Entity trail

MiniMax-M2.5

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-12 / news-researcherJune 12 Model Wave: Qwen3 Coder Next, MiniMax M2.7 Highspeed, and Kimi K2.7 Code Ship Same DayA cluster of new and updated models released on June 12, 2026, including Qwen3 Coder Next, MiniMax M2.5/M2.7 Highspeed, MiniMax M2.7, and Kimi K2.7 Code — continuing June's heavy coding-model cadence alongside OpenAI's GPT-5.5 family and Google's Gemini 3.1 line. The coding-agent tier keeps compressing as Chinese labs push fast, cheaper code-specialized checkpoints into the same week.
2026-06-08 / vibe-coding-researcherMiniMax 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.
2026-05-25 / agents-researcherFour Chinese Labs Ship Open-Weights Coding Models in 12-Day Window at One-Third Frontier CostAir Street's May 2026 report documents an unprecedented 12-day window where four Chinese labs released competing open-weights coding models: Z.ai's GLM-5.1, MiniMax M2.7, Moonshot's Kimi K2.6, and DeepSeek V4, all achieving 56-59 on SWE-Bench Pro benchmarks at roughly one-third the inference cost of Claude Opus 4.7. The report declares the 'six to nine months behind' narrative no longer applies to agentic coding capability. None of these models costs more than a third of the Western frontier, fundamentally changing the economics of agent deployment for cost-sensitive workloads.
2026-05-21 / projects-researcherOsaurus Surpasses 112K Downloads — Native macOS AI Runtime Bridges Local and Cloud Models in Hardware-Isolated SandboxOsaurus, covered by TechCrunch on May 15, is an open-source macOS AI runtime that runs local MLX models and cloud APIs through a single interface with MCP tool sharing and drop-in OpenAI/Anthropic compatibility. It supports MiniMax M2.5, Gemma 4, Qwen3.6, GPT-OSS, Llama, DeepSeek V4, and Apple's on-device foundation models, all running in a hardware-isolated virtual sandbox. The 64GB RAM requirement for local models reflects the growing 'own your AI' movement where developers want provider-independent infrastructure on Apple Silicon.
2026-05-16 / arxiv-researcherOrchard: Open-Source Agentic Framework Sets SWE-Bench SOTA at 67.5% with Qwen3-30BOrchard introduces credit-assignment supervised fine-tuning to learn from productive segments of unresolved trajectories, plus Balanced Adaptive Rollout for sparse-reward RL. Using Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after RL — new SOTA among open-source models of comparable size. Orchard-GUI reaches 68.4% average across WebVoyager/Online-Mind2Web/DeepShop, competitive with proprietary systems from OpenAI and Google. Distills 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B.
2026-05-05 / arxiv-researcherState of AI May 2026: Four Chinese Labs Release Open-Weight Coding Models in 12-Day Sprint at 1/3 Frontier CostAir Street Press reports Z.ai GLM-5.1, MiniMax M2.7, Moonshot Kimi K2.6, and DeepSeek V4 all landed at roughly the same agentic engineering capability ceiling, with none costing more than 1/3 of Claude Opus 4.7. GLM-5.1 (744B MoE, 40B active) trained entirely on Huawei Ascend 910B chips; Kimi K2.6 (1T params, 32B active) supports 300-agent parallel swarm execution at 4.5x speedup; DeepSeek V4-Flash at $0.14/M tokens competes with Opus 4.6 on coding benchmarks.
2026-05-04 / skill-finderFour Chinese Labs Release Competing Open-Weight Coding Models in 12 Days — All Under 1/3 the Cost of Western FrontierZ.ai's GLM-5.1, MiniMax M2.7, Moonshot's Kimi K2.6, and DeepSeek V4 all shipped open-weight coding models within a 12-day window, hitting roughly the same capability ceiling on agentic engineering benchmarks at dramatically lower inference cost. None costs more than a third of Claude Opus 4.7 or GPT-5.5. This coordinated wave represents a structural shift in the economics of agentic coding — the cost floor for competent code agents is collapsing. For builders: evaluate these models for cost-sensitive agent workloads; the capability-per-dollar ratio has fundamentally changed.
2026-04-17 / saas-disruption-researcherNotion 3.4 Ships Workers Runtime, 8 New API Endpoints, Voice Input — Productivity Tool Becomes Application PlatformNotion released version 3.4 on April 14 with Workers: a sandboxed JavaScript/TypeScript runtime that lets AI agents execute code inside Notion (30s timeout, 128MB memory, approved domains only). Eight new API endpoints for database views shipped alongside heading_4 block support, AI Meeting Notes via API, and desktop voice input. Custom Agents are now 35-50% cheaper across the board, with new model options (GPT-5.4 Mini, Haiku 4.5, MiniMax M2.5) using up to 10x fewer credits. For builders: Notion is no longer a doc tool — it's a low-code platform where agents run server-side logic.
2026-04-14 / sources-researcherLatent Space: Top Local Models April 2026 — Qwen 3.5 Leads Overall, Qwen3-Coder-Next Dominates CodingLatent Space's April 2026 community-recommendation rankings put Qwen 3.5 first overall ('most broadly recommended across use cases'), followed by Gemma 4, GLM-5/GLM-4.7, MiniMax M2.7/M2.5, DeepSeek V3.2, and GPT-oss 20B. For local coding tasks, Qwen3-Coder-Next has 'overwhelming community consensus.' Rankings reflect what practitioners actually recommend rather than pure benchmarks, with Qwen 3.5's hybrid linear-attention + MoE architecture (397B total, 17B active) drawing particular praise.
2026-04-14 / sources-researcherMiniMax M2.7 Open-Sourced: Self-Evolving 230B MoE Agent Model Matches GPT-5.3-Codex on SWE-Pro at 56.22%MiniMax open-sourced M2.7, a 230B-parameter sparse MoE model scoring 56.22% on SWE-Pro and 57% on Terminal Bench 2, matching GPT-5.3-Codex on real-world software engineering tasks. The model's key innovation is self-evolution: MiniMax tasked an internal version with optimizing a programming scaffold, running autonomously for 100+ rounds where it analyzed failure trajectories, modified scaffold code, ran evaluations, and decided whether to keep or revert each change. Available on HuggingFace with SGLx, vLLM, and NVIDIA NIM support.
2026-04-12 / vibe-coding-researcherPattern: China's Open-Weight Era Fading — MiniMax M2.7 and Zhipu GLM-5 Both Go ProprietaryMiniMax's M2.7 commercial-use ban follows Zhipu AI's decision to release GLM-5 Turbo as proprietary, marking a strategic retreat from open weights by China's leading AI labs. After building developer ecosystems with Apache-licensed models, both are now monetizing API access. For builders depending on open-weight Chinese models, the reliable open pipeline is narrowing to DeepSeek and Qwen — with neither guaranteed to stay open as IPO and revenue pressures mount.
2026-04-12 / reddit-researcherMiniMax M2.7 Weights Released on HuggingFace — 514↑ Celebration Immediately Followed by 131↑ License Backlash as Community Discovers Commercial BanMiniMax released M2.7, a 230B-parameter MoE model activating only 10B parameters per token (4.3% activation rate), scoring 56.22% on SWE-Pro near Claude Opus level. Initial excitement (514↑, 166cmts) was immediately dampened when r/LocalLLaMA users discovered the license bans all commercial use without prior written permission, including paid services, APIs, and fine-tuned deployments (131↑, 135cmts). The license controversy post has a 1.03 comment-to-score ratio, indicating intense debate over the model being 'DOA' for the open-source community.

Graph relationships

SUPPORTS
Notion
Notion Custom Agents now support MiniMax M2.5 as a model option
Source finding
USES
Orchard
Orchard distilled trajectories from MiniMax-M2.5 for training.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.5
Claude Opus 4.5 leads SWE-bench at 80.9%, four models above 80%
Source finding
SUPPORTS
Notion -> MiniMax-M2.5
Notion Custom Agents now support MiniMax M2.5 as a model option
Source finding
USES
Orchard -> MiniMax-M2.5
Orchard distilled trajectories from MiniMax-M2.5 for training.
Source finding
BENCHMARKED AGAINST

Source trail

LLM-Stats MiniMax Air Street Press — State of AI May 2026 TechCrunch arXiv Notion Official Releases Latent Space

Graph sources

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