MiniMax
Public MindPattern findings, entities, and graph evidence that cite this source.
Findings
7
All-time hits
7
High value
5
Last seen
2026-06-08
Connected entities
MiniMaxMiniMax M3 Released: 1M-Context Open-Weight Coding Model Beats GPT-5.5 on SWE-Bench Pror/LocalLLaMA / MiniMaxMiniMax M2.7 Will Be Open Weights — Self-Evolving Model Handled 30-50% of Its Own RL TrainingMiniMax M2.7: First Self-Evolving Model — SWE-Pro 56.22%, GDPval ELO 1495 (Highest Open-Source)MiniMax M2.7 Launches — Self-Evolving Proprietary Model Handled 30-50% of Its Own RL Training WorkflMiniMax M2.7 Ships: Self-Evolving Model Ran 30–50% of Its Own RL Training Workflow
Related findings
- 2026-06-08 / TOOLSMiniMax M3 Released: 1M-Context Open-Weight Coding Model Beats GPT-5.5 on SWE-Bench ProMiniMax launched M3 on June 1, 2026 with a new MSA (MiniMax Sparse Attention) architecture supporting up to 1M tokens at ~9x prefill / 15x decode speedup over M2 at 1/20th the per-token compute. It scores 59.0% on SWE-Bench Pro (surpassing GPT-5.5 and Gemini 3.1 Pro), 83.5 on BrowseComp (vs Opus 4.7's 79.3), 66.0% Terminal Bench 2.1, and 74.2% MCP Atlas. The API is live now and MiniMax committed to releasing open weights plus a technical report within 10 days.
- 2026-04-14 / DISPATCHMiniMax Music 2.6: Open-Sources Three Music Skills for AI Agent EcosystemMiniMax launched Music 2.6 and open-sourced three Music Skills for the AI agent ecosystem: minimax-music-gen (one-line prompt to full track with auto-selection of original, instrumental, or cover), buddy-sings (AI persona sings in first person), and minimax-music-playlist (agent scans local music, learns taste, builds playlists). Music 2.6 introduces a Cover feature that extracts melodic skeletons and lets users swap style, arrangement, and lyrics independently. The model supports 100+ instruments with improved sub-bass depth, and initial generation latency is under 20 seconds.
- 2026-04-07 / REDDITMiniMax M2.7: First 'Self-Evolving' Model Runs 100+ Autonomy Cycles, Scores 56.2% on SWE-Pro — 317 UpvotesMiniMax M2.7, which launched March 18, is generating renewed community interest (317↑, 30cmts on r/LocalLLaMA) as practitioners discover its self-evolution capability: the model autonomously ran 100+ rounds of scaffold optimization during training, achieving 30% performance improvement without human intervention. It scores 56.22% on SWE-Pro (near Opus-level), maintains 97% skill adherence across 40+ complex skills, and costs just $0.30/$1.20 per million input/output tokens — an order of magnitude cheaper than frontier models.
- 2026-03-22 / REDDITMiniMax M2.7 Will Be Open Weights — Self-Evolving Model Handled 30-50% of Its Own RL TrainingMiniMax announced that M2.7 will be released as open weights, a significant reversal from the proprietary API-only stance at launch. The model is architecturally novel: it autonomously triggered log-reading, debugging, and metric analysis to handle 30-50% of its own reinforcement learning development workflow. It scored 56.22% on SWE-Pro at $0.30/$1.20 per million input/output tokens — roughly one-third the cost of GLM-5 — making it a strong candidate for enterprise coding workloads once weights drop.
- 2026-03-19 / TOOLSMiniMax M2.7: First Self-Evolving Model — SWE-Pro 56.22%, GDPval ELO 1495 (Highest Open-Source)MiniMax M2.7 claims to be 'our first model deeply participating in its own evolution,' autonomously optimizing its own performance scaffolds and achieving a 66.6% medal rate across 22 ML competitions. Benchmarks: SWE-Pro 56.22% (matching GPT-5.3-Codex), VIBE-Pro 55.6% (near Opus 4.6 parity), GDPval-AA ELO 1495 (highest among open-source models), 97% skill adherence rate on 40+ complex skills (2,000+ tokens each). Production incident recovery times reduced to under 3 minutes in real-world engineering scenarios.
- 2026-03-18 / REDDITMiniMax M2.7 Launches — Self-Evolving Proprietary Model Handled 30-50% of Its Own RL Training WorkflowMiniMax released M2.7 on March 18, a proprietary (not open-source) model that participated in its own development, autonomously handling 30-50% of its reinforcement learning research pipeline. Benchmark highlights: SWE-Pro 56.22% matching GPT-5.3-Codex, GDPval-AA ELO 1495 highest among comparable models, MM Claw 62.7% approaching Sonnet 4.6, available on OpenRouter at $0.30/$1.20 per million tokens. Unlike M2/M2.1/M2.5 which were MIT-licensed open-weight releases, M2.7 is closed-weight despite shipping via the same community channels.
- 2026-03-18 / TOOLSMiniMax M2.7 Ships: Self-Evolving Model Ran 30–50% of Its Own RL Training WorkflowMiniMax released M2.7, a model that autonomously participated in 30–50% of its own reinforcement learning development — building skills, reading logs, debugging, and analyzing metrics without human direction. On SWE-Pro it scores 56.22% and achieves 97% skill adherence across 40+ complex skills each exceeding 2,000 tokens. Available on OpenRouter at $0.30/$1.20 per million tokens with 204K context, making it a cost-competitive option for agent pipelines.