OPEN LINK ↗
// 46d agoMODEL RELEASE
MiniMax drops M2.7 open weights with self-improving MoE
The highly anticipated open weights for MiniMax M2.7 have been released, delivering a 230-billion parameter Sparse MoE model with a 200k context window. Built with a recursive self-improvement training loop, the model is heavily optimized for complex agentic workflows and local inference.
// ANALYSIS
MiniMax M2.7 is a massive win for the local LLM community, proving that top-tier agentic performance isn't locked behind closed APIs.
- –The MoE architecture activates only 10B parameters per token, making this massive 230B model surprisingly viable for high-end local setups and cost-effective deployment
- –Its recursive self-improvement approach to synthetic data generation has yielded impressive results, scoring 56.22% on SWE-Pro
- –Out-of-the-box support for vLLM and SGLang ensures developers can immediately integrate it into multi-agent pipelines
// TAGS
minimax-m2-7llmopen-weightsagentreasoninginference
DISCOVERED
46d ago
2026-04-12
PUBLISHED
46d ago
2026-04-12
RELEVANCE
9/ 10
AUTHOR
samthepotatoeman
