.dev

Recently, we took time to consolidate all of the work behind M2 and published it here: our M2 paper on arXiv It’s been just over six months since we first open-sourced M2 on December 23 last year. During that time, a number of our ideas and systems have been broadly adopted by the open-source community — including CISPO, Forge RL System, Self-Evolution. Over the past six months, we’ve felt incredible enthusiasm from the open-source community. Nearly every model release reached the #1 spot on the Hugging Face leaderboard. Now it’s time for a new chapter. We’re getting ready for M3. MSA paper is on the road. arxiv.org/abs/2605.26494

#MSA #OpenSource #M3 🫣😎

MiniMax Agent now uses @perplexity_ai Search. We benchmarked 3 AI-native search providers across 700+ agent tasks. Perplexity delivered the best combination of answer quality and snippet density: more useful evidence per token, less irrelevant context, and lower end-to-end cost. Compared with Serper, our previous default: ⚡Tool calls per task: 17.8 vs 32.6 (45% reduction) 💰Token usage: 94.6M vs. 162.3M (42% reduction) 🔍Pass rate: +2% increase Total cost: 27% decrease in total cost savings In agent workflows, search is not a one-shot lookup. It is a loop. Better snippets mean better grounding. Better grounding means fewer searches, less context, better answers, and lower cost. One good search can save 14 bad ones. Now shipping in MiniMax Agent. agent.minimax.io

Great being in China this week with customers, partners, developers, and our @AMD teams across Beijing, Shanghai, and Suzhou. Loved the energy at AMD DevDay in Shanghai showcasing the progress we’re making to bring AI Everywhere. Thanks to everyone who joined us.