MiniMaxMiniMax·💬 Text Generation

MiniMax M2.7

ReasoningCodeFunction CallingWeb Searchprivate
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Quick reference
MiniMax M2.7 — TLDR
  • 🧠 Agentic LLM built for long-horizon software engineering and productivity workflows
  • 🆕 First MiniMax model that participates in its own evolution
  • 📏 Roughly 198K-token context for extended reasoning and tool use
  • 🔧 Function calling, multi-agent "Agent Teams," dynamic tool search
  • 🎯 Vendor-reported 56.22% on SWE-Pro, 57.0% on Terminal Bench 2
  • 🏢 Built by MiniMax for autonomous productivity and agentic workflows
  • 📚 Released March 2026 under a non-commercial MiniMax license
💰 Pricing
$0.375 / $1.50
per 1M · input / output
📏 Context
198K tokens
📅 On Venice since
Mar 18, 2026
123 days ago
Provider

MiniMax is an AI company building generative models across multiple modalities, with a focus that spans both language understanding and audio creation. Their rapid release cadence in early 2026—delivering several new models within just a few months—reflects…

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7 models on Venice
3 text · 3 music · 1 tts
Since Feb 12, 2026

About this model

MiniMax M2.7 is a text model from MiniMax aimed at autonomous, real-world productivity: complex software engineering, agentic tool use, and office document workflows. Its headline feature is "self-evolution" — MiniMax says an internal version of the model autonomously optimized a programming scaffold over 100+ rounds, analyzing failure trajectories, modifying code, running evaluations, and deciding whether to keep or revert changes, for a reported 30% improvement on internal benchmarks. It also introduces native Agent Teams for multi-agent collaboration with stable role identity and autonomous decision-making.

Within the same family, M2.7 follows MiniMax M2.5 and precedes MiniMax M3 and its M3 Preview. MiniMax reports significant gains over the previous generation in professional finance tasks — for instance, autonomously reading annual reports and earnings calls, designing assumptions, and building revenue models like a junior analyst.

On vendor-reported evaluations, M2.7 scores 56.22% on SWE-Pro, 76.5 on SWE Multilingual, 55.6% on VIBE-Pro, and 57.0% on Terminal Bench 2, alongside a reported 1495 ELO on GDPval-AA. These figures are self-reported by MiniMax rather than independent evaluators.

The model supports reasoning, code-optimized generation, function calling, and web search, with a context window near 198K tokens. MiniMax describes system-level uses such as correlating monitoring metrics, trace analysis, and SRE-style debugging, citing live incident recovery reduced to under three minutes on multiple occasions. It is distributed under a non-commercial MiniMax license.

This About section is AI-generated from public sources (Claude Opus 4.8), with no human editing. It may contain inaccuracies — verify critical details against the sources listed above.

Data sources: Venice API · HuggingFace · Wikipedia — enrichment updated 4d ago